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Academic Motivation-Achievement Cycle and the Behavioural Pathways: A Short-Timeframe Experiment with Manipulated Perceived Achievement

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Title: Academic Motivation-Achievement Cycle and the Behavioural Pathways: A Short-Timeframe Experiment with Manipulated Perceived Achievement
Language: English
Authors: TuongVan Vu (ORCID 0000-0001-6700-2439), Martijn Meeter (ORCID 0000-0002-5112-6717), Abe Hofman (ORCID 0000-0003-4269-5296), Brenda Jansen (ORCID 0000-0001-9262-933X), Lucía Magis-Weinberg (ORCID 0000-0002-6036-2850), Elise van Triest (ORCID 0000-0002-8770-0717), Nienke van Atteveldt (ORCID 0000-0002-3387-6151)
Source: British Journal of Educational Psychology. 2025 95(2):683-722.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 40
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Learning Motivation, Academic Achievement, Correlation, Undergraduate Students, Foreign Countries, Second Language Learning, English (Second Language), Achievement Gains, Vocabulary Development
Geographic Terms: Netherlands
DOI: 10.1111/bjep.12731
ISSN: 0007-0998
2044-8279
Abstract: Background: The purported reciprocity between motivation and academic achievement in education has largely been supported by correlational data. Aims: Our first aim was to determine experimentally whether motivation and achievement are reciprocally related. The second objective was to investigate a potential behavioural mediation pathway between motivation and achievement by measuring the objective effort expended on learning. Finally, we studied the causality of these relations by analysing the dynamics between motivation and achievement (rather than examining them as individual constructs) when perceived achievement was experimentally manipulated. Sample(s): The study employed a short-timeframe experiment in which 309 Dutch undergraduate students (M[subscript age] = 19.89, SD = 2.08) learned new English vocabulary. Methods: Their motivation, effort, and achievement were measured at multiple time points within one hour. Midway through the experiment, participants received manipulated feedback indicating an achievement decline, which was expected to influence their subsequent motivation, effort, and actual achievement. A random-intercept cross-lagged panel framework was employed to model how one construct influenced another over time. Results: We found a unilateral effect of achievement on motivation (i.e., no reciprocity), which remained stable across the time points. Our experimental manipulation partially supported a causal interpretation of the unilateral achievement[right arrow]motivation pathway. Additionally, no mediation effect of effort was identified: motivation was not associated with effort, nor was effort linked to achievement. Conclusions: Our findings underscore the importance of further exploration of behavioural mediation pathways, a broad operationalization of motivation, and the application of appropriate modelling strategies to investigate the motivation-achievement reciprocity.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1470953
Database: ERIC
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  Value: <anid>AN0185103687;6kx01jun.25;2025May14.03:55;v2.2.500</anid> <title id="AN0185103687-1">Academic motivation–achievement cycle and the behavioural pathways: A short‐timeframe experiment with manipulated perceived achievement </title> <p>Background: The purported reciprocity between motivation and academic achievement in education has largely been supported by correlational data. Aims: Our first aim was to determine experimentally whether motivation and achievement are reciprocally related. The second objective was to investigate a potential behavioural mediation pathway between motivation and achievement by measuring the objective effort expended on learning. Finally, we studied the causality of these relations by analysing the dynamics between motivation and achievement (rather than examining them as individual constructs) when perceived achievement was experimentally manipulated. Sample(s): The study employed a short‐timeframe experiment in which 309 Dutch undergraduate students (Mage = 19.89, SD = 2.08) learned new English vocabulary. Methods: Their motivation, effort, and achievement were measured at multiple time points within one hour. Midway through the experiment, participants received manipulated feedback indicating an achievement decline, which was expected to influence their subsequent motivation, effort, and actual achievement. A random‐intercept cross‐lagged panel framework was employed to model how one construct influenced another over time. Results: We found a unilateral effect of achievement on motivation (i.e., no reciprocity), which remained stable across the time points. Our experimental manipulation partially supported a causal interpretation of the unilateral achievement→motivation pathway. Additionally, no mediation effect of effort was identified: motivation was not associated with effort, nor was effort linked to achievement. Conclusions: Our findings underscore the importance of further exploration of behavioural mediation pathways, a broad operationalization of motivation, and the application of appropriate modelling strategies to investigate the motivation‐achievement reciprocity.</p> <p>Keywords: academic achievement; effort; motivation; random‐intercept cross‐lagged panel model; reciprocal effect</p> <hd id="AN0185103687-2">STAGE 1 REPORT</hd> <p></p> <hd id="AN0185103687-3">Introduction</hd> <p>Many theoretical and empirical studies have concluded that there are close relations between motivation and academic achievement (Eccles & Wigfield, [<reflink idref="bib21" id="ref1">21</reflink>]; Gottfried et al., [<reflink idref="bib28" id="ref2">28</reflink>]; Valentine et al., [<reflink idref="bib83" id="ref3">83</reflink>]; Winne & Nesbit, [<reflink idref="bib91" id="ref4">91</reflink>]), with a sizeable body of literature suggesting that these relations are reciprocal or cyclical (Huang, [<reflink idref="bib35" id="ref5">35</reflink>]; Marsh & Martin, [<reflink idref="bib46" id="ref6">46</reflink>]; Möller et al., [<reflink idref="bib53" id="ref7">53</reflink>]; Niepel et al., [<reflink idref="bib56" id="ref8">56</reflink>], [<reflink idref="bib57" id="ref9">57</reflink>]; Sewasew & Koester, [<reflink idref="bib73" id="ref10">73</reflink>]; Sewasew & Schroeders, [<reflink idref="bib74" id="ref11">74</reflink>]). Various motivation theories (Eccles & Wigfield, [<reflink idref="bib21" id="ref12">21</reflink>]) generate conceptualizations of motivation that encompass beliefs and behaviours. However, motivation in many studies is measured with self‐reports, neglecting the behavioural pathway leading from motivation to achievement. Certain motivation theories suggest that effort investment is the most plausible pathway (Cleary & Zimmerman, [<reflink idref="bib11" id="ref13">11</reflink>]; Feldon et al., [<reflink idref="bib25" id="ref14">25</reflink>]; Keller, [<reflink idref="bib37" id="ref15">37</reflink>]; Kuhl, [<reflink idref="bib39" id="ref16">39</reflink>]), yet this is a hypothesis to be tested. Moreover, the causality of this reciprocal relationship between motivation and achievement is still an open question. Few studies have taken an experimental approach in which reciprocal effects through time are investigated and a construct is manipulated. The present research is a 1 h experiment to study the cyclical relationship between motivation and academic achievement in Dutch undergraduate students. We measure motivation and achievement at eight time points, with an emphasis on measuring effort expenditure objectively. The experiment also tackled the question of causality where we included an experimental manipulation on perceived achievement and tested if the reciprocity is still preserved.</p> <hd id="AN0185103687-4">Reciprocal relationship between motivation and achievement</hd> <p>There is theoretical and empirical support in the form of correlational data for the strong relationship between students' motivation and their academic achievements (Burnette et al., [<reflink idref="bib8" id="ref17">8</reflink>]; Eccles & Wigfield, [<reflink idref="bib21" id="ref18">21</reflink>]; Gottfried et al., [<reflink idref="bib28" id="ref19">28</reflink>]; Guay et al., [<reflink idref="bib29" id="ref20">29</reflink>]; Huang, [<reflink idref="bib35" id="ref21">35</reflink>]; Marsh & Craven, [<reflink idref="bib44" id="ref22">44</reflink>]; Marsh & Martin, [<reflink idref="bib46" id="ref23">46</reflink>]; Robbins et al., [<reflink idref="bib66" id="ref24">66</reflink>]; Seaton et al., [<reflink idref="bib72" id="ref25">72</reflink>]). To study motivation, many related constructs such as academic self‐concepts (ASCs), achievement goals, self‐efficacy and so forth have been used (Eccles & Wigfield, [<reflink idref="bib21" id="ref26">21</reflink>]). Much empirical research in the reciprocal motivation–achievement relationship operationalizes motivation as <emph>ASC</emph>, which is students' perception of their ability in a certain academic domain, most often in mathematics and verbal subjects (Gottfried et al., [<reflink idref="bib28" id="ref27">28</reflink>]; Guay et al., [<reflink idref="bib29" id="ref28">29</reflink>]; Seaton et al., [<reflink idref="bib72" id="ref29">72</reflink>]; for meta‐analyses and reviews, see Burnette et al., [<reflink idref="bib8" id="ref30">8</reflink>]; Eccles & Wigfield, [<reflink idref="bib21" id="ref31">21</reflink>]; Marsh & Craven, [<reflink idref="bib44" id="ref32">44</reflink>]; Marsh & Martin, [<reflink idref="bib46" id="ref33">46</reflink>]; Robbins et al., [<reflink idref="bib66" id="ref34">66</reflink>]). Motivation such as these constructs are related to achievement because having a positive self‐concept is theorized to lead to being engaged in self‐affirmative and self‐regulatory behaviours such as exerting effort, demonstrating more persistence, selecting adaptive goals and making behavioural choices that are congruent with academic goals (Valentine et al., [<reflink idref="bib83" id="ref35">83</reflink>]).</p> <p>Previous findings indicated that, in educational contexts, motivation not only affects academic achievement but that the relation is bidirectional in nature where achievement also boosts motivation (Huang, [<reflink idref="bib35" id="ref36">35</reflink>]; Marsh & Craven, [<reflink idref="bib44" id="ref37">44</reflink>]; Marsh & Martin, [<reflink idref="bib46" id="ref38">46</reflink>]; Möller et al., [<reflink idref="bib53" id="ref39">53</reflink>]). A common view is that both the 'motivation → achievement' or 'achievement → motivation' links exist because motivation and achievement influence each other in a reciprocal manner over time (Arens et al., [<reflink idref="bib2" id="ref40">2</reflink>]; Guay et al., [<reflink idref="bib29" id="ref41">29</reflink>]; Huang, [<reflink idref="bib35" id="ref42">35</reflink>]; Marsh et al., [<reflink idref="bib48" id="ref43">48</reflink>]; Marsh & Craven, [<reflink idref="bib44" id="ref44">44</reflink>]; Marsh & Martin, [<reflink idref="bib46" id="ref45">46</reflink>]; Möller et al., [<reflink idref="bib53" id="ref46">53</reflink>]; Murayama et al., [<reflink idref="bib54" id="ref47">54</reflink>]; Niepel et al., [<reflink idref="bib56" id="ref48">56</reflink>], [<reflink idref="bib57" id="ref49">57</reflink>]; Seaton et al., [<reflink idref="bib72" id="ref50">72</reflink>]). Yet, empirical research has not reached a full consensus. Recently, there have been some doubts about the robustness of the reciprocal relationship between motivation and achievement when testing under different statistical models. Specifically, methodologists pointed out a flaw in the widely used CLPM in earlier research: the conflation of between‐ and within‐person variations (Hamaker et al., [<reflink idref="bib31" id="ref51">31</reflink>]). Newer studies contrasting the results from CLPM and RI‐CLPM–the latter can distinguish these two sorts of variations–suggested that when fitting RI‐CLPM to the same data, the reciprocal effects were either not found (Ehm et al., [<reflink idref="bib22" id="ref52">22</reflink>]) or only the achievement → motivation (ASC) link was apparent (Burns et al., [<reflink idref="bib9" id="ref53">9</reflink>]).</p> <hd id="AN0185103687-5">Causality, timeframe and behavioural consequences</hd> <p>To our knowledge, several aspects of the relations between motivation and achievement are still to be further investigated. First, even when extant research has demonstrated longitudinal relations between the constructs, a <emph>causal interpretation</emph> remains difficult because of the shortage of studies that have both a longitudinal design <emph>and</emph> an experimental manipulation of either motivation or achievement independently (Marsh et al., [<reflink idref="bib49" id="ref54">49</reflink>]; Mega et al., [<reflink idref="bib52" id="ref55">52</reflink>]). In other words, experimentally manipulating one of the components <emph>within the cyclical loop</emph> between motivation and achievement has been rarely done in previous research. As a consequence, direct evidence about causality is largely lacking. Unsurprisingly, in almost every longitudinal study investigating the reciprocal relations between motivation and achievement, the discussion section mentions the need for experimental designs that can tackle this issue (Marsh et al., [<reflink idref="bib48" id="ref56">48</reflink>], [<reflink idref="bib49" id="ref57">49</reflink>]; Mega et al., [<reflink idref="bib52" id="ref58">52</reflink>]; Pinxten et al., [<reflink idref="bib63" id="ref59">63</reflink>]).</p> <p>Second, much research on the reciprocity between motivation and achievement was conducted at large time intervals, which reflect changes that happen over a long period of time such as a school year or semester (e.g., (Harackiewicz et al., [<reflink idref="bib32" id="ref60">32</reflink>]; Marsh et al., [<reflink idref="bib50" id="ref61">50</reflink>]; Nuutila et al., [<reflink idref="bib59" id="ref62">59</reflink>])). However, theories of motivation are often framed in terms that suggest that motivation constructs fluctuate in wide‐ranging timeframes. For instance, within expectancy–value theory and self‐efficacy theory, ASC and self‐efficacy are expected and found to shift indeed over a school semester (Eccles & Wigfield, [<reflink idref="bib21" id="ref63">21</reflink>]). Yet, according to self‐determination theory (Ryan & Connell, [<reflink idref="bib69" id="ref64">69</reflink>], p. 750), intrinsic motivation is 'reasons for action where the behaviour is done simply for its inherent enjoyment', which seems to suggest that this motivation construct operates on a much shorter timeframe than self‐concepts. An emerging body of research in achievement emotions also suggests that motivation can be fast changing (Pekrun, [<reflink idref="bib62" id="ref65">62</reflink>]). A related issue is that data collection at large intervals might not be able to capture the ebbs and flows of the relations between constructs over time. Taken together, future studies with special attention to motivation constructs of short timeframes, and in an intensive manner, therefore, should be highly valuable for both theories and applications.</p> <p>Third, previous reciprocity studies have also left an unanswered question concerning the behavioural mediation pathway from motivation to achievement. Although motivation theories in educational psychology generate many ways of conceptualizing and operationalizing motivation (Eccles & Wigfield, [<reflink idref="bib21" id="ref66">21</reflink>]), the majority of empirical studies studying reciprocity have operationalized motivation as ASC, achievement goals, self‐efficacy and the likes (Gottfried et al., [<reflink idref="bib28" id="ref67">28</reflink>]; Guay et al., [<reflink idref="bib29" id="ref68">29</reflink>]; Seaton et al., [<reflink idref="bib72" id="ref69">72</reflink>]; for meta‐analyses and reviews, see Burnette et al., [<reflink idref="bib8" id="ref70">8</reflink>]; Eccles & Wigfield, [<reflink idref="bib21" id="ref71">21</reflink>]; Marsh & Craven, [<reflink idref="bib44" id="ref72">44</reflink>]; Marsh & Martin, [<reflink idref="bib46" id="ref73">46</reflink>]; Robbins et al., [<reflink idref="bib66" id="ref74">66</reflink>]). These constructs often tap into students' <emph>beliefs or perceptions</emph> about their competence and efficacy, their <emph>expectations</emph> of success or failure and <emph>sense</emph> of control (Eccles & Wigfield, [<reflink idref="bib21" id="ref75">21</reflink>]). However, in theoretical models of general motivation, motivation is usually associated with <emph>behaviours</emph> that are employed to reach an outcome (Berridge & Robinson, [<reflink idref="bib4" id="ref76">4</reflink>]; Weiner, [<reflink idref="bib90" id="ref77">90</reflink>]). In fact, many empirical studies cite the rationale that motivation leads to active and effortful commitment to learning, implying that motivation influences achievement by inducing what could be called engagement or effort (see Figure 1).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0001.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0001.jpg" title="1 The cyclical relations between motivation and achievement, mediated by effort. The red arrows represent the cross‐lagged parameters and the green arrows are auto‐regressive parameters similar to those in Figure 4 (see below)." /> </p> <p></p> <p>In the few longitudinal motivation–achievement studies that have investigated effort, the findings differed as a function of whether subjective or objective measures of effort were used. Pinxten et al. ([<reflink idref="bib63" id="ref78">63</reflink>]) were among the first, to our knowledge, to conduct a longitudinal study on the motivation–achievement relations that also included a measure of effort. They found that although motivation and achievement had positive reciprocal effects on each other, they had <emph>negative</emph> effects on subsequent efforts. Marsh et al. ([<reflink idref="bib48" id="ref79">48</reflink>]) actually found the ASC → effort links to be non‐significant in most of their statistical models. Note that Marsh et al. ([<reflink idref="bib48" id="ref80">48</reflink>]) and Pinxten et al. ([<reflink idref="bib63" id="ref81">63</reflink>]) measured <bold>subjective</bold> effort, that is, students were asked to rate their own effort expenditure. Students might perceive that having to try hard (i.e., expending a great deal of effort) is indicative of a lack of academic ability (Baars et al., [<reflink idref="bib3" id="ref82">3</reflink>]). Subjective effort, as opposed to objective effort, might therefore have a very different relation to motivation and achievement. We concur with Pinxten et al.'s ([<reflink idref="bib63" id="ref83">63</reflink>]) and Marsh et al.'s ([<reflink idref="bib48" id="ref84">48</reflink>]) hunch that the most feasible pathway for motivation, such as ASC, to relate to achievement is via <emph>objective</emph> effort put into learning.</p> <p>Studies that do include objective measures of effort also depict a complex picture. There is some evidence for a positive link between academic achievement and effort when <emph>quality</emph> of effort is measured (e.g., adopting effective learning strategies) (Trigwell et al., [<reflink idref="bib79" id="ref85">79</reflink>]). For example, in vocabulary learning, a meta‐analysis concluded that higher‐quality strategies that make use of contextual information (e.g., example sentences) yielded higher achievement than a lower‐quality strategy in which students only study the definition of the words (Stahl & Fairbanks, [<reflink idref="bib77" id="ref86">77</reflink>]). However, when effort is measured as <emph>quantity</emph> of learning (e.g., study time), the effort–achievement relationship turns out to be negative (Koriat et al., [<reflink idref="bib38" id="ref87">38</reflink>]; Nelson & Leonesio, [<reflink idref="bib55" id="ref88">55</reflink>]; Undorf & Ackerman, [<reflink idref="bib80" id="ref89">80</reflink>]) or only significant after controlling for quality of effort (Cury et al., [<reflink idref="bib13" id="ref90">13</reflink>]; Doumen et al., [<reflink idref="bib17" id="ref91">17</reflink>]; Plant et al., [<reflink idref="bib64" id="ref92">64</reflink>]). Together, these findings suggest that to really understand the behavioural pathways of the mutual influences between motivation and achievement, it is necessary to look at objective measures of effort and to examine both quantity and quality of effort expenditure.</p> <p>Furthermore, a large volume of previous research suggests that students form implicit theories about their intelligence which are on a continuum between two extremes: incremental and entity theories. These implicit theories may affect students' quantity and quality of effort (Burnette et al., [<reflink idref="bib8" id="ref93">8</reflink>]; Dweck, [<reflink idref="bib20" id="ref94">20</reflink>]) as they have been shown to be associated with students' academic motivation in terms of achievement goals (Burnette et al., [<reflink idref="bib8" id="ref95">8</reflink>]) and are theorized to influence effort expenditure and beliefs about effort (Blackwell et al., [<reflink idref="bib6" id="ref96">6</reflink>]; Tempelaar et al., [<reflink idref="bib78" id="ref97">78</reflink>]). Students who hold an incremental perspective (or have a growth mindset) believe that their intelligence is malleable and therefore, can be changed through effort. In contrast, those who are entity theorists (or have a fixed mindset) see intelligence as a fixed entity which cannot be improved much (Dweck, [<reflink idref="bib20" id="ref98">20</reflink>]). Incremental theorists often focus on learning goals and employ mastery‐oriented strategies to reach their goals. In contrast, entity theorists more often focus on performance goals and employ helpless‐oriented strategies when facing challenges (but see recent research by Altikulaç et al. ([<reflink idref="bib1" id="ref99">1</reflink>]) and Yu and McLellan ([<reflink idref="bib93" id="ref100">93</reflink>]) for a more nuanced picture where differences in goal orientation (GO) are found even within a mindset). This means that when there are setbacks (e.g., low perceived achievement), the differences in terms of effort expenditure and GO between entity and incremental theorists will be revealed as these students will have dissimilar changes in their subsequent efforts. Yet, there has also been little empirical research on the moderating role of implicit theory of intelligence (TOI) (or mindset) by experimentally introducing setbacks in the reciprocal relationship among motivation, effort and achievement.</p> <hd id="AN0185103687-7">The present research</hd> <p>The present research was motivated by the gaps identified above and its theoretical and practical aims were threefold. The first was to replicate the findings of previous research that the relationship between motivation and achievement is a cyclical loop, each construct leading to changes in the other in the subsequent time point (Hypothesis 1). We focused on a motivation construct that was expected to fluctuate in a short timeframe which was intrinsic motivation. Additionally, while looking into the loop over time, we could estimate the relative magnitude of each of the 'motivation → achievement' and 'achievement → motivation' links.</p> <p>The second aim was to investigate the mechanism underlying the reciprocal relationship by measuring the proposed behavioural pathway between motivation and achievement: effort spent on learning. Intrinsic motivation was hypothesized to be associated with quantity and quality of objective effort, which in turn would be related to achievement which again would be linked with subsequent motivation and so on (Hypothesis 2, see Figure 1).</p> <p>Third, we examined the causality of the relations among motivation, effort and achievement by manipulating perceived achievement, which is expected to have a chain impact on the entire cycle rather than on individual constructs (Hypothesis 3a). Specifically, we expected that when experiencing setbacks, learners' levels of motivation, effort and achievement would be affected yet the relationships between these constructs would remain reciprocal.</p> <p>Next, we predicted this effect to depend on a learner's TOI. A growth mindset was expected to be associated with smaller changes in effort after a drop in perceived achievement, while a fixed mindset be linked with larger and negative changes in effort after the drop, which would have corresponding effects on the participants' achievement and subsequent motivation (Hypothesis 3b). As explained above, mindset theory also postulates implications for achievement GO and effort beliefs (EBs) of learners; we included questionnaires to measure them but would only analyse these additional constructs exploratorily.</p> <p>To reach these three aims, the present research has several innovations. First, to understand the unfolding dynamics between motivation and achievement, we made use of random‐intercept cross‐lagged panel models (RI‐CLPM). An RI‐CLPM includes a cross‐lagged parameter that reflects the effect of the previous score in one variable on the change score in another variable in the subsequent time point. Yet, unlike CLPM, this relatively new modelling technique takes into account stable individual differences or initial levels in constructs (Usami et al., [<reflink idref="bib81" id="ref101">81</reflink>]). This means that the RI‐CLPM enables uncovering possible reciprocal relations between these variables temporally and ascertaining the relative magnitude of each link while taking into account potential confounds that are unrelated to the temporal dynamics. Second, the present study was a one‐hour experiment that has a short‐timeframe, multiple‐time point design. Over eight time points, the motivation, effort and achievement of students who learn new vocabulary were measured. The use of multiple time points had the advantage of capturing the ebbs and flows of the relations between constructs over time that might be missed if measured by relatively lengthy intervals (Duff et al., [<reflink idref="bib18" id="ref102">18</reflink>]; McNeish & Hamaker, [<reflink idref="bib51" id="ref103">51</reflink>]). Finally, our experiment had a manipulation of perceived achievement. Longitudinal studies with data collected at schools (which are mostly non‐experimental designs) are informative because they provide a comprehensive overview of the multiple factors that are related to motivation and achievement. Yet, conclusions that can be drawn from non‐experimental designs are limited to associations and confounding variables that may play a role in observed effects cannot be ruled out. Therefore, experiments are the gold standard for inferring causal relations between manipulated variables and dependent ones.</p> <hd id="AN0185103687-8">METHODS</hd> <p></p> <hd id="AN0185103687-9">Sample characteristics</hd> <p>The initial sample was 354 Dutch college students (see sample size determination and final sample calculation below) who were recruited from a research university in the Netherlands and who are non‐native speakers of English. Participants who completed the experiment were remunerated with course credits.</p> <hd id="AN0185103687-10">Participant recruitment and exclusion</hd> <p>As we study motivation, we hope to align participation in the experiment with participants' own interests. The experiment was advertised as a vocabulary learning study for non‐native speakers of English to attract participants whose motivation was in line with the experimental task.</p> <p>Recruitment was done through an online booking system of the university (called SONA). Students who were required to earn credits browsed this website where our experiment was advertised. To participate, they clicked on a URL which led them to the Pavlovia server hosting the experiment. Only complete data files and questionnaires without missing data for the main variables of interest (i.e., motivation, effort, achievement and mindset) were used for analyses. To comply with the guidelines of ethics and privacy protection of the university, participants were allowed to skip demographic questions. Therefore, missing data in the demographic questionnaire were considered acceptable. We planned to stop data collection as soon as we knew that the target sample size was reached. Due to conservative overbooking, we did slightly exceed the target sample size. To make sure that we have reached the target sample size even after having to remove some participants, we checked the completeness of the data files during data collection. However, only after having stopped data collection, we analysed the data.</p> <hd id="AN0185103687-11">Manipulation check and final sample</hd> <p>Participants were excluded on the basis of failing the manipulation check and/or not participating in all time points. For the manipulation check, two questions were asked at the end of the experiment (the 'Notice' question: 'Did you notice a change in your achievement in the second round of the task?' and the 'Explain' question: 'Can you explain why the change happened?'). Based on their answers, six participants who appeared to have not believed the manipulation were excluded. Additionally, 26 participants who did not answer the manipulation check questions were also excluded (the Notice question: <emph>n</emph> = 14, and the Explain question: <emph>n</emph> = 21 with 9 participants answering neither question). Furthermore, one participant was excluded after reporting dyslexia and one answered all questions in English making it uncertain if they were a native speaker of Dutch (unique <emph>n</emph> = 1, non‐native speaker also failed manipulation check). Lastly, 16 participants missed at least one time point (unique <emph>n</emph> = 12) because four participants also failed the manipulation check. In sum, a total of 45 unique participants were excluded on the basis of not participating in all blocks and/or failing the manipulation check. The final sample consisted of 309 participants, which is very close to the planned sample size. Participants were university students, on average 19.89 years old (<emph>SD</emph> = 2.08); 56 of them (18.12%) identified as male and 3 participants indicated another gender. Mean English grade in high school was 7.14 (out of 10, <emph>SD</emph> = 0.76). Six participants completed the pre‐vocational education (VMBO) track in secondary school, 36 the higher general continued education (HAVO) track, 265 the preparatory scientific education (VWO) track and 2 participants did not specify their secondary school level.</p> <p>The study received approval from the research ethical committee of the university before conducting data collection, where an important consideration was that our experiment contained minor deception (see Achievement below). The committee received the final Stage 1 report, the consent and debriefing forms.</p> <hd id="AN0185103687-12">Pilot</hd> <p>While waiting for the review of the first version of this Stage 1 report, we ran a pilot. The modifications to the design described in this version of the report reflect both our response to the feedback of the reviewers and insights from this pilot. When the report received in‐principle acceptance, we proceeded by first running another pilot with 20 participants. The purpose of the new pilot is to identify and fix any programming issues or unclear instructions (which might lead to non‐optimal data organization in data files, potential loss of data, participant attrition, etc.). The pilot exposed some technical issues with running the experiment online and also helped us to make a decision about the fake rankings of the participants (see the Rank subsection below).</p> <hd id="AN0185103687-13">Experimental design and procedure</hd> <p>The experiment consisted of a 1 h session in which participants learned new English words. Participants were tested on English words that they did not know and received feedback on their performance, which was experimentally manipulated to rank them first as above peer average, and later below peer average.</p> <p>Due to the COVID‐19‐related restrictions, the experiment was computerized and conducted online. Participants were required to participate using a computer, a mouse and a keyboard. The experiment was programmed in Python‐based open‐sourced PsychoPy (Peirce et al., [<reflink idref="bib61" id="ref104">61</reflink>]) and was hosted online by Pavlovia (https://pavlovia.org/). First, after being directed to Pavlovia, participants read and signed the informed consent form which included general information about the experiment. If consent was given, participants would proceed to read general instructions and would be told that the goal of their task in this experiment was to enrich their English vocabulary. First, they answered a number of pre‐task questionnaires.</p> <hd id="AN0185103687-14">Pre‐task questionnaires (see Appendix A)</hd> <p></p> <hd id="AN0185103687-15">General theory of intelligence questionnaire (general TOI)</hd> <p>The general TOI consisted of four statements about fixed mindset (e.g., '<emph>To be honest, I don't think I can really change how intelligent I am</emph>') and four statements about growth mindset (e.g., '<emph>I believe I can always substantially improve how intelligent I am</emph>'). Participants indicated their responses on a 6‐point Likert scale ranging from 1 (<emph>completely disagree</emph>) to 6 (<emph>completely agree</emph>). The full questionnaire (with the reversed‐coded fixed mindset items and the growth mindset items) shows good reliability (8 items, <emph>α</emph> = .89).</p> <hd id="AN0185103687-16">Specific theory of intelligence questionnaire (specific TOI)</hd> <p>Given that someone's mindset can be different depending on the subject (Burnette et al., [<reflink idref="bib8" id="ref105">8</reflink>]), we added a specific subscale to measure beliefs about the malleability of language learning. In this version of the TOI, the term 'intelligence' is adapted into 'foreign language aptitude'. Again, this TOI consisted of four statements about the growth mindset and four statements about the entity mindset. Participants indicated their responses on a 6‐point Likert scale ranging from 1 (<emph>completely disagree</emph>) to 6 (<emph>completely agree</emph>). The full questionnaire (with the reversed coded fixed mindset items and the growth mindset items) shows good reliability (8 items, <emph>α</emph> = .89).</p> <hd id="AN0185103687-17">Goal‐orientation questionnaire</hd> <p>GO was measured by the 2 × 2 Achievement Goal Questionnaire (AGQ) which had four subscales (mastery‐approach goal, mastery‐avoidance goal, performance‐approach goal and performance‐avoidance goal) and consisted of a total of 12 items (Elliot & McGregor, [<reflink idref="bib23" id="ref106">23</reflink>]; Elliot & Murayama, [<reflink idref="bib24" id="ref107">24</reflink>]). An example item of mastery‐approach goal was '<emph>My aim is completely master the material presented in this class</emph>', of mastery‐avoidance goal was '<emph>I am striving to avoid an incomplete understanding of the course materials</emph>', of performance‐approach goal was '<emph>I am striving to do well compared to other students</emph>' and of performance‐avoidance goal was '<emph>My goal is to avoid performing poorly compared to others</emph>'. Participants indicated their responses on a 6‐point Likert scale ranging from 1 (<emph>completely disagree</emph>) to 6 (<emph>completely agree</emph>). The performance‐approach and ‐avoidance subscales both show good reliability (three items each, <emph>α</emph><subs>approach</subs> = .83, <emph>α</emph><subs>avoidance</subs> = .87). The mastery‐approach and ‐avoidance subscales both show questionable reliability (three items each, <emph>α</emph><subs>approach</subs> = .68, <emph>α</emph><subs>avoidance</subs> = .63).</p> <hd id="AN0185103687-18">EB questionnaire</hd> <p>This questionnaire consisted of four items of positive attitude (e.g., '<emph>The harder you work at something, the better you will be at it'</emph>.) and five items of negative attitude about effort (e.g., '<emph>If you're not good at a subject, working hard won't make you good at it'</emph>.) (Blackwell, [<reflink idref="bib5" id="ref108">5</reflink>]). Participants indicated their responses on a 6‐point Likert scale ranging from 1 (<emph>completely disagree</emph>) to 6 (<emph>completely agree</emph>). The negative EBs questionnaire shows questionable reliability (5 items, <emph>α</emph> = .63). The positive EBs questionnaire shows poor reliability (4 items, <emph>α</emph> = .47).</p> <hd id="AN0185103687-19">ASC questionnaire</hd> <p>Since our experimental task tapped into verbal ability (see below), we administered the subscale of verbal self‐concept (and not the other subscales) of the widely used Self‐Description Questionnaire III (Marsh, [<reflink idref="bib43" id="ref109">43</reflink>]; Marsh & O'Neill, [<reflink idref="bib47" id="ref110">47</reflink>]). The verbal subscale consisted of 10 items (e.g., '<emph>I have a poor vocabulary'</emph>). Participants indicated their responses on a 6‐point Likert scale ranging from 1 (<emph>not true</emph>) to 6 (<emph>true</emph>). The ASC questionnaire shows good reliability (10 items, <emph>α</emph> = .80). Although, in the present research, ASC was not our focus, given the importance of ASC in previous research, we included this measure for exploratory investigation.</p> <hd id="AN0185103687-20">Word selection phase</hd> <p>In this phase, participants saw the words that they would potentially learn (see the Section 2.5.4 below). From a total pool of 1305 items (see Section 2.5.4 below), words were presented one by one on the screen and participants indicated whether they already knew the meaning of each word by answering 'Yes', 'Kind of' and 'No'. This phase ended when the participant indicated a total of 250 words as unknown (i.e., to which they answered 'No') which were used in the subsequent phases (for that particular participant). We planned that participants who did not reach 250 unknown words would not be asked to continue to the next phases of the experiment and would be reimbursed with half the study credits. One participant did not reach 250 words and was able to stop their participation. An overview of the experimental design is in Figure 2.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0002.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0002.jpg" title="2 Schematic presentation of the experimental design and trial structure. Each block consisted of multiple trials depending on the speed of individual participants. Note that for the purpose of illustration, the number of blocks in each round is four. Yet, in the experiment, the number of blocks within a round differed across participants, that is, participants experienced the achievement drop/setback at different moments in the task. The total number of blocks was always eight for all participants." /> </p> <p></p> <hd id="AN0185103687-22">Learning and testing phase</hd> <p></p> <hd id="AN0185103687-23">Round structure</hd> <p>Participants then entered the next phase which started with a practice round followed by two experimental rounds. The trials in the practice round were identical to those in the experimental rounds, but the practice round only consisted of 10 trials and 5 words. Participants were informed that the practice round was to familiarise them with the task and their scores were not calculated until the 'real experiment' started. To ensure that participants understood the task well, participants had to practise until they had an accuracy rate of 60% or above and demonstrated at least a total of 3 s of the quantity of effort measure (see Effort below). Participants could proceed to the experimental rounds if they pass this test or if they have done the practice rounds at least three times.</p> <p>Each experimental round consisted of multiple blocks (see Block Structure below and Figure 2), and each block consisted of multiple trials (see Trial Structure below). The two experimental rounds were identical to each other except for the feedback (actual and manipulated achievement) that appeared at the end of each <emph>block</emph>.</p> <hd id="AN0185103687-24">Achievement (actual and manipulated)</hd> <p>At the end of each block, participants saw their score and the scores of 'other participants' of that block. This feedback thus included <emph>two</emph> pieces of information: <emph>score</emph>, that is, actual achievement, and <emph>rank</emph>, that is, manipulated achievement. Crucially, there were two variants of the manipulated achievement: above average and below average. This manipulation concerned the rank, but not the score. The first round consisted of blocks that ended with 'above‐average' rank and the second round consisted of blocks with 'below‐average' rank. A normal distribution of scores of 19 'other participants' (fake participants) was generated on the fly from the score of the real participant in that block. In the <emph>above</emph>‐average round, the 'other participants' and their scores were selected from the distribution so that the participant was randomly either ranked third, fourth or fifth in the list that displayed a total of 20 participants. In the <emph>below</emph>‐average round, the 'other participants' and their corresponding scores were selected from the same distribution in a way that the rankings of the real participants were either the 13th, 14th or 15th in the list of 20 participants. The order of the rounds was fixed to create an achievement drop (or a setback) from above to below average. The ranking positions of the fake participants were created using an inverse sigmoid function with the score and ranking of the participants in each block as input (see Appendix B) to create a believable change in achievement. See Figure 3 for examples of the two variants of feedback manipulation.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0003.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0003.jpg" title="3 Example of the manipulation of achievement feedback used in the learning and testing phase. At the end of each block, participants were shown a scoring board which contained two pieces of information: (1) their actual achievement (the score, i.e., the percentage of correct answers in that block) and (2) the manipulated achievement (the ranking of the participant relative to 'other participants' in that block). In the blocks of the first round, no matter what score the participants achieved, they were shown a scoring board where their ranks were above average. Conversely, in the blocks of the second round, regardless of their actual achievement, below‐average scoring boards were shown. See details in Achievement (actual and manipulated)." /> </p> <p></p> <p>Importantly, the number of blocks within a round differed across participants, that is, participants experienced the achievement drop/setback at different moments in the task. One‐third of the participants had four above‐average and four below‐average blocks (like in Figure 2). Another third had three above‐average and five below‐average blocks and the last third had five above‐average and three below‐average blocks (currently not illustrated in Figure 2). Participants were randomly assigned to these 'conditions'. This feature was not a focus of the experimental design. It only allowed us to rule out the possibility that the drop in motivation and effort was a result of fatigue and not of the feedback manipulation. If fatigue was the cause, then motivation and effort might wane generally at the later blocks regardless of the conditions. If not, motivation and effort should just follow the feedback manipulation.</p> <hd id="AN0185103687-26">Trial structure</hd> <p>In each trial, after the 500 ms fixation cross, 1 of the participant's individual pool of 250 unknown words was randomly drawn for presentation. Its meaning (i.e., correct Dutch translation) together with three incorrect alternatives (thus, four Dutch words in total) were presented on the screen and participants were prompted to click and choose an option. If they chose a correct response, then a 'Correct!' screen popped up and the next trial began. If their response was incorrect, then the 'Incorrect!' screen popped up and subsequently, participants were asked to choose: either to view only the meaning of the word (i.e., effort of lower quality) or the meaning of the word <emph>and</emph> an example sentence (i.e., effort of higher quality) (Stahl & Fairbanks, [<reflink idref="bib77" id="ref111">77</reflink>]). The choice of the participant was our measure of <emph>quality</emph> of effort. Whichever option they chose, they had to press a mouse button for the materials to be displayed on the screen. If they did not press, the screen remained blank until the end of the duration of the trial. The amount of time that participants pressed the mouse button constituted our measure of <emph>quantity</emph> of effort.</p> <p>To prevent excessively large differences in the number of trials across participants (as blocks have a fixed length, but trials do not), an upper and lower limit was imposed on the duration of trial. The duration of each trial was at least 7 s (even if participants had a correct answer) and at most 12 s (even if participants spent time viewing the materials). Pilot results showed that participants never spent more than 5 s on viewing the learning materials. Words to which participants provided an incorrect response randomly reappeared in a future trial that is between two and eight trials away. This meant that a majority of the words reappeared within the block. The next trial was always a word randomly drawn from the learning subset, and never the same word as in the immediately preceding two trials.</p> <hd id="AN0185103687-27">Block structure</hd> <p>Each word reappeared until it was answered correctly three times or the block ended. A block ended after it exceeded the total block time of 4 min, resulting in a maximum of 34 trials and a minimum of 20 trials per block. A unique subset of 25 words was created for each block. When a word was answered correctly three times, it was taken out of the learning subset of the block to be replaced by another word from the subset. The use of a learning subset per block was necessary to ensure that it did not take too long for a word to repeat. Words that were already used in a previous block, even when not yet learned, did not appear in subsequent blocks. After each block, participants <emph>first received the achievement feedback</emph> (both the actual, i.e., scores and the manipulated feedback, i.e., ranking) and <emph>then</emph> responded to the motivation measure (see Motivation below). This order (see Figure 2) was important because achievement was expected to influence their subsequent motivation and behaviours.</p> <hd id="AN0185103687-28">Motivation</hd> <p>At the end of every block, a three‐part question appeared on the screen: '<emph>I am doing this task</emph>: <emph>... because it is fun. ... because I enjoy doing it. ... because it is fun to answer hard questions</emph>'. Participants were instructed to drag the cursor to a point that corresponds to their answer on three lines below each of these three questions (visual analogue scale) (de Boer et al., [<reflink idref="bib14" id="ref112">14</reflink>]; Gallagher et al., [<reflink idref="bib27" id="ref113">27</reflink>]). One end of each line corresponded to '<emph>completely disagree</emph>' and the other end '<emph>completely agree</emph>' (corresponding to a score between 1 and 100), with intermittent anchors at 10‐score intervals. These questions were the intrinsic motivation subscale from the Academic Self‐Regulation Scale (Ryan & Connell, [<reflink idref="bib69" id="ref114">69</reflink>]), which was a well‐validated and widely used questionnaire connected to self‐determination theory.</p> <hd id="AN0185103687-29">Manipulation check</hd> <p>After completing the learning and testing phase, participants were asked if they noticed any change in their achievement in the second round of the task (i.e., the Notice question). We also asked (i.e., the 'Explain' question) if they could guess why the change happened (without giving it away) and planned to discard those who correctly guessed that the feedback was rigged. Additionally, participants were also asked what they did when they pressed the mouse to keep the materials displayed. This was to check whether they indeed studied the meanings of the words. The answers from the participants confirmed that this was indeed the case.</p> <hd id="AN0185103687-30">Demographic questionnaire</hd> <p>Finally, participants answered the demographic questionnaire which consisted of questions regarding participants' age, gender, study track in high school and their English grade in high school.</p> <p>Then, they were thanked and debriefed thoroughly about the manipulated achievement before being directed back to the SONA system to collect their study credit.</p> <hd id="AN0185103687-31">Stimuli</hd> <p>In this experiment, we used the pool of 1305 words from the website Exam Word (https://<ulink href="http://www.examword.com/difficult/gre">www.examword.com/difficult/gre</ulink>) which provided the study materials to prepare for the Graduate Record Examinations (GRE), a standardized test for admission to graduate schools in the United States and Canada. The correct option for the meaning of each word was taken from the equivalent in Dutch provided by various English–Dutch, English–English and Dutch–Dutch dictionaries. The other (incorrect) options were Dutch translations of other words within the pool. The translations example sentences were chosen by the first author and research assistants who were native speakers of Dutch. The criteria were that the sentences must come from a dictionary (Merriam Webster or Longman), not longer than 100 letters, must independently give some clues to the meaning of the word and there must be correspondence between the Dutch translation and the meaning of the English word in the example sentence. The full stimulus list can be found at https://osf.io/de4j5/?view_only=f16516d796a04c26a2115d1a5712209f.</p> <hd id="AN0185103687-32">Variables</hd> <p></p> <hd id="AN0185103687-33">Questionnaire variables</hd> <p>For each subscale of the GO, EB and verbal ASC (vASC) questionnaires, we computed individual mean scores. Regarding general TOI, the fixed mindset statements were reverse scored and added to the growth mindset score, so the higher a sum score (general mindset score, gTOI) it was assumed that the more of a general growth mindset the participant endorsed (De Castella & Byrne, [<reflink idref="bib16" id="ref115">16</reflink>]; Dweck, [<reflink idref="bib20" id="ref116">20</reflink>]). The same treatment was used for the specific TOI subscale, resulting in specific mindset scores (sTOI).</p> <hd id="AN0185103687-34">Motivation</hd> <p>Motivation score was the average motivation rating on the three intrinsic motivation items that participants rated at the end of every block. There were eight motivation scores corresponding to eight experimental blocks.</p> <hd id="AN0185103687-35">Effort</hd> <p></p> <hd id="AN0185103687-36">Effort quality</hd> <p>Qualitative effort was the percentage of time per block that participants chose the option of higher‐quality effort (i.e., viewing the meaning of the word and the example sentence) over the total number of incorrect trials in that particular block (i.e., the total number of times that participants could make a choice). There were eight measurements of quality of effort corresponding to eight experimental blocks.</p> <p>In non‐SEM models, we planned to directly model every choice that participants made in each block (i.e., the coding scheme is 1 = higher‐quality effort and 0 = lower‐quality effort). See Results for the specifics.</p> <hd id="AN0185103687-37">Effort quantity</hd> <p>Quantitative effort is the average time per block that participants spent actively viewing the learning materials. There were eight measurements of quantity of effort corresponding to eight experimental blocks.</p> <hd id="AN0185103687-38">Actual achievement (score)</hd> <p>The score was computed as the proportion of trials answered correctly within a block, after excluding those trials in which a word was presented the first time (since on such trials participants can only guess). For example, consider a participant who completed 41 trials in a block, of which 11 were new words presented for the first time; of the remaining 30, if that participant answered 12 correctly, (s)he would score 40% in that block. There were eight measurements of achievement corresponding to eight experimental blocks.</p> <hd id="AN0185103687-39">Manipulated achievement (rank)</hd> <p>In the <emph>above</emph>‐average round, participants' scores were randomly either ranked third, fourth or fifth. In the <emph>below</emph>‐average round, the ranking of the participants' scores was either the 13th, 14th or 15th. In the pilot, we also included a version of the experiment where the rankings for the above‐average round were set as the 7th, 8th and 9th and the rankings for the below‐average round as the 11th, 12th and 13th. The results indicated that there were no significant differences between the two versions of the experiment in terms of motivation, effort and actual achievement. Therefore, we decided to only include the version with the biggest drop in rank. There were eight measurements of rank corresponding to eight experimental blocks.</p> <hd id="AN0185103687-40">ANALYSIS PLAN</hd> <p>All analyses were conducted in R using the <emph>lavaan</emph> package (Version 5.22 (Rosseel, [<reflink idref="bib67" id="ref117">67</reflink>])) and based on the R scripts by Usami et al. ([<reflink idref="bib81" id="ref118">81</reflink>]). Full‐information maximum likelihood with robust standard errors to account for missingness and non‐normality will be used. There were violations of multivariate normality; however, the robust weighted least squares estimator could not be used. Below we explained why and described the alternative.</p> <hd id="AN0185103687-41">Modelling specifications</hd> <p>We fit a series of RI‐CLPM (Usami et al., [<reflink idref="bib81" id="ref119">81</reflink>]) and follow Usami et al.'s ([<reflink idref="bib81" id="ref120">81</reflink>]) modelling framework and rationale (paraphrased in the following text). First, RI‐CLPM had two classes of parameters that could serve our purposes: the auto‐regressive parameter (<emph>β</emph>) and the cross‐lagged parameter (<emph>γ</emph>). The latter was the parameter for inferring reciprocal relations between the variables. They represented a simple partial regression coefficient from the predictor (e.g., motivation) to the outcome variable (e.g., achievement) after controlling for the effect of the outcome variable at the previous time point. Crucially, the RI‐CLPM framework allowed us to directly model (within‐person) changes over time as a function of structural parameters. The second (and third) domains of interest were measured on the same number of occasions as the first domain. This allowed for the investigation of 'cross‐domain' or cross‐lagged relations that captured the extent to which change in one domain (actual achievement) was a function of the level of the second domain (motivation), whose change was in turn a function of the third domain (qualitative or quantitative effort). These dynamics were investigated in a series of RI‐CLPM models (see Model Comparisons below). Likelihood‐ratio tests of these dynamic parameters furnished evidence for, or against, models that represent unilateral or bidirectional hypothesized causal influences.</p> <p>Second, the RI‐CLPM differs from the CLPM (whose flaws were discussed in the previous research reviewed in the Introduction) in that it explicitly models stable between‐person differences over time. The measurements of variables motivation and achievement at time <emph>t</emph> for individual <emph>i</emph> were a combination of temporal group means, an invariant component of a variable in an individual over time and residuals. The RI‐CLPM then separates stable between‐person differences (i.e., the invariant component) from within‐person fluctuations over time and the regression coefficients represent the amount of within‐person carry‐over. In addition, our pilot data showed large individual differences in all three key variables of interest (namely, motivation, effort and achievement); therefore, the random intercepts in RI‐CLPM were also useful in this respect.</p> <p>Finally, the RI‐CLPM model could capture at least four different motivation–effort–achievement relationships. First, we have motivation–effort covariance and the effort–achievement covariance at time point 1. Second, we had motivation–effort and effort–achievement cross‐lagged parameters (shown in red in Figure 4). Third, we had a rate, or degree, of change over time of the domains (auto‐regressive parameters, shown in green). Third, we have an estimate of correlated changes (currently not shown yet in Figure 4), reflecting the degree to which the domains' changes co‐occur after taking into account the cross‐lagged pathways.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0004.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0004.jpg" title="4 Illustration of the planned trivariate RI‐CLPM models. Here, the three main groups of variables of interest are motivation, effort and achievement. Mt (and mt) are the motivation variables, et the effort variable, At (and at) the actual achievement variables and Rt the rankings (manipulated achievement), with t indicating the time point (block). Circles indicate latent variables and rectangles indicate observed variables. Ia, Ie and Im are the random intercepts of the achievement, effort and motivation variables. Note that a*, m* and e* are the latent variables where the mean and intercepts are removed, reflecting the dissociation of between‐person differences (i.e., the invariant component) from within‐person fluctuations over time (see Model Specifications below). The arrows denoting the cross‐lagged parameter are in red and the auto‐regressive parameter is in green. For visual clarity, variances and covariances are not included in the figure. For the same reason, we only include the latent variable structure for e*, but in the analysis, observed variables of effort were modelled using the same structure as a* (see further details in Model Specifications). The ranks (manipulated achievement) were modelled as observed variables that influence motivation directly." /> </p> <p></p> <p>Note that we modelled the rankings (manipulated achievement) as observed variables that influence motivation directly. Therefore, the achievement‐related cross‐lagged parameters we referred to from this point on are linked to the latent variables of <emph>actual</emph> achievement, not manipulated achievement. Qualitative and quantitative efforts were modelled separately (see details in Hypothesis 2 below).</p> <hd id="AN0185103687-43">Model comparisons and parameter estimates</hd> <p>The RI‐CLPM model as specified in Figure 4 was fitted separately for the first round and second round.</p> <p>Hypothesis 1 was that the relationship between motivation and achievement would be a cyclical loop, each link leading to changes in the other in the subsequent time point in a short timeframe. To test this hypothesis, we examined bivariate models (see Figure 5) which were the core of the trivariate models presented in Figure 4. The only difference was that the bivariate models did not include the Effort variables which were not relevant for Hypothesis 1.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0005.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0005.jpg" title="5 Illustration of the planned bivariate RI‐CLPM models to test Hypothesis 1. Here, the two main variables of interest are motivation and achievement. This figure is largely identical to Figure 4; the only difference is the exclusion of the Effort variables. All other notations follow the convention of those in Figure 4." /> </p> <p></p> <p>Hypothesis 2 was that the underlying behavioural pathway from motivation to achievement was effort spent on learning. Intrinsic motivation was expected to lead to changes in quantity and quality of objective effort, which in turn led to changes in achievement which again fed into subsequent motivation and so on. To test this hypothesis, we examined trivariate models that did include the Effort variables (Figure 4). Qualitative and quantitative efforts were modelled in separate series of models. Henceforth, we use the term 'effort' to refer to qualitative and quantitative efforts collectively for the sake of brevity.</p> <p>Hypothesis 3a and Hypothesis 3b was about causality of the relations among motivation, effort and achievement. When introducing the experimental manipulation of perceived achievement, we expected this to have an impact on the cycle rather than on individual constructs (Hypothesis 3a). To test Hypothesis 3a, we compared the values of the key parameters and mean scores in the first round of the task with those in the second round of the task. The expectations of the changes in the parameters and scores are presented in the table below.</p> <p></p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">In the second round of the experimental task, compared to the first round</th></tr></thead><tbody valign="top"><tr><td align="left">Auto‐regressive parameter</td><td align="left">Would not change</td></tr><tr><td align="left">Cross‐lagged parameter</td><td align="left">Would not change</td></tr><tr><td align="left">Means of scores</td><td align="left">Means of motivation, effort and actual achievement decrease (3a) and the change would be even larger when the mindset score decreases (as lower score indicates more of a fixed mindset) (3b)</td></tr></tbody></table> </ephtml> </p> <p>To test the role of mindset (Hypothesis 3b), the general TOI and specific TOI scores were to be regressed separately on the motivation, effort and achievement scores.</p> <p>Previous research has also demonstrated that the item‐specific difficulty of the task (or the item‐specific knowledge level) may moderate the pathway from motivation to effort choice (Koriat et al., [<reflink idref="bib38" id="ref121">38</reflink>]; Undorf & Ackerman, [<reflink idref="bib80" id="ref122">80</reflink>]). Given that each participant worked with their unique stimuli set (see the word selection phase in Figure 2) where each item had been judged by the participant him/herself to be completely unknown (and therefore possibly of relative difficulty), we did not expect this to have a systematic impact on the dynamics between the variables of interests. However, to be 100% certain, since we cannot rule out any effect of similarity based on previously incorrect answers, we would conduct an extra multi‐level model with random intercepts across stimuli to check for any potential effects due to the potential differences in perceived difficulty across stimuli. All other analyses were exploratory.</p> <hd id="AN0185103687-45">Statistical criteria for model comparison</hd> <p>We planned to assess overall model fit via the chi‐square test, the root mean square error of approximation (RMSEA; acceptable fit: <.08, good fit: <.05), the comparative fit index (CFI; acceptable fit:.95–.97, good fit: >.97) and the standardized root mean square residual (SRMR; acceptable fit:.05–.10, good fit: <.05 (Schermelleh‐Engel et al., [<reflink idref="bib70" id="ref123">70</reflink>])). We compared the models in three ways: overall model fit (cf. (Schermelleh‐Engel et al., [<reflink idref="bib70" id="ref124">70</reflink>])), information criteria (Akaike's information criterion, AIC, and Bayesian information criterion, BIC) and Akaike weights (Wagenmakers & Farrell, [<reflink idref="bib88" id="ref125">88</reflink>]), which use differences in AICs and BICs to quantify the relative likelihood of a model being the best among the set of competitors, given the data. To ease parameter comparisons, we planned to always ask for standardized coefficients in the analyses.</p> <hd id="AN0185103687-46">Data simulation, sample size determination and model complexity</hd> <p>A set of data were simulated and the codes are provided in Data S1. For the sake of sample size estimate, the data simulation was based on a bivariate model (Figure 5) which was the core of the trivariate model (Figure 4). Hereby, we assumed that if there were a reciprocal relationship between motivation and achievement, then the effect would be mediated by effort. Hence, to find a mediator effect by effort, we should be certain to find cross‐lagged effects between motivation and achievement in the bivariate model. In other words, data simulation was constructed to be sufficient for conducting the test Hypothesis 1 (see above), leaving the Effort variables out of the equation. Using the RI‐CLPM framework, we modelled the change scores on two domains (motivation and achievement) as the function of two processes: an auto‐regressive process (<emph>β</emph>) and a cross‐lagged process (<emph>γ</emph>), as explained above (see Modelling Specifications).</p> <p>Since we did not have strong expectations of the effect size, we studied the power of the most important model comparison for a large range of possible values of cross‐lagged parameters. We set the possible values of the cross‐lagged parameters to be 0, 0.025, 0.05, 0.1, 0.2, 0.2 and 0.3. The sample size was 300 and the number of replicated datasets was 500. The key question was with the sample size of 300 as informed by the rule of thumb–how the power of the study would vary when the real effect sizes vary. In each replication, we fitted models to the simulated data and then compared the models to the differences between the BIC (Wagenmakers & Farrell, [<reflink idref="bib88" id="ref126">88</reflink>]). As can be seen in Figure 6, when the cross‐lagged parameter was of interest and of a magnitude similar to what was found in previous studies, the data simulation showed that, with a sample size of 300, we would achieve desirable power (>0.75).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0006.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0006.jpg" title="6 Plots showing estimated power given various possible values of cross‐lagged parameters capture how the change in one construct of interest is determined by another in the previous time point (and vice versa). Power is estimated with regard to the three different model comparisons based on the BIC differences. 'Free' is the model where both cross‐lagged parameters are freely estimated (Model 1). 'Unilateral' is where a unidirectional relationship (i.e., either one pathway) is freely estimated and the other constrained to 0 (Model 2a and 2b, which are identical from the perspective of data simulation). 'Full' is the fully constrained model where both directions are constrained to 0 (Model 3). The simulation shows that with a sample size of 300 participants, we have the power greater than 0.75 to detect the reciprocal relationship of effect size of 0.1." /> </p> <p></p> <hd id="AN0185103687-48">Open resources</hd> <p>The codes to build the experiment are hosted and made freely accessible on GitLab at the following address: https://gitlab.pavlovia.org/letris/letris_210201.</p> <hd id="AN0185103687-49">STAGE 2 REPORT</hd> <p></p> <hd id="AN0185103687-50">Results</hd> <p></p> <hd id="AN0185103687-51">Registered analyses</hd> <p>Aggregated scores across items and descriptive statistics (<emph>M</emph>, <emph>SD</emph> and correlations) for the motivation and actual achievement measures by time point are shown in Tables 1, 2a, 2b and 3. Intercorrelations of motivation from one time point to the next were high (<emph>rs</emph> ranging from.78 to.96). Moderate correspondence was found for achievement (<emph>rs</emph> ranging from.48 to.63). Moderate‐to‐high associations for quantitative effort across waves were found (<emph>rs</emph> ranging from.69 to.95). Qualitative effort was highly consistent across waves (<emph>rs</emph> ranging from.73 to.98). Performance and motivation did not always correlate and when they did, only weakly so (<emph>r</emph> ranging from.11 to.23). No significant correlations between motivation and quantitative effort were found. Performance and quantitative effort also sometimes correlated weakly (<emph>r</emph> ranging from.12 to.29). Similarly, the correspondence between quantitative effort and qualitative effort was small (<emph>r</emph> ranging from.18 to.30).</p> <p>1 TABLE Descriptive statistics: Mean and standard deviations of the main variables.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variable</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left">St. dev.</th><th align="left">Min</th><th align="left">Max</th></tr></thead><tbody valign="top"><tr><td align="left">Ach_T1</td><td align="left">309</td><td align="char" char=".">83.645</td><td align="char" char=".">12.173</td><td align="char" char=".">19.231</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T2</td><td align="left">309</td><td align="char" char=".">84.664</td><td align="char" char=".">12.480</td><td align="char" char=".">20.690</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T3</td><td align="left">309</td><td align="char" char=".">84.041</td><td align="char" char=".">13.627</td><td align="char" char=".">13.636</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T4</td><td align="left">309</td><td align="char" char=".">83.744</td><td align="char" char=".">14.723</td><td align="char" char=".">12.121</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T5</td><td align="left">309</td><td align="char" char=".">83.252</td><td align="char" char=".">13.790</td><td align="char" char=".">14.286</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T6</td><td align="left">309</td><td align="char" char=".">83.978</td><td align="char" char=".">14.010</td><td align="char" char=".">17.500</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T7</td><td align="left">309</td><td align="char" char=".">81.786</td><td align="char" char=".">15.632</td><td align="char" char=".">7.692</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Ach_T8</td><td align="left">309</td><td align="char" char=".">82.472</td><td align="char" char=".">15.588</td><td align="char" char=".">12.500</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Mot_T1</td><td align="left">309</td><td align="char" char=".">43.006</td><td align="char" char=".">20.038</td><td align="char" char=".">0.000</td><td align="char" char=".">95.000</td></tr><tr><td align="left">Mot_T2</td><td align="left">309</td><td align="char" char=".">42.706</td><td align="char" char=".">21.660</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Mot_T3</td><td align="left">309</td><td align="char" char=".">41.845</td><td align="char" char=".">22.303</td><td align="char" char=".">0.000</td><td align="char" char=".">99.333</td></tr><tr><td align="left">Mot_T4</td><td align="left">309</td><td align="char" char=".">40.891</td><td align="char" char=".">22.905</td><td align="char" char=".">0.000</td><td align="char" char=".">99.000</td></tr><tr><td align="left">Mot_T5</td><td align="left">309</td><td align="char" char=".">39.632</td><td align="char" char=".">23.649</td><td align="char" char=".">0.000</td><td align="char" char=".">99.000</td></tr><tr><td align="left">Mot_T6</td><td align="left">309</td><td align="char" char=".">38.041</td><td align="char" char=".">23.256</td><td align="char" char=".">0.000</td><td align="char" char=".">99.000</td></tr><tr><td align="left">Mot_T7</td><td align="left">309</td><td align="char" char=".">36.839</td><td align="char" char=".">22.972</td><td align="char" char=".">0.000</td><td align="char" char=".">99.333</td></tr><tr><td align="left">Mot_T8</td><td align="left">309</td><td align="char" char=".">35.939</td><td align="char" char=".">23.687</td><td align="char" char=".">0.000</td><td align="char" char=".">97.667</td></tr><tr><td align="left">Quant_T1</td><td align="left">309</td><td align="char" char=".">1.635</td><td align="char" char=".">1.177</td><td align="char" char=".">0.000</td><td align="char" char=".">5.557</td></tr><tr><td align="left">Quant_T2</td><td align="left">309</td><td align="char" char=".">1.467</td><td align="char" char=".">1.177</td><td align="char" char=".">0.000</td><td align="char" char=".">6.208</td></tr><tr><td align="left">Quant_T3</td><td align="left">309</td><td align="char" char=".">1.356</td><td align="char" char=".">1.174</td><td align="char" char=".">0.000</td><td align="char" char=".">6.297</td></tr><tr><td align="left">Quant_T4</td><td align="left">309</td><td align="char" char=".">1.271</td><td align="char" char=".">1.147</td><td align="char" char=".">0.000</td><td align="char" char=".">6.186</td></tr><tr><td align="left">Quant_T5</td><td align="left">309</td><td align="char" char=".">1.247</td><td align="char" char=".">1.150</td><td align="char" char=".">0.000</td><td align="char" char=".">6.224</td></tr><tr><td align="left">Quant_T6</td><td align="left">309</td><td align="char" char=".">1.245</td><td align="char" char=".">1.155</td><td align="char" char=".">0.000</td><td align="char" char=".">6.060</td></tr><tr><td align="left">Quant_T7</td><td align="left">309</td><td align="char" char=".">1.186</td><td align="char" char=".">1.109</td><td align="char" char=".">0.000</td><td align="char" char=".">5.982</td></tr><tr><td align="left">Quant_T8</td><td align="left">309</td><td align="char" char=".">1.158</td><td align="char" char=".">1.097</td><td align="char" char=".">0.000</td><td align="char" char=".">6.017</td></tr><tr><td align="left">Qual_T1</td><td align="left">309</td><td align="char" char=".">20.402</td><td align="char" char=".">35.330</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T2</td><td align="left">309</td><td align="char" char=".">21.421</td><td align="char" char=".">37.319</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T3</td><td align="left">309</td><td align="char" char=".">20.733</td><td align="char" char=".">36.740</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T4</td><td align="left">309</td><td align="char" char=".">20.892</td><td align="char" char=".">37.496</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T5</td><td align="left">309</td><td align="char" char=".">20.262</td><td align="char" char=".">37.680</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T6</td><td align="left">309</td><td align="char" char=".">20.566</td><td align="char" char=".">37.673</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T7</td><td align="left">309</td><td align="char" char=".">21.379</td><td align="char" char=".">38.839</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr><tr><td align="left">Qual_T8</td><td align="left">309</td><td align="char" char=".">22.009</td><td align="char" char=".">38.275</td><td align="char" char=".">0.000</td><td align="char" char=".">100.000</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>: Each variable is the aggregated score (across items for motivation and across trials in a block for achievement, quantitative and qualitative effort).</p> <ulist> <item>2 Abbreviations: Ach, Achievement; M, mean; Mot, motivation; Qual, qualitative effort; Quant, quantitative effort; St. Dev., standard deviation; T, stands for timepoint.</item> </ulist> <p>2A TABLE Correlation matrix: Achievement, motivation and quantitative effort.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variable</th><th align="left">Ach1</th><th align="left">Ach2</th><th align="left">Ach3</th><th align="left">Ach4</th><th align="left">Ach5</th><th align="left">Ach6</th><th align="left">Ach7</th><th align="left">Ach8</th><th align="left">Mot1</th><th align="left">Mot2</th><th align="left">Mot3</th><th align="left">Mot4</th><th align="left">Mot5</th><th align="left">Mot6</th><th align="left">Mot7</th><th align="left">Mot8</th></tr></thead><tbody valign="top"><tr><td align="left">Ach1</td></tr><tr><td align="left">Ach2</td><td align="char" char=".">0.53***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Ach3</td><td align="char" char=".">0.54***</td><td align="char" char=".">0.54***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Ach4</td><td align="char" char=".">0.48***</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.63***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Ach5</td><td align="char" char=".">0.48***</td><td align="char" char=".">0.54***</td><td align="char" char=".">0.57***</td><td align="char" char=".">0.59***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Ach6</td><td align="char" char=".">0.58***</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.54***</td><td align="char" char=".">0.59***</td><td align="char" char=".">0.58***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Ach7</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.45***</td><td align="char" char=".">0.52***</td><td align="char" char=".">0.52***</td><td align="char" char=".">0.49***</td><td align="char" char=".">0.60***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Ach8</td><td align="char" char=".">0.53***</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.57***</td><td align="char" char=".">0.57***</td><td align="char" char=".">0.52***</td><td align="char" char=".">0.63***</td><td align="char" char=".">0.57***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot1</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.09</td><td align="char" char=".">0.09</td><td align="char" char=".">0.07</td><td align="char" char=".">0.07</td><td align="char" char=".">0.09</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot2</td><td align="char" char=".">0.21***</td><td align="char" char=".">0.10</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.09</td><td align="char" char=".">0.92***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot3</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.15**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.90***</td><td align="char" char=".">0.95***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot4</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.09</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.11</td><td align="char" char=".">0.86***</td><td align="char" char=".">0.93***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot5</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.08</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.86***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.93***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot6</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.10</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.84***</td><td align="char" char=".">0.89***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.94***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot7</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.09</td><td align="char" char=".">0.82***</td><td align="char" char=".">0.87***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.92***</td><td align="char" char=".">0.94***</td><td align="left" /><td align="left" /></tr><tr><td align="left">Mot8</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.78***</td><td align="char" char=".">0.83***</td><td align="char" char=".">0.85***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.90***</td><td align="char" char=".">0.93***</td><td align="char" char=".">0.95***</td><td align="left" /></tr><tr><td align="left">Quant1</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.21***</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.19***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.00</td><td align="char" char=".">0.05</td><td align="char" char=".">0.06</td><td align="char" char=".">0.05</td><td align="char" char=".">0.07</td><td align="char" char=".">0.07</td><td align="char" char=".">0.07</td><td align="char" char=".">0.07</td></tr><tr><td align="left">Quant2</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.15**</td><td align="char" char=".">0.23***</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.02</td><td align="char" char=".">0.04</td></tr><tr><td align="left">Quant3</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.07</td><td align="char" char=".">0.21***</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.19***</td><td align="char" char=".">0.24***</td><td align="char" char=".">−0.04</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.01</td><td align="char" char=".">0.03</td><td align="char" char=".">0.01</td><td align="char" char=".">0.04</td></tr><tr><td align="left">Quant4</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.08</td><td align="char" char=".">0.19***</td><td align="char" char=".">0.22***</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.25***</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.05</td><td align="char" char=".">0.02</td><td align="char" char=".">0.04</td></tr><tr><td align="left">Quant5</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.07</td><td align="char" char=".">0.19***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.24***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.23***</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.04</td><td align="char" char=".">0.01</td><td align="char" char=".">0.03</td></tr><tr><td align="left">Quant6</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.03</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.22***</td><td align="char" char=".">0.19***</td><td align="char" char=".">0.21***</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.01</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.02</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.02</td></tr><tr><td align="left">Quant7</td><td align="char" char=".">0.15*</td><td align="char" char=".">0.06</td><td align="char" char=".">0.15*</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.10</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.21***</td><td align="char" char=".">−0.03</td><td align="char" char=".">0.01</td><td align="char" char=".">0.00</td><td align="char" char=".">−0.01</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.00</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.01</td></tr><tr><td align="left">Quant8</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.04</td><td align="char" char=".">0.15**</td><td align="char" char=".">0.15**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.21***</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.24***</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.01</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.01</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.02</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note</emph>: Numbering is time point. Ach variable is the aggregated score (across items for motivation and across trials in a block for achievement, quantitative and qualitative effort).</item> <item>4 Abbreviations: Ach, stands for Achievement; Mot, motivation; Quant, quantitative effort.</item> <item>5 *<emph>p</emph> < .1; **<emph>p</emph> < .05; ***<emph>p</emph> < .01.</item> </ulist> <p>2B TABLE Correlation matrix: Achievement, motivation and qualitative effort.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variable</th><th align="left">Ach1</th><th align="left">Ach2</th><th align="left">Ach3</th><th align="left">Ach4</th><th align="left">Ach5</th><th align="left">Ach6</th><th align="left">Ach7</th><th align="left">Ach8</th><th align="left">Mot1</th><th align="left">Mot2</th><th align="left">Mot3</th><th align="left">Mot4</th><th align="left">Mot5</th><th align="left">Mot6</th><th align="left">Mot7</th><th align="left">Mot8</th></tr></thead><tbody valign="top"><tr><td align="left">Perf1</td></tr><tr><td align="left">Perf2</td><td align="char" char=".">0.53***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perf3</td><td align="char" char=".">0.54***</td><td align="char" char=".">0.54***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perf4</td><td align="char" char=".">0.48***</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.63***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perf5</td><td align="char" char=".">0.48***</td><td align="char" char=".">0.54***</td><td align="char" char=".">0.57***</td><td align="char" char=".">0.59***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perf6</td><td align="char" char=".">0.58***</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.54***</td><td align="char" char=".">0.59***</td><td align="char" char=".">0.58***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perf7</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.45***</td><td align="char" char=".">0.52***</td><td align="char" char=".">0.52***</td><td align="char" char=".">0.49***</td><td align="char" char=".">0.60***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perf8</td><td align="char" char=".">0.53***</td><td align="char" char=".">0.51***</td><td align="char" char=".">0.57***</td><td align="char" char=".">0.57***</td><td align="char" char=".">0.52***</td><td align="char" char=".">0.63***</td><td align="char" char=".">0.57***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot1</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.09</td><td align="char" char=".">0.09</td><td align="char" char=".">0.07</td><td align="char" char=".">0.07</td><td align="char" char=".">0.09</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot2</td><td align="char" char=".">0.21***</td><td align="char" char=".">0.10</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.09</td><td align="char" char=".">0.92***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot3</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.15**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.90***</td><td align="char" char=".">0.95***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot4</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.09</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.11</td><td align="char" char=".">0.86***</td><td align="char" char=".">0.93***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot5</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.08</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.86***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.93***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot6</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.10</td><td align="char" char=".">0.14*</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.84***</td><td align="char" char=".">0.89***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.94***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mot7</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.16**</td><td align="char" char=".">0.11*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.09</td><td align="char" char=".">0.82***</td><td align="char" char=".">0.87***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.92***</td><td align="char" char=".">0.94***</td><td align="left" /><td align="left" /></tr><tr><td align="left">Mot8</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.12*</td><td align="char" char=".">0.13*</td><td align="char" char=".">0.10</td><td align="char" char=".">0.78***</td><td align="char" char=".">0.83***</td><td align="char" char=".">0.85***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.90***</td><td align="char" char=".">0.93***</td><td align="char" char=".">0.95***</td><td align="left" /></tr><tr><td align="left">Qual1</td><td align="char" char=".">0.04</td><td align="char" char=".">−0.02</td><td align="char" char=".">−0.06</td><td align="char" char=".">−0.07</td><td align="char" char=".">−0.03</td><td align="char" char=".">0.01</td><td align="char" char=".">−0.04</td><td align="char" char=".">0.00</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.00</td><td align="char" char=".">0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.06</td></tr><tr><td align="left">Qual2</td><td align="char" char=".">0.04</td><td align="char" char=".">0.01</td><td align="char" char=".">−0.02</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.01</td><td align="char" char=".">0.01</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.04</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.07</td></tr><tr><td align="left">Qual3</td><td align="char" char=".">0.05</td><td align="char" char=".">−0.02</td><td align="char" char=".">−0.02</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.00</td><td align="char" char=".">0.02</td><td align="char" char=".">0.00</td><td align="char" char=".">0.02</td><td align="char" char=".">0.04</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.07</td></tr><tr><td align="left">Qual4</td><td align="char" char=".">0.03</td><td align="char" char=".">0.02</td><td align="char" char=".">−0.01</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.05</td><td align="char" char=".">0.03</td><td align="char" char=".">0.07</td></tr><tr><td align="left">Qual5</td><td align="char" char=".">0.04</td><td align="char" char=".">0.02</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.03</td><td align="char" char=".">0.05</td><td align="char" char=".">0.06</td><td align="char" char=".">0.06</td><td align="char" char=".">0.08</td></tr><tr><td align="left">Qual6</td><td align="char" char=".">0.04</td><td align="char" char=".">0.01</td><td align="char" char=".">0.01</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">0.05</td><td align="char" char=".">0.04</td><td align="char" char=".">0.07</td></tr><tr><td align="left">Qual7</td><td align="char" char=".">0.06</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.06</td><td align="char" char=".">0.08</td><td align="char" char=".">0.07</td><td align="char" char=".">0.03</td><td align="char" char=".">0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.03</td><td align="char" char=".">0.01</td><td align="char" char=".">0.05</td></tr><tr><td align="left">Qual8</td><td align="char" char=".">0.06</td><td align="char" char=".">0.01</td><td align="char" char=".">0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.02</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.01</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.03</td></tr></tbody></table> </ephtml> </p> <ulist> <item>6 <emph>Note</emph>: Numbering is time point. Ach variable is the aggregated score (across items for motivation and across trials in a block for achievement, quantitative and qualitative effort).</item> <item>7 Abbreviations: Ach, stands for Achievement; Mot, motivation; Qual, qualitative effort.</item> <item>8 *<emph>p</emph> < .1; **<emph>p</emph> < .05; ***<emph>p</emph> < .01.</item> <item>3 TABLE Correlation matrix: Quantitative and qualitative effort.</item> </ulist> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variable</th><th align="left">Quant1</th><th align="left">Quant2</th><th align="left">Quant3</th><th align="left">Quant4</th><th align="left">Quant5</th><th align="left">Quant6</th><th align="left">Quant7</th><th align="left">Quant8</th><th align="left">Qual1</th><th align="left">Qual2</th><th align="left">Qual3</th><th align="left">Qual4</th><th align="left">Qual5</th><th align="left">Qual6</th><th align="left">Qual7</th></tr></thead><tbody valign="top"><tr><td align="left">Quant1</td></tr><tr><td align="left">Quant2</td><td align="char" char=".">0.88***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Quant3</td><td align="char" char=".">0.83***</td><td align="char" char=".">0.92***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Quant4</td><td align="char" char=".">0.79***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.93***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Quant5</td><td align="char" char=".">0.76***</td><td align="char" char=".">0.86***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.94***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Quant6</td><td align="char" char=".">0.74***</td><td align="char" char=".">0.82***</td><td align="char" char=".">0.89***</td><td align="char" char=".">0.92***</td><td align="char" char=".">0.91***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Quant7</td><td align="char" char=".">0.70***</td><td align="char" char=".">0.81***</td><td align="char" char=".">0.87***</td><td align="char" char=".">0.90***</td><td align="char" char=".">0.89***</td><td align="char" char=".">0.95***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Quant8</td><td align="char" char=".">0.69***</td><td align="char" char=".">0.78***</td><td align="char" char=".">0.85***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.94***</td><td align="char" char=".">0.94***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Qual1</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.22***</td><td align="char" char=".">0.17**</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.11</td><td align="char" char=".">0.11</td><td align="char" char=".">0.11</td><td align="char" char=".">0.11*</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Qual2</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.31***</td><td align="char" char=".">0.25***</td><td align="char" char=".">0.24***</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.18**</td><td align="char" char=".">0.90***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Qual3</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.19***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.22***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.84***</td><td align="char" char=".">0.93***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Qual4</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.28***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.21***</td><td align="char" char=".">0.24***</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.81***</td><td align="char" char=".">0.89***</td><td align="char" char=".">0.94***</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Qual5</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.23***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.79***</td><td align="char" char=".">0.86***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.98***</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Qual6</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.22***</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.28***</td><td align="char" char=".">0.77***</td><td align="char" char=".">0.84***</td><td align="char" char=".">0.88***</td><td align="char" char=".">0.95***</td><td align="char" char=".">0.96***</td><td align="left" /><td align="left" /></tr><tr><td align="left">Qual7</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.28***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.22***</td><td align="char" char=".">0.27***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.28***</td><td align="char" char=".">0.73***</td><td align="char" char=".">0.82***</td><td align="char" char=".">0.87***</td><td align="char" char=".">0.94***</td><td align="char" char=".">0.94***</td><td align="char" char=".">0.97***</td><td align="left" /></tr><tr><td align="left">Qual8</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.24***</td><td align="char" char=".">0.28***</td><td align="char" char=".">0.20***</td><td align="char" char=".">0.26***</td><td align="char" char=".">0.30***</td><td align="char" char=".">0.29***</td><td align="char" char=".">0.74***</td><td align="char" char=".">0.82***</td><td align="char" char=".">0.85***</td><td align="char" char=".">0.91***</td><td align="char" char=".">0.92***</td><td align="char" char=".">0.94***</td><td align="char" char=".">0.97***</td></tr></tbody></table> </ephtml> </p> <ulist> <item>9 <emph>Note</emph>: Quant stands for quantitative effort; Qual, for qualitative effort. Numbering is time point. Ach variable is the aggregated score (across items for motivation and across trials in a block for achievement, quantitative and qualitative effort).</item> <item>10 *<emph>p</emph> < .1; **<emph>p</emph> < .05; ***<emph>p</emph> < .01.</item> </ulist> <p>The motivation, achievement and quantitative effort variables were scaled to have an M of 0 and <emph>SD</emph> of 1 after scaling. The qualitative effort score represents the percentage of the trials where participants opted for higher‐quality effort (i.e., viewing sentences and word meaning) rather than lower‐quality effort (i.e., viewing only the meaning of the word). This percentage was calculated by dividing the total number of trials within a specific block by the number of trials where higher‐quality effort was chosen.</p> <p>In terms of normality, achievement variables were negatively skewed, which was somewhat remedied when using reverse and log transformation. Quantitative effort variables were positively skewed, but log transformation did not resolve the issue. Using robust estimators is considered a superior approach to transformation (Curran & Bauer, [<reflink idref="bib12" id="ref127">12</reflink>]). In line with the Stage 1 report, robust weighted least squares estimator was attempted (estimator = 'WLSM'); however, we belatedly discovered that WLS was only appropriate for categorical data. Therefore, maximum likelihood with robust standard errors was used for all the models discussed below. Unless otherwise indicated, equality constraints on auto‐regressive and cross‐lagged coefficients were imposed (i.e., constraining the same type of pathways to be equal across waves) to simplify the model and facilitate convergence (Orth et al., [<reflink idref="bib60" id="ref128">60</reflink>]).</p> <hd id="AN0185103687-52">1 Hypothesis</hd> <p> <emph>The cyclical loop of motivation and achievement</emph>.</p> <p>To test whether there was a reciprocal relationship between motivation and achievement, a series of bivariate RI‐CLPMs were fit (see Figure 5). In Table 4, we report the details of goodness‐of‐fit statistics for each of the four competing models and the model comparisons. Generally, four RI‐CLPMs for motivation and achievement all had reasonable fit (RMSEA <0.05 and SRMR = 0.05; CFI >0.90).</p> <p>4 TABLE Goodness‐of‐fit statistics for bivariate RI‐CLPMs to test for reciprocal relationship between motivation and achievement.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Models</th><th align="left">Goodness‐of‐fit indices</th><th align="left">Model comparison</th><th align="left"><p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0001" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mrow><mi>p</mi><msub><mi mathvariant="normal">Δ</mi><msup><mi>χ</mi><mn>2</mn></msup></msub></mrow></semantics></math></p></th></tr><tr><th align="left"><italic>Df</italic></th><th align="left">RMSEA</th><th align="left">SRMR</th><th align="left">CFI</th><th align="left">AIC</th><th align="left">BIC</th></tr></thead><tbody valign="top"><tr><td align="left">RI‐CLPM</td></tr><tr><td align="left">Model 1 (fully constrained)</td><td align="char" char=".">287.00</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.98</td><td align="char" char=".">14731.28</td><td align="char" char=".">14869.41</td><td align="left">1 vs. 3</td><td align="char" char=".">.007*</td></tr><tr><td align="left">Model 2a: Motivation <p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0002" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mo>→</mo></semantics></math></p> achievement = 0</td><td align="char" char=".">286.00</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.98</td><td align="char" char=".">14723.32</td><td align="char" char=".">14865.18</td><td align="left">2a. vs. 3</td><td align="char" char=".">.239</td></tr><tr><td align="left">Model 2b: Achievement <p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0003" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mo>→</mo></semantics></math></p> motivation = 0</td><td align="char" char=".">286.00</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.98</td><td align="char" char=".">14733.15</td><td align="char" char=".">14875.02</td><td align="left">2b vs. 3</td><td align="char" char=".">.002*</td></tr><tr><td align="left">Model 3 (freely estimated)</td><td align="char" char=".">285.00</td><td align="char" char=".">0.04</td><td align="char" char=".">0.05</td><td align="char" char=".">0.98</td><td align="char" char=".">14723.76</td><td align="char" char=".">14869.36</td><td align="left">–</td><td align="left" /></tr></tbody></table> </ephtml> </p> <p>11 <emph>Note</emph>: *Statistically significant. RI‐CLPM is random intercept cross‐lagged panel model.</p> <p>Pairwise model comparisons using <emph>χ</emph><sups>2</sups>‐difference tests showed that Model 1 (where both pathways were fixed to 0, i.e., the fully constrained model) had significantly worse fit than Model 3 (where both pathways were freely estimated, i.e., the 'free' model), suggesting that at least one pathway should be modelled. Model 2b (unilateral model, where the achievement → motivation path was fixed to 0) also fit significantly worse than Model 3, suggesting that the achievement → motivation pathway should be freely estimated. However, Model 2a (unilateral model, where the motivation → achievement pathway was fixed to 0) was not significantly different from Model 3, suggesting that the model can be reduced by fixing the motivation → achievement pathway to 0. Thus, only the unilateral effect from achievement to motivation was supported. We therefore report the associated parameter estimates of this Model 2a: Significant auto‐regressive coefficients were found for both motivation (<emph>β</emph> = .618, <emph>SE</emph> = .051, <emph>p</emph> < .001) and achievement (<emph>β</emph> = .091, <emph>SE</emph> = .033, <emph>p</emph> = .006), showing temporal stability of these variables. The cross‐lagged parameter from achievement → motivation was positive and significant (<emph>β</emph> = .030, <emph>SE</emph> = .010, <emph>p</emph> = .004). The correlation between the random intercepts (i.e., the covariance between motivation and achievement at time point 1) was significant (<emph>β</emph> = .127, =.045, <emph>p</emph> = .005). The correlated change after considering the cross‐lagged pathways was non‐significant (<emph>β</emph> = −.003, <emph>p</emph> = .478). Figure 7 is an illustration of Model 2a with the estimated pathways.</p> <hd id="AN0185103687-53">2 Hypothesis</hd> <p> <emph>The role of effort as mediator in the relationship between motivation and achievement</emph>.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0007.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0007.jpg" title="7 Illustration of the final bivariate RI‐CLPM model (Model 2a) and its parameter estimates. Parameter estimates are shown in grey, borderless boxes, with an asterisk (*) indicating statistical significance. In brackets are standard errors. As equality constraints were imposed across all waves, only two of the eight timewaves are displayed for simplicity and clarity. All other notations follow the convention of Figure 4, with one exception: Motivation 1 is regressed on Achievement 1 instead of Achievement 2, as motivation was measured at the beginning of Block 1 and achievement at the end of the same block and so on. The cross‐lagged relationships were constructed exactly as planned; the only difference is the numbering of the time waves due to the nature of the block design of the experiment." /> </p> <p></p> <p>To test whether effort mediated the behavioural pathway from motivation to achievement, we examine a series of trivariate RI‐CLPMs (see Figure 4). We modelled quantitative effort separately from qualitative effort. Mediation analysis literature (Rucker et al., [<reflink idref="bib68" id="ref129">68</reflink>]) suggests that having failed to establish the total effect (of motivation on achievement) should not deter testing for the mediation effect (of effort).</p> <p>In Table 5, we report the details of goodness‐of‐fit statistics for each of the six competing models. Generally, all six trivariate RI‐CLPM models had good fit (<emph>p</emph>‐value of <emph>χ</emph><sups>2</sups> < .001; RMSEA and SRMR <.05; TLI and CFI >.90). Model comparisons showed that Model 4 (fully constrained) fitted worse than Model 8 (where all cross‐lagged parameters were freely estimated), suggesting that at least one pathway needed to be estimated. Model 5 (where the motivation → effort pathway was fixed to 0) was not significantly different from Model 8, suggesting that the model could be reduced by fixing the motivation → effort pathway to 0. Model 6 (where the effort → achievement pathway was constrained to 0) was not significantly different from Model 8, and thus the effort → achievement pathway could also be fixed to 0. However, Model 7 (where the achievement → motivation pathway was fixed to 0) was significantly worse than Model 8 and thus, the achievement → motivation pathway should be freely estimated.</p> <p>5 TABLE Goodness‐of‐fit statistics for trivariate RI‐CLPMs testing the mediating role of effort in the reciprocal relationship between motivation and achievement.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Models</th><th align="left">Goodness‐of‐fit indices</th><th align="left">Model comparison</th><th align="left"><p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0004" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mrow><mi>p</mi><msub><mi mathvariant="normal">Δ</mi><msup><mi>χ</mi><mn>2</mn></msup></msub></mrow></semantics></math></p></th></tr><tr><th align="left"><italic>Df</italic></th><th align="left">RMSEA</th><th align="left">SRMR</th><th align="left">CFI</th><th align="left">AIC</th><th align="left">BIC</th></tr></thead><tbody valign="top"><tr><td align="left">RI‐CLPM</td></tr><tr><td align="left">Model 4 (fully constrained)</td><td align="left">505.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.97</td><td align="left">17368.59</td><td align="left">17573.92</td><td align="left">4 vs. 8</td><td align="left">.004*</td></tr><tr><td align="left">Model 5: motivation <p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0005" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mo>→</mo></semantics></math></p> effort = 0</td><td align="left">503.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17360.95</td><td align="left">17573.75</td><td align="left">5 vs. 8</td><td align="left">.094</td></tr><tr><td align="left">Model 6: effort <p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0006" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mo>→</mo></semantics></math></p> achievement = 0</td><td align="left">503.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17360.43</td><td align="left">17573.23</td><td align="left">6 vs. 8</td><td align="left">.220</td></tr><tr><td align="left">Model 7: achievement <p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0007" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mo>→</mo></semantics></math></p> motivation = 0</td><td align="left">503.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17368.60</td><td align="left">17581.40</td><td align="left">7 vs. 8</td><td align="left">.004*</td></tr><tr><td align="left">Model 8 (freely estimated)</td><td align="left">502.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17360.67</td><td align="left">17577.20</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Model 9a</td><td align="left">504.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17361.13</td><td align="left">17570.20</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Model 9b</td><td align="left">503.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17360.79</td><td align="left">17573.59</td><td align="left">9b vs. 9a</td><td align="left">.134</td></tr><tr><td align="left">Model 10</td><td align="left">500.00</td><td align="left">0.04</td><td align="left">0.05</td><td align="left">0.98</td><td align="left">17356.94</td><td align="left">17580.9</td><td align="left">10 vs. 9b</td><td align="left">.019*</td></tr><tr><td align="left">Model 10</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">10 vs. 9a</td><td align="left">.016*</td></tr></tbody></table> </ephtml> </p> <p>12 <emph>Note</emph>: *Statistically significant. RI‐CLPM is random intercept cross‐lagged panel model. Model 9a is the model where motivation <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0008" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>→</mo></mrow></semantics></math> </ephtml> effort and effort <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0009" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>→</mo></mrow></semantics></math> </ephtml> achievement are fixed to 0 and achievement <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0010" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>→</mo></mrow></semantics></math> </ephtml> motivation is freely estimated. Model 9b is Model 9a plus allowing the direct path from motivation to achievement cross‐lagged parameter to be freely estimated. Model 10 is Model 9b plus the achievement <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0011" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>→</mo></mrow></semantics></math> </ephtml> effort direct link.</p> <p>Following the plan outlined in Stage 1 report, we proceeded to construct Model 9a based on the results of the model comparisons above. In Model 9a, the motivation → effort and effort → achievement links were fixed to 0 while the achievement → motivation pathway was freely estimated. Parameter estimates of Model 9a indicated significant auto‐regressive coefficients for motivation (<emph>β</emph> = .616, <emph>SE</emph> = .050, <emph>p</emph> < .001), achievement (<emph>β</emph> = .104, <emph>SE</emph> = .033, <emph>p</emph> = .002) and quantitative effort (<emph>β</emph> = .484, <emph>SE</emph> = .049, <emph>p</emph> < .001), demonstrating the temporal stability of these variables. The cross‐lagged parameter of the achievement → motivation pathway was positive and significant (<emph>β</emph> = .029, <emph>SE</emph> = .011, <emph>p</emph> = .005). The correlation between the random intercepts of achievement and effort was 0.162 (<emph>SE</emph> = .040, <emph>p</emph> < .001), between motivation and effort was 0.014 (<emph>SE</emph> = .051, <emph>p</emph> = .793) and between achievement and motivation was 0.127 (<emph>SE</emph> = .045, <emph>p</emph> = .005).</p> <p>Next, we compared Model 9a to Model 9b, which was essentially the same as Model 9a, with the addition of a direct motivation → achievement path. Model 9a and 9b did not significantly differ from each other (<emph>χ</emph><sups>2</sups> (<reflink idref="bib1" id="ref130">1</reflink>) = 2.241, <emph>p</emph> = .134), suggesting that the direct motivation → achievement path could be constrained to 0. Parameter estimates of Model 9b also indicated that the direct motivation → achievement path was non‐significant (<emph>β</emph> = .083, <emph>p</emph> = .139). Therefore, Model 9a remained the best model. Figure 8 is an illustration of Model 9a with the estimated pathways.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0008.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0008.jpg" title="8 Illustration of the final trivariate RI‐CLPM model (Model 9a) and its parameter estimates. All notations follow the convention of Figures 4 and 7." /> </p> <p></p> <p>It was not possible to construct a similar series of models for qualitative effort because the variable had a U‐shaped distribution. This was because most participants consistently never chose sentences while some participants almost always chose sentences throughout the experiment. Consequently, we were unable to carry out this specific pre‐registered analysis due to the nature of the data.</p> <p>Furthermore, we also exploratorily constructed Model 10 which was Model 9b plus the achievement → effort direct link to test the possibility that achievement of a previous time point might have directly influenced the exertion of effort at a next time point. This direct link turned out to be non‐significant (see Data S1).</p> <hd id="AN0185103687-56">3a Hypothesis</hd> <p> <emph>The causality of the relations among motivation, effort and achievement</emph>.</p> <p>Halfway through the experiment, we introduced a manipulation of perceived achievement. The manipulation was expected to have an impact on the cycle rather than on individual constructs. This means that even though the raw scores of the variables could change (see Table S1), the cross‐lagged parameters should not change between the rounds. Note that participants experienced achievement setbacks at different moments in the task. One‐third of the participants had three above‐average blocks and five below‐average blocks (the 'Early' group). Another third had four above‐average blocks and four below‐average blocks (the 'Middle' group). The last third had five above‐average blocks and three below‐average blocks (the 'Late' group). This 'jitter' in the experimental set‐up was taken into account in the modelling.</p> <p>Specifically, we first constructed Model 11 which was a multi‐group RI‐CLPM with equality constraints both across the groups (all three groups 'Early', 'Middle' and 'Late' have the same cross‐lagged parameter) and across the rounds (i.e., both round 1 and round 2 have the same cross‐lagged parameter). Model 12 closely resembled Model 11, differing only in that it maintained the equality constraint across the three groups of participants while allowing the cross‐lagged parameters for rounds 1 and 2 to be estimated freely and separately. Similarly, Model 13 was almost identical to Model 11, apart from imposing the equality constraint across the rounds while allowing the cross‐lagged parameters for the three groups to be estimated freely and separately. In Model 14, which was also multi‐group RI‐CLPM, the cross‐lagged parameters were freely estimated both across the groups of participants and across the one to eight time points.</p> <p>The model comparison results (see Table 6) supported Hypothesis 3a. The fully constrained model (Model 11) did not significantly differ from the freely estimated model (Model 14), suggesting that at least one constraint needs to be in place. Model with the group equality constraint (Model 12) did not significantly differ from the free model either, similarly suggesting that the group equality constraint needed to be kept. Model with the round equality constraint (Model 13) did not significantly differ from the free model either, which meant that the round equality constraint could also be imposed.</p> <p>6 TABLE Goodness‐of‐fit statistics for bivariate RI‐CLPMs testing the mediating role of effort in the reciprocal relationship between motivation and achievement.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Models</th><th align="left">Goodness‐of‐fit indices</th><th align="left">Model comparison</th><th align="left"><italic>p</italic>∆<sub><italic>χ</italic></sub>2</th></tr><tr><th align="left">df</th><th align="left">RMSEA</th><th align="left">SRMR</th><th align="left">CFI</th><th align="left">AIC</th><th align="left">BIC</th></tr></thead><tbody valign="top"><tr><td align="left">RI‐CLPM</td></tr><tr><td align="left">Model 14 freely estimated</td><td align="center">837</td><td align="center">0.14</td><td align="center">1.91</td><td align="center">0.73</td><td align="left">14087.16</td><td align="center">14591.16</td><td align="center">11 vs. 14</td><td align="center">0.344</td></tr><tr><td align="left">Model 13</td><td align="center">909</td><td align="center">0.13</td><td align="center">1.91</td><td align="center">0.74</td><td align="left">14064.7</td><td align="left">14299.9</td><td align="center">13 vs. 14</td><td align="center">0.223</td></tr><tr><td align="left">Model 12</td><td align="center">913</td><td align="center">0.13</td><td align="center">1.91</td><td align="center">0.74</td><td align="center">14061.44</td><td align="center">14281.71</td><td align="center">12 vs. 14</td><td align="center">0.236</td></tr><tr><td align="left">Model 11 fully constrained</td><td align="center">917</td><td align="center">0.13</td><td align="center">1.91</td><td align="center">0.74</td><td align="center">14057.66</td><td align="center">14262.99</td><td align="center">–</td><td align="center">–</td></tr></tbody></table> </ephtml> </p> <p>13 <emph>Note</emph>: RI‐CLPM is random intercept cross‐lagged panel model. Model 11 which was a multi‐group RI‐CLPM with equality constraints both across the groups (all three groups "Early", "Middle", and "Late" have the same cross‐ lagged parameter) and across the rounds (i.e. both round 1 and round 2 have the same cross‐lagged parameter). Model 12 was similar to Model 11; the only difference was that the equality constraint across the three groups of participants was retained but the cross‐lagged parameters for round 1 and round 2 were freely and separately estimated. Model 13 was also similar to Model 11; the only difference was that the equality constraint across the rounds was imposed but the cross‐lagged parameters for three groups were freely and separately estimated. In Model 14, which was also multi‐group RI‐CLPM, the cross‐lagged parameters were freely estimated both across the groups of participants and across the 1–8 time points.</p> <p>We therefore report the estimates from the fully constrained model (Model 11). Significant auto‐regressive coefficients were found for both motivation (<emph>β</emph> = .618, <emph>p</emph> < .001) and achievement (<emph>β</emph> = .087, <emph>p</emph> = .009), showing temporal stability of these variables. In terms of cross‐lagged parameters, we only found significant achievement → motivation pathway (<emph>β</emph> = .034, <emph>p</emph> = .002). The motivation → achievement was non‐significant (<emph>β</emph> = .122, <emph>p</emph> = .190). This indicates that the cross‐lagged effect of achievement on motivation was identified and remained consistent across the rounds, offering partial support for Hypothesis 3a. Furthermore, the rankings (manipulated achievement) did not have a significant direct influence on motivation (<emph>β</emph> = −.004, <emph>p</emph> = .530).</p> <p>Additionally, an unplanned series of stepwise multi‐level models were fitted to predict motivation, achievement and quantitative effort to gain more understanding of the temporal dynamics of these constructs (for details, see Data S1). The results of these models suggest that there were non‐trivial individual differences in motivation, achievement and quantitative effort at the initial time point and in the degree to which motivation, achievement and quantitative effort waned over time. Specifically for qualitative effort, there was a significant and negative effect of the manipulation: The probability of using higher‐quality effort decreased after participants were told that their rankings had dropped to below average.</p> <hd id="AN0185103687-57">3b Hypothesis</hd> <p> <emph>The role of TOI in moderating the effect of the manipulation on motivation, effort and achievement</emph>.</p> <p>To test the role of TOI, a series of multi‐level models were fit, in which motivation, achievement and quantitative effort were each predicted by the time points, the manipulation (the <emph>before</emph> vs. <emph>after manipulation</emph> factor), the TOI score (either general or specific TOI) and the interaction between the manipulation and the according TOI score. Random intercepts are allowed to account for the intercept differences between individual participants. The <emph>before</emph> versus <emph>after manipulation</emph> factor was coded as 0 for time points before the manipulation and 1 for those after. Given the conceptual differences between general and specific TOI scores, separate models (one with general TOI score and the other with specific TOI score) were constructed. The results (see Table 7a for the full parameter estimates) provided no support for the hypothesis. Contrary to the expectation, general TOI did not moderate the effect of the manipulation on quantitative effort. Similarly, specific TOI did not moderate the effect of manipulation on quantitative effort, motivation or achievement.</p> <p>7A TABLE Goodness‐of‐fit statistics and parameter estimates from multilevel models testing the moderating effects of theory of intelligence on quantitative effort.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Dependent variable</th></tr><tr><th align="left">Motivation</th><th align="left">Achievement</th><th align="left">Effort</th><th align="left">Motivation</th><th align="left">Achievement</th><th align="left">Effort</th></tr></thead><tbody valign="top"><tr><td align="left">Time point</td><td align="left">−0.939<sup>***</sup></td><td align="center">−0.236</td><td align="center">−0.082<sup>***</sup></td><td align="center">−0.938<sup>***</sup></td><td align="center">−0.233</td><td align="center">−0.082<sup>***</sup></td></tr><tr><td align="left">−0.112</td><td align="center">−0.148</td><td align="center">−0.007</td><td align="center">−0.112</td><td align="center">−0.148</td><td align="center">−0.007</td></tr><tr><td align="left">Manipulation</td><td align="left">−3.934<sup>**</sup></td><td align="center">0.094</td><td align="center">0.181</td><td align="center">−1.118</td><td align="center">1.884</td><td align="center">0.082</td></tr><tr><td align="left">−1.85</td><td align="center">−2.452</td><td align="center">−0.112</td><td align="center">−2.037</td><td align="center">−2.698</td><td align="center">−0.123</td></tr><tr><td align="left">TOI general</td><td align="left">1.98</td><td align="center">−0.790</td><td align="center">−0.0001</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">−1.65</td><td align="center">−0.853</td><td align="center">−0.083</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Manipulation × TOI general</td><td align="left">0.711<sup>*</sup></td><td align="center">−0.079</td><td align="center">−0.013</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">−0.412</td><td align="center">−0.546</td><td align="center">−0.025</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">TOI specific</td><td align="left" /><td align="left" /><td align="left" /><td align="center">4.051<sup>**</sup></td><td align="center">1.472</td><td align="center">−0.099</td></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="center">−1.75</td><td align="center">−0.907</td><td align="center">−0.088</td></tr><tr><td align="left">Manipulation × TOI specific</td><td align="left" /><td align="left" /><td align="left" /><td align="center">0.052</td><td align="center">−0.484</td><td align="center">0.01</td></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="center">−0.444</td><td align="center">−0.588</td><td align="center">−0.027</td></tr><tr><td align="left">Constant</td><td align="left">35.927<sup>***</sup></td><td align="center">87.984<sup>***</sup></td><td align="center">1.643<sup>***</sup></td><td align="center">26.517<sup>***</sup></td><td align="center">78.095<sup>***</sup></td><td align="center">2.081<sup>***</sup></td></tr><tr><td align="left">−7.189</td><td align="center">−3.74</td><td align="center">−0.36</td><td align="center">−7.836</td><td align="center">−4.082</td><td align="center">−0.394</td></tr><tr><td align="left">Observations</td><td align="left">2472</td><td align="center">2472</td><td align="center">2472</td><td align="center">2472</td><td align="center">2472</td><td align="center">2472</td></tr><tr><td align="left">Log likelihood</td><td align="left">−9038.231</td><td align="center">−9442.743</td><td align="center">−2059.950</td><td align="center">−9037.799</td><td align="center">−9441.682</td><td align="center">−2059.291</td></tr><tr><td align="left">Akaike Inf. Crit.</td><td align="left">18,090.46</td><td align="center">18,899.49</td><td align="center">4133.90</td><td align="center">18,089.60</td><td align="center">18,897.36</td><td align="center">4132.58</td></tr><tr><td align="left">Bayesian Inf. Crit.</td><td align="left">18,131.14</td><td align="center">18,940.16</td><td align="center">4174.58</td><td align="center">18,130.27</td><td align="center">18,938.04</td><td align="center">4173.26</td></tr></tbody></table> </ephtml> </p> <p>14 <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-1001" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mtext mathvariant="script">Note</mtext><mo>:</mo></math> </ephtml><sups>*</sups><emph>p</emph> < 0.1; <sups>**</sups><emph>p</emph> < 0.05; <sups>***</sups><emph>p</emph> < 0.01. TOI is theory of intelligence. Effort is quantitative effort (i.e. the average time that participants spent actively viewing the learning materials).</p> <p>The same analysis was conducted for qualitative effort (see Table 7b), and similarly, neither general nor specific TOI moderated the effect of the manipulation on quantitative effort, motivation and achievement. Other motivation variables that were measured in the pre‐task (goal orientations, EB and ASC) were intended to be exploratory analyses (as stated in Stage 1 report) and therefore, the results concerning these variables can be found in the Data S1.</p> <p>7B TABLE Goodness‐of‐fit statistics and parameter estimates from multilevel models testing the moderating effects of the‐ ory of intelligence on qualitative effort.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Dependent variable</th></tr><tr><th align="left">Qualitative effort</th></tr></thead><tbody valign="top"><tr><td align="left">Time point</td><td align="left">−0.051 (0.200)</td><td align="left">−0.052 (0.200)</td></tr><tr><td align="left">Manipulation</td><td align="left">2.399 (3.315)</td><td align="left">−4.338 (3.647)</td></tr><tr><td align="left">TOI general</td><td align="left">−1.013 (2.722)</td><td align="left" /></tr><tr><td align="left">Manipulation × TOI general</td><td align="left">−0.327 (0.739)</td><td align="left" /></tr><tr><td align="left">TOI specific</td><td align="left">−2.624 (2.901)</td></tr><tr><td align="left">Manipulation × TOI specific</td><td align="left">1.205 (0.794)</td></tr><tr><td align="left">Constant</td><td align="left">25.163** (11.862)</td><td align="left">32.407** (12.988)</td></tr><tr><td align="left">Observations</td><td align="left">2472</td><td align="left">2472</td></tr><tr><td align="left">Log likelihood</td><td align="left">−10,452.330</td><td align="left">−10,450.930</td></tr><tr><td align="left">Akaike Inf. Crit.</td><td align="left">20,918.65</td><td align="left">20,915.87</td></tr><tr><td align="left">Bayesian Inf. Crit.</td><td align="left">20,959.33</td><td align="left">20,956.54</td></tr></tbody></table> </ephtml> </p> <p>15 <emph>Note</emph>: **<emph>p</emph> < 0.05. TOI is theory of intelligence. Effort is qualitative effort (or percentage of the time participants chose higher quality effort per block).</p> <hd id="AN0185103687-58">Multi‐level model testing the pathway from motivation to effort choice, taking into account i...</hd> <p>This is a deviation from the Stage 1 report because this analysis turned out infeasible. To construct the proposed models, we would need to account for the random effects of items; these models either did not converge or converged with singularity. Secondly, given that each participant had a personalized subset of items, we do not see how to interpret such models. In sum, as stated in Stage 1 report, since each participant worked with their unique stimuli set where each item has been judged by the participant him/herself to be completely unknown (and therefore possibly of relative difficulty), it is still reasonable to assume item‐specific difficulty, if any, to have no systematic impact on the dynamics between the variables of interests.</p> <hd id="AN0185103687-59">Exploratory analyses</hd> <p></p> <hd id="AN0185103687-60">CLPM versus RI‐CLPMs</hd> <p>Traditionally, cross‐lagged interactions between variables have been analysed with cross‐lagged panel models (CLPM), which have in recent years been criticized for conflating estimates of intra‐ and interindividual differences (Hamaker et al., [<reflink idref="bib31" id="ref131">31</reflink>]; Núñez‐Regueiro et al., [<reflink idref="bib58" id="ref132">58</reflink>]). In this context, RI‐CLPM has been argued to be a better modelling option than CLPM. To test robustness across different modelling strategies (RI‐CLPM vs. CLPM), we also fit a parallel freely estimated CLPM to test Hypothesis 1. CLPM estimates suggested the existence of reciprocity between motivation and achievement that was not seen with RI‐CLPM. The cross‐lagged parameters in the CLPM were significant for both achievement → motivation (<emph>β</emph> = .02, <emph>p</emph> = .001) and motivation → achievement (<emph>β</emph> = .05, <emph>p</emph> = .043). Full parameter estimates and indices of model fits of both modelling options are shown in Table 8.</p> <p>8 TABLE Freely estimated bivariate CLPM versus freely estimated bivariate RI‐CLPM to test the reciprocity between motivation and achievement.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">CLPM</th><th align="left">RI‐CLPM</th></tr><tr><th align="left">Estimate</th><th align="left"><italic>S.E</italic>.</th><th align="left"><italic>p</italic>‐value</th><th align="left">Estimate</th><th align="left"><italic>S.E</italic>.</th><th align="left"><italic>p</italic>‐value</th></tr><tr><th align="left">Regressive and cross‐lagged parameters</th></tr></thead><tbody valign="top"><tr><td align="left">FFm</td></tr><tr><td align="left">FFm</td><td align="left">0.95</td><td align="char" char=".">.01</td><td align="char" char=".">.000</td><td align="left">0.62</td><td align="char" char=".">.05</td><td align="char" char=".">.000</td></tr><tr><td align="left">FFa</td><td align="left">0.02</td><td align="char" char=".">.01</td><td align="char" char=".">.000</td><td align="left">0.03</td><td align="char" char=".">.01</td><td align="char" char=".">.002</td></tr><tr><td align="left">FFa</td></tr><tr><td align="left">FFa</td><td align="left">0.57</td><td align="char" char=".">.03</td><td align="char" char=".">.000</td><td align="left">0.09</td><td align="char" char=".">.03</td><td align="char" char=".">.009</td></tr><tr><td align="left">FFm</td><td align="left">0.05</td><td align="char" char=".">.02</td><td align="char" char=".">.043</td><td align="left">0.12</td><td align="char" char=".">.09</td><td align="char" char=".">.190</td></tr><tr><td align="left">Fit Indices</td></tr><tr><td align="left"><p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0013" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mi>χ</mi>2<mi>df</mi></semantics></math></p></td><td align="left">867.88</td><td align="left" /><td align="left" /><td align="left">460.25</td><td align="left" /><td align="left" /></tr><tr><td align="left">df</td><td align="left">288.00</td><td align="left" /><td align="left" /><td align="left">285.00</td><td align="left" /><td align="left" /></tr><tr><td align="left">RMSEA</td><td align="left">0.08</td><td align="left" /><td align="left" /><td align="left">0.04</td><td align="left" /><td align="left" /></tr><tr><td align="left">SRMR</td><td align="left">0.10</td><td align="left" /><td align="left" /><td align="left">0.05</td><td align="left" /><td align="left" /></tr><tr><td align="left">CFI</td><td align="left">0.91</td><td align="left" /><td align="left" /><td align="left">0.97</td><td align="left" /><td align="left" /></tr><tr><td align="left">AIC</td><td align="left">15125.39</td><td align="left" /><td align="left" /><td align="left">14723.76</td><td align="left" /><td align="left" /></tr><tr><td align="left">BIC</td><td align="left">15259.79</td><td align="left" /><td align="left" /><td align="left">14869.36</td><td align="left" /><td align="left" /></tr><tr><td align="left">p‐value</td><td align="left">.00</td><td align="left" /><td align="left" /><td align="left">.00</td><td align="left" /><td align="left" /></tr><tr><td align="left">Scaled <p><math altimg="urn:x-wiley:00070998:media:bjep12731:bjep12731-math-0014" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mi>χ</mi>2<mi>df</mi></semantics></math></p></td><td align="left">765.32 (288)</td><td align="left" /><td align="char" char=".">.000</td><td align="left">406.60 (285)</td><td align="left" /><td align="char" char=".">.000</td></tr></tbody></table> </ephtml> </p> <ulist> <item>16 <emph>Note</emph>: CLPM stands for cross‐lagged random panel model.</item> <item>17 Abbreviations: FFa, latent variable of achievement; FFm, latent variable of motivation; RI‐CLPM, random intercept cross‐lagged random panel model.</item> </ulist> <hd id="AN0185103687-61">DISCUSSION</hd> <p>The current study is a short‐timeframe experiment to investigate (a) the reciprocity between motivation and academic achievement, (b) the potential role of qualitative and quantitative effort as a mediator in this reciprocity, (c) whether using an experimental manipulation would provide evidence for a causal interpretation of the reciprocity and (d) whether students' growth mindset mitigates decreases in motivation, effort and achievement following academic setbacks. In this 1‐hour experiment, university students who were non‐native English speakers learned new English words by answering multiple‐choice questions. Each time they made a mistake, they had the opportunity to review the learning materials before being tested again. The rationale for the study was based on the theoretical model by Vu et al. ([<reflink idref="bib86" id="ref133">86</reflink>]), which was an attempt to integrate multiple theories of academic motivation (see Figure 9). This model outlines how motivation and achievement can be reciprocally related and how this relationship is mediated by the quantity and quality of learning behaviours (referred to as 'effort' in the present research).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/01jun25/bjep12731-fig-0009.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12731-fig-0009.jpg" title="9 The Motivation–Achievement Cycle: A Summary Model of Motivation–Achievement Interactions. The model captures some of the commonalities within prominent theories of academic motivation. Blue boxes denote motivation constructs, green (dotted) arrows behavioural intermediaries (quality of learning and quantity of learning) and yellow boxes and arrows denote achievement‐related constructs (e.g., perceived performance). Figure reproduced from Vu et al. ([86]) with the authors' permission." /> </p> <p></p> <p>First, our findings provided little support for the existence of reciprocity between motivation and academic achievement. We only found a unilateral effect of achievement on reported motivation at a subsequent time point. The motivation → achievement link was not found, suggesting that an increase in motivation may not lead to subsequent increased achievement. Second, we found no evidence that effort, measured as the amount of time participants actively engaged with the learning materials, mediated the relationship between motivation and academic achievement. Specifically, higher prior motivation was not significantly linked to more effort, nor did increased effort lead to higher achievement. Third, using an experimental design with multiple time points and experimental manipulation, we found partial support for a causal interpretation of the relationship between achievement and motivation – specifically, the unilateral achievement → motivation pathway. When participants experienced a setback in their perceived achievement (i.e., a drop in rank relative to simulated other participants), the unilateral relationship between achievement and motivation continued to manifest. Finally, there was no evidence for the moderating effect of mindset on quantitative effort, motivation and achievement. Contrary to our expectation, having a higher (i.e., more growth) mindset score was, in our data, not associated with a smaller decrease in effort after experiencing a setback. Below, we delve into each of the findings considering both the methodological and theoretical perspectives.</p> <hd id="AN0185103687-63">Motivation and achievement</hd> <p>The finding of a unilateral effect of achievement on motivation, and not <emph>vice versa</emph>, aligns with previous results. Studies have consistently found reciprocal effects between motivation and achievement only when motivation was operationalized as ASC but not when motivation is operationalized as different constructs (Huang, [<reflink idref="bib35" id="ref134">35</reflink>]; Valentine & DuBois, [<reflink idref="bib82" id="ref135">82</reflink>]; Vu et al., [<reflink idref="bib87" id="ref136">87</reflink>]; Wu et al., [<reflink idref="bib92" id="ref137">92</reflink>]). Among the studies that were meta‐analysed by Vu et al. ([<reflink idref="bib87" id="ref138">87</reflink>]), five studies tapped into intrinsic motivation (see Table 1). These are, namely, Guo et al. ([<reflink idref="bib30" id="ref139">30</reflink>]), Hebbecker et al. ([<reflink idref="bib34" id="ref140">34</reflink>]), Liu and Hou ([<reflink idref="bib42" id="ref141">42</reflink>]), Schiefele et al. ([<reflink idref="bib71" id="ref142">71</reflink>]) and Weidinger et al. ([<reflink idref="bib89" id="ref143">89</reflink>]). These studies yielded effect sizes that are close to 0 for the motivation → achievement pathway. In the current research, we measured motivation with three questions tapping into intrinsic motivation (e.g., 'I try to answer hard questions because it's fun to answer hard questions'). Our finding of non‐significant motivation → achievement pathway therefore dovetails with the findings of Guo et al. ([<reflink idref="bib30" id="ref144">30</reflink>]), Hebbecker et al. ([<reflink idref="bib34" id="ref145">34</reflink>]), Liu and Hou ([<reflink idref="bib42" id="ref146">42</reflink>]), Schiefele et al. ([<reflink idref="bib71" id="ref147">71</reflink>]) and Weidinger et al. ([<reflink idref="bib89" id="ref148">89</reflink>]). Together these findings suggest that when learners experience progress in their learning, this boosts intrinsic motivation, but the effects of intrinsic motivation on achievement are much harder to find (Vu et al., [<reflink idref="bib87" id="ref149">87</reflink>]).</p> <p>Surprisingly, when manipulating perceived achievement, however, we did not observe an effect on motivation. Two explanations for this finding can be offered. First, Vu et al. ([<reflink idref="bib86" id="ref150">86</reflink>]) suggested that rather than perceived achievement, it is the experience of flow accompanying successful performance that could fuel intrinsic motivation. It is also possible that it is a timescale problem (Kuiper & Ryan, [<reflink idref="bib40" id="ref151">40</reflink>]; Marsh et al., [<reflink idref="bib45" id="ref152">45</reflink>]). Participants were informed at every trial whether their answer was correct, which may be seen as trial‐by‐trial feedback. This continuous, instantaneous feedback may have had a stronger effect on intrinsic motivation than the delayed block‐by‐block comparative feedback that was manipulated in this experiment. Further studies are needed to test these alternative explanations.</p> <p>Next, contrary to the expectation, an increase in intrinsic motivation did not necessarily bring about further achievement at the next time point. There are multiple alternative explanations for this null finding. First, the effect of motivation on achievement is possibly restricted to motivation constructs that are self‐beliefs, which is in line with the findings of a recent meta‐analysis on the reciprocity between motivation and achievement (Vu et al., [<reflink idref="bib87" id="ref153">87</reflink>]). In that case, we might have not measured the motivation construct that fuelled the effort that was in turn translated into learning achievement. Recall that we asked participants about their intrinsic motivation, whereas previous research predominantly operationalized motivation as ASC. Vu et al. ([<reflink idref="bib87" id="ref154">87</reflink>]) showed that the effect of motivation on achievement, if any, was solely found in studies that employed self‐belief type of motivation constructs. When they pooled the effects of studies that utilized motivation constructs such as achievement goal, intrinsic motivation and interest, then the motivation → achievement association was negligent. However, the question is also whether self‐belief is the 'right' type of motivation construct, given that there is stark conceptual overlap between self‐belief motivation constructs and perceived academic achievement, which could result in spurious self‐belief → achievement association, as Vu et al. ([<reflink idref="bib87" id="ref155">87</reflink>]) argued. Second, we cannot exclude the possibility that motivation, even when the 'right' construct is measured, truly has little or no effect on achievement, which is what the results of the recent meta‐analysis by Vu et al. ([<reflink idref="bib87" id="ref156">87</reflink>]) also suggested. Vu et al. ([<reflink idref="bib87" id="ref157">87</reflink>]) found that the reciprocal effects between motivation (even for the self‐belief type) and achievement are found to be a function of the used statistical modelling technique (see further discussion of this point below). Third, given that, in our exploratory analyses, high school grades in English significantly predicted achievement in this English word learning task, it is plausible that the breadth of one's existing vocabulary has a strong influence on how well one can learn new words. This not only again dovetails with the results of the meta‐analysis of Vu et al. ([<reflink idref="bib87" id="ref158">87</reflink>]) but also with those of other large‐scale behavioural genetic studies (Van Bergen et al., [<reflink idref="bib85" id="ref159">85</reflink>], [<reflink idref="bib84" id="ref160">84</reflink>]), which together suggest that reading achievement fosters reading motivation and begets further reading achievement.</p> <hd id="AN0185103687-64">Effort</hd> <p>Regarding the behavioural pathway between motivation and achievement, we did not find that motivation affected quantitative effort. To our knowledge, our study is one of the few experiments–and the first in studies of the reciprocity of motivation and achievement–to measure objective expenditure of effort. We achieved this through an innovative feature in the learning task: Participants had to keep the mouse pressed for the learning materials to be displayed. This ensured that we measured how long participants were actively engaged with the learning materials, making it an objective measure of quantitative effort. With this measure, the absence of motivation → effort pathway could suggest two possibilities: either we did not measure the motivation that was responsible for driving effort, or motivation simply does not lead to effort. The latter would be puzzling. One possibility is that exerting effort is a stable trait that does not fluctuate easily, or at least not within the timeframe of our experiment (Bustamante et al., [<reflink idref="bib10" id="ref161">10</reflink>]). Another possibility is that this finding reflects a phenomenon named the 'cold start problem' (Pliakos et al., [<reflink idref="bib65" id="ref162">65</reflink>]). At the start of an unknown task like the one used in the current research, learners may not know how much effort is required for good performance and therefore, randomly choose a level of effort. This random level of effort is subsequently reinforced because participants receive positive feedback regardless of how much effort they exert (in the first round of the experiment). This continued until effort could no longer be reinforced when participants received the manipulated feedback.</p> <p>Our data also showed no quantitative effort → achievement link. This may have been explained by the so‐called labour‐in‐vain effect, where quantitative effort and achievement are not necessarily positively and linearly related (Nelson & Leonesio, [<reflink idref="bib55" id="ref163">55</reflink>]; Undorf & Ackerman, [<reflink idref="bib80" id="ref164">80</reflink>]). Another possibility is that <emph>this specific type</emph> of quantitative effort–simply spending more time being engaged with the learning materials–does not necessarily lead to learning progress. Several lines of self‐regulated learning research suggested that it is not the quantity of learning behaviours but rather the <emph>quality</emph> of learning, such as using effective learning strategies (Dunlosky et al., [<reflink idref="bib19" id="ref165">19</reflink>]) or engaging with 'desirable difficulties' (De Bruin et al., [<reflink idref="bib15" id="ref166">15</reflink>]), that most likely contributes to increased learning. We attempted to capture this by giving participants the option of viewing the correct words in a context, a more elaborate way of studying its meaning (high‐quality effort). However, even this strategy did not lead to higher achievement, possibly because elaboration is especially beneficial for long‐term retention rather than short‐term performance (e.g., Zhang & Reynolds, [<reflink idref="bib94" id="ref167">94</reflink>]).</p> <p>Additionally, the exploratory analyses (the multi‐level models) surprisingly showed that a drop in perceived performance did not significantly affect motivation or achievement. However, it did affect effort in paradoxical ways: Participants increased the time they spent studying the words (i.e., they expended more quantitative effort) but were less likely to engage in deeper processing of the words (i.e., qualitative effort). This may reflect a change in learning strategy, where students opt for superficial learning practice because of performance threat, which was observed in previous research (Hattie & Donoghue, [<reflink idref="bib33" id="ref168">33</reflink>]).</p> <hd id="AN0185103687-65">Causality in the relation between motivation and achievement</hd> <p>Our study is, to the best of our knowledge, the first experiment that utilized a manipulation to attempt to tackle the causality issue when studying the reciprocity between motivation and academic achievement. We expected the manipulation to have an impact on the dynamics between motivation and achievement rather than on motivation or achievement alone. This means that even though the average motivation and achievement levels of participants might change after experiencing the setback, the relationship between motivation and achievement should remain unchanged. We indeed found the drop in perceived performance to result in no changes in the (unilateral) relationship between motivation and achievement. However, this evidence is insufficient to support a causal relationship between motivation and achievement due to the lack of clear evidence that the manipulation worked. Exploratory analyses indicated that the manipulation primarily influenced effort: Participants spent more time on learning materials after noticing the setback but shifted to lower‐quality materials as the experiment progressed. While the feedback appeared to affect participants, it only influenced effort, not motivation or achievement, providing weak evidence overall.</p> <hd id="AN0185103687-66">Statistical modelling strategies</hd> <p>In terms of statistical modelling, we found that when applying RI‐CLPM to the same dataset, reciprocal effects were not observed, with only the unilateral achievement → motivation link being evident. This contrasts with the findings from fitting CLPM, where reciprocity between motivation and achievement was identified. This discrepancy dovetails with the meta‐analytic results of Vu et al. ([<reflink idref="bib87" id="ref169">87</reflink>]) which suggested that whether the reciprocal effects between motivation (even for the self‐belief type) and achievement are found is a function of the used statistical modelling technique. CLPM, the predominantly used statistical tool in studies finding support for the reciprocity between motivation and achievement, might have conflated the between‐ and within‐person variations (Hamaker et al., [<reflink idref="bib31" id="ref170">31</reflink>]; Núñez‐Regueiro et al., [<reflink idref="bib58" id="ref171">58</reflink>]). Extensions of CLPMs such as RI‐CLPM and RC‐CLPM are the suggested remedies which work by separating the between‐variations from the within‐person processes. Similarly to ours, a few longitudinal studies investigating reciprocity and utilizing extended CLPMs also reported diminished or disappeared effects of motivation on achievement compared to standard CLPMs (Burnette et al., [<reflink idref="bib7" id="ref172">7</reflink>]; Ehm et al., [<reflink idref="bib22" id="ref173">22</reflink>]; Liu et al., [<reflink idref="bib41" id="ref174">41</reflink>]; Núñez‐Regueiro et al., [<reflink idref="bib58" id="ref175">58</reflink>]). Hübner et al. ([<reflink idref="bib36" id="ref176">36</reflink>]) argued that the debate over which statistical model is appropriate is not final due to the contingency between the assumed structure in data and the chosen model (see also Marsh et al., [<reflink idref="bib45" id="ref177">45</reflink>]), and that future studies using data simulation–where the underlying processes between constructs are known–would be able to offer a clear answer. To this end, a simulation study by Freichel et al. ([<reflink idref="bib26" id="ref178">26</reflink>]) showed that RI‐CLPM outperformed CLPM in terms of separating within‐person temporal effects. Very recently, independent re‐analyses of the meta‐analytic data of Wu et al. ([<reflink idref="bib92" id="ref179">92</reflink>]) and of the simulated data that resemble Marsh et al.'s ([<reflink idref="bib45" id="ref180">45</reflink>]) data both suggested that the previously found reciprocal effects could have been spurious (Sorjonen et al, [<reflink idref="bib75" id="ref181">75</reflink>]; [<reflink idref="bib76" id="ref182">76</reflink>]). In sum, most current methodological and empirical works seem to converge on the possibility that there is no reciprocity between motivation and achievement when appropriate modelling strategy is employed.</p> <hd id="AN0185103687-67">Limitations and future research</hd> <p>Despite its strengths, the current work suffers a number of limitations. Regarding motivation construct, we chose intrinsic motivation with the expectation that it would be more mutable than other motivation constructs (e.g., ASC or achievement goals) within the hour that the experiment lasted. However, students did not come to the lab with the intrinsic motivation to learn new English words. The main drive behind their participation may have been to obtain study credits. This suggests that we may have assessed only one motivational construct among many possible constructs that interacted to affect learning or that were specifically directed to the task at hand, whereas both the effort and achievement measured were specific to that task. Note that this problem would also arise if ASC were measured. We did find that motivation waxed and waned over the course of the learning task. This suggests that there was still some relationship between the motivation that we measured and the motivation that was behind the participation in the task, regardless of what exactly that motivation was. Furthermore, even though we did include questionnaires of EB, goal orientation and ASC, they were only measured at one time point. Future research could use such measures longitudinally to test the reciprocity with such measures of motivation.</p> <p>Our conceptualization of motivation is rather broad, reflecting the many conceptualizations of motivation in the literature (Vu et al., [<reflink idref="bib86" id="ref183">86</reflink>]). The multiple ways in which motivation is defined have consequences for how we should understand the reciprocity between academic achievement and motivation. Not all authors studying relations between academic achievement and ASC refer to the reciprocity between academic achievement and <emph>motivation</emph>. However, the prevailing view in the literature positions ASC as a motivational construct. Our research aims to reassess this reciprocity in relation to ASC and extend the scope to explore potential reciprocal relationships with other motivational constructs, such as intrinsic motivation. Although we can empirically investigate only one motivational construct at a time, our goal is to offer a theoretical contribution that transcends any single construct. Future studies could examine other specific motivational constructs.</p> <p>Our model specifications did not allow for studying the effort → motivation link specifically but this pathway could be interesting for future research. It is possible that participants form an implicit judgement of how much effort they exerted in a previous block which then had an effect on their motivation in the subsequent block. This is, in a way, a retrospective but not declarative judgement of effort, which possibly has a different relationship with motivation and achievement from the type of effort that we discussed in the Introduction and also from the type of effort that we did measure in the current research. Since we only have correlational data for this pathway, a promising avenue for future research is to experimentally study it.</p> <hd id="AN0185103687-68">CONCLUSIONS</hd> <p>In conclusion, the present research found little support for the existence of the reciprocity between academic achievement and motivation in a short timeframe vocabulary learning experiment. Only the unilateral effect of previous achievement on subsequent motivation was evident. The search for the mechanism also revealed that motivation did not seem to be related to the effort put into learning and that effort did not translate into achievement in our experiment. The effects of the experimental manipulation seemed to suggest that the relationship between motivation and achievement remained stable over time, yet there was insufficient evidence for the causal relationship between motivation and achievement. In addition, contrary to what is frequently posited in mindset theory, having a more growth mindset was not related to how learners responded to setbacks. Our study also highlights the importance of studying the behavioural‐mediating pathway and the broad operationalizations of motivation in the literature. Last but not least, our results support the emerging body of research suggesting that whether the reciprocal effects between motivation and achievement are found is a function of the statistical modelling technique used.</p> <hd id="AN0185103687-69">AUTHOR CONTRIBUTIONS</hd> <p> <bold>TuongVan Vu:</bold> Conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; visualization; writing – original draft; writing – review and editing. <bold>Martijn Meeter:</bold> Conceptualization; funding acquisition; methodology; writing – review and editing. <bold>Abe Hofman:</bold> Formal analysis; methodology; visualization; writing – review and editing. <bold>Brenda Jansen:</bold> Funding acquisition; writing – review and editing. <bold>Lucía Magis‐Weinberg:</bold> Funding acquisition; writing – review and editing. <bold>Elise van Triest:</bold> Formal analysis; writing – review and editing. <bold>Nienke van Atteveldt:</bold> Conceptualization; funding acquisition; supervision; writing – review and editing.</p> <hd id="AN0185103687-70">CONFLICT OF INTEREST STATEMENT</hd> <p>The authors have no conflict of interest to declare.</p> <hd id="AN0185103687-71">DATA AVAILABILITY STATEMENT</hd> <p>The anonymised data that support the power analysis and findings of this study are openly available in the Open Science Framework (OSF) at https://osf.io/de4j5/.</p> <hd id="AN0185103687-72">A APPENDIX</hd> <p>Pre‐task questionnaires.</p> <hd1 id="AN0185103687-73">General Theory of Intelligence Questionnaire</hd1> <p> <emph>Entity subscale</emph> </p> <p>I don't think I personally can do much to increase my intelligence.</p> <p>My intelligence is something about me that I personally can't change very much.</p> <p>To be honest, I don't think I can really change how intelligent I am.</p> <p>I can learn new things, but I don't have the ability to change my basic intelligence.</p> <p> <emph>Incremental subscale</emph> </p> <p>With enough time and effort I think I could significantly improve my intelligence level.</p> <p>I believe I can always substantially improve on my intelligence.</p> <p>Regardless of my current intelligence level, I think I have the capacity to change it quite a bit.</p> <p>I believe I have the ability to change my basic intelligence level considerably over time.</p> <hd1 id="AN0185103687-74">Specific Theory Of Intelligence Questionnaire</hd1> <p> <emph>Entity subscale</emph> </p> <p>I don't think I personally can do much to increase my foreign language aptitude.</p> <p>My foreign language aptitude is something about me that I personally can't change very much.</p> <p>To be honest, I don't think I can really change my foreign language learning aptitude.</p> <p>I can learn new things, but I don't have the ability to change my basic foreign language aptitude.</p> <p> <emph>Incremental subscale</emph> </p> <p>With enough time and effort I think I could significantly improve my foreign language aptitude level.</p> <p>I believe I can always substantially improve on my foreign language aptitude.</p> <p>Regardless of my current foreign language aptitude level, I think I have the capacity to change it quite a bit.</p> <p>I believe I have the ability to change my basic foreign language aptitude level considerably over time.</p> <hd1 id="AN0185103687-75">Achievement Goal Questionnaire</hd1> <p> <emph>Mastery‐approach subscale</emph> </p> <p>My aim is to completely master the material presented in this class.</p> <p>My goal is to learn as much as possible.</p> <p>I am striving to understand the content as thoroughly as possible.</p> <p> <emph>Performance‐approach subscale</emph> </p> <p>I am striving to do well compared to other students.</p> <p>My aim is to perform well relative to other students.</p> <p>My goal is to perform better than the other students.</p> <p> <emph>Mastery‐avoidance subscale</emph> </p> <p>My aim is to avoid learning less than I possibly could.</p> <p>My goal is to avoid learning less than it is possible to learn.</p> <p>I am striving to avoid an incomplete understanding of the course material.</p> <p> <emph>Performance‐avoidance subscale</emph> </p> <p>My goal is to avoid performing poorly compared to others.</p> <p>I am striving to avoid performing worse than others.</p> <p>My aim is to avoid doing worse than other students.</p> <hd1 id="AN0185103687-76">Effort Beliefs</hd1> <p> <emph>Positive belief subscale</emph> </p> <p>When something is hard, it just makes me want to work more on it, not less.</p> <p>If you don't work hard and put in a lot of effort, you probably won't do well.</p> <p>The harder you work at something, the better you will be at it.</p> <p>If an assignment is hard, it means I'll probably learn a lot doing it.</p> <p> <emph>Negative belief subscale</emph> </p> <p>To tell the truth, when I work hard at my schoolwork, it makes me feel like I'm not very smart.</p> <p>It doesn't matter how hard you work–if you're not smart, you won't do well.</p> <p>If you're not good at a subject, working hard won't make you good at it.</p> <p>If a subject is hard for me, it means I probably won't be able to do really well at it.</p> <p>If you're not doing well at something, it's better to try something easier.</p> <hd1 id="AN0185103687-77">Verbal Academic Self‐Concept</hd1> <p>I have trouble expressing myself when trying to write something.</p> <p>I can write effectively.</p> <p>I have a poor vocabulary.</p> <p>I am an avid reader.</p> <p>I do not do well on tests that require a lot of verbal reasoning ability.</p> <p>Relative to most people, my verbal skills are quite good.</p> <p>I often have to read things several times before I understand them.</p> <p>I am good at expressing myself.</p> <p>In school I had more trouble learning to read than most other students.</p> <p>I have good reading comprehension.</p> <hd id="AN0185103687-78">B APPENDIX</hd> <p>Codes to generate the scores of rankings of the fake participants.</p> <p># Define sigmoid and inverse sigmoid.</p> <p>def sigmoid(x):</p> <p>Return 100/(1 + 2*np.exp. (−3.5*x/100))**2</p> <p>def inverse_sigmoid(y):</p> <p>return −100/3.5*np.log ((np.sqrt(100/y)‐1)/2)</p> <p># Shuffle the participant names, but only within their categories.</p> <p>for sublist in [pBest, pGood, pAverage, pBad, pWorst]:</p> <p>np.random.shuffle (sublist)</p> <p># Create different versions of the manipulation.</p> <p>if Block == 'AAF':</p> <p>rank_max = 7</p> <p>rank_min = 9</p> <p>else:</p> <p>rank_max = 11</p> <p>rank_min = 13</p> <p>rank = np.random.randint (rank_max, rank_min + 1).</p> <p>thisExp.addData ('Participant_rank', rank).</p> <p># Concatenate the participant names and the participant in the correct position.</p> <p>pName_shuffled = np.concatenate ((pBest, pGood, pAverage, pBad, pWorst)).</p> <p>pName_shuffled = np.insert (pName_shuffled, rank‐1, 'You: ')</p> <p># Create sample of scores.</p> <p>inverse_correct_percentage = inverse_sigmoid (correct_percentage).</p> <p>sample = np.random.normal (loc = inverse_correct_percentage, scale = 20, size = 500).</p> <p>higher_scores = sigmoid (sample [sample > inverse_correct_percentage] [:rank‐1]).</p> <p>lower_scores = sigmoid (sample [sample < inverse_correct_percentage] [:20‐rank]).</p> <p>scores = np.concatenate ((higher_scores, lower_scores)).</p> <p>scores = np.around (np.sort (np.append (scores, correct_percentage)), decimals = 1) [::‐1].</p> <p>GRAPH: Data S1.</p> <p>GRAPH: Table S1.</p> <p>GRAPH: Tables S2–S4.</p> <p>GRAPH: Table S5.</p> <ref id="AN0185103687-79"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref99" type="bt">1</bibl> <bibtext> An English word can have multiple meanings that correspond to different Dutch translations. 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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Academic Motivation-Achievement Cycle and the Behavioural Pathways: A Short-Timeframe Experiment with Manipulated Perceived Achievement
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22TuongVan+Vu%22">TuongVan Vu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6700-2439">0000-0001-6700-2439</externalLink>)<br /><searchLink fieldCode="AR" term="%22Martijn+Meeter%22">Martijn Meeter</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5112-6717">0000-0002-5112-6717</externalLink>)<br /><searchLink fieldCode="AR" term="%22Abe+Hofman%22">Abe Hofman</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4269-5296">0000-0003-4269-5296</externalLink>)<br /><searchLink fieldCode="AR" term="%22Brenda+Jansen%22">Brenda Jansen</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9262-933X">0000-0001-9262-933X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lucía+Magis-Weinberg%22">Lucía Magis-Weinberg</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6036-2850">0000-0002-6036-2850</externalLink>)<br /><searchLink fieldCode="AR" term="%22Elise+van+Triest%22">Elise van Triest</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8770-0717">0000-0002-8770-0717</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nienke+van+Atteveldt%22">Nienke van Atteveldt</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3387-6151">0000-0002-3387-6151</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22British+Journal+of+Educational+Psychology%22"><i>British Journal of Educational Psychology</i></searchLink>. 2025 95(2):683-722.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 40
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research<br />Tests/Questionnaires
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+Motivation%22">Learning Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Gains%22">Achievement Gains</searchLink><br /><searchLink fieldCode="DE" term="%22Vocabulary+Development%22">Vocabulary Development</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Netherlands%22">Netherlands</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/bjep.12731
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0007-0998<br />2044-8279
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: The purported reciprocity between motivation and academic achievement in education has largely been supported by correlational data. Aims: Our first aim was to determine experimentally whether motivation and achievement are reciprocally related. The second objective was to investigate a potential behavioural mediation pathway between motivation and achievement by measuring the objective effort expended on learning. Finally, we studied the causality of these relations by analysing the dynamics between motivation and achievement (rather than examining them as individual constructs) when perceived achievement was experimentally manipulated. Sample(s): The study employed a short-timeframe experiment in which 309 Dutch undergraduate students (M[subscript age] = 19.89, SD = 2.08) learned new English vocabulary. Methods: Their motivation, effort, and achievement were measured at multiple time points within one hour. Midway through the experiment, participants received manipulated feedback indicating an achievement decline, which was expected to influence their subsequent motivation, effort, and actual achievement. A random-intercept cross-lagged panel framework was employed to model how one construct influenced another over time. Results: We found a unilateral effect of achievement on motivation (i.e., no reciprocity), which remained stable across the time points. Our experimental manipulation partially supported a causal interpretation of the unilateral achievement[right arrow]motivation pathway. Additionally, no mediation effect of effort was identified: motivation was not associated with effort, nor was effort linked to achievement. Conclusions: Our findings underscore the importance of further exploration of behavioural mediation pathways, a broad operationalization of motivation, and the application of appropriate modelling strategies to investigate the motivation-achievement reciprocity.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1470953
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1470953
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bjep.12731
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 40
        StartPage: 683
    Subjects:
      – SubjectFull: Learning Motivation
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: English (Second Language)
        Type: general
      – SubjectFull: Achievement Gains
        Type: general
      – SubjectFull: Vocabulary Development
        Type: general
      – SubjectFull: Netherlands
        Type: general
    Titles:
      – TitleFull: Academic Motivation-Achievement Cycle and the Behavioural Pathways: A Short-Timeframe Experiment with Manipulated Perceived Achievement
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: TuongVan Vu
      – PersonEntity:
          Name:
            NameFull: Martijn Meeter
      – PersonEntity:
          Name:
            NameFull: Abe Hofman
      – PersonEntity:
          Name:
            NameFull: Brenda Jansen
      – PersonEntity:
          Name:
            NameFull: Lucía Magis-Weinberg
      – PersonEntity:
          Name:
            NameFull: Elise van Triest
      – PersonEntity:
          Name:
            NameFull: Nienke van Atteveldt
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0007-0998
            – Type: issn-electronic
              Value: 2044-8279
          Numbering:
            – Type: volume
              Value: 95
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: British Journal of Educational Psychology
              Type: main
ResultId 1