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Is Boredom the Opposite of Interest? A Longitudinal Reciprocal Effect Study

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Title: Is Boredom the Opposite of Interest? A Longitudinal Reciprocal Effect Study
Language: English
Authors: Katharina Luisa Boehme, Thomas Goetz, Markus Feuchter, Franzis Preckel (ORCID 0000-0002-5768-8702)
Source: Educational Psychology Review. 2025 37(1).
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 35
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Foreign Countries, Secondary School Mathematics, Secondary School Students, Secondary School Teachers, German, Mathematics Education, Student Interests, Student Attitudes, Learner Engagement, Psychological Patterns, Native Language Instruction, Attitude Measures
Geographic Terms: Germany
DOI: 10.1007/s10648-025-09991-5
ISSN: 1040-726X
1573-336X
Abstract: After decades of being conceptualised solely as a lack of interest, boredom has recently gained attention as an important construct in its own right. However, there is still a lack of studies focusing on the relations and developmental interplay of these two closely related constructs. This study examines the overall long-term developmental structure and interplay of students' boredom and interest in the school domains of mathematics and German from fifth to eighth grade. We investigated German secondary school students (N = 1471) over four waves of measurement, using self-report questionnaires. Confirmatory factor analyses in preparation to the longitudinal approach revealed a significantly better fit for two- vs. one-factor models, indicating an empirical separability of boredom and interest. This was further supported by different stabilities in our latent cross-lagged models with low autoregressive paths for boredom and high paths for interest. The latent cross-lagged models also revealed that higher levels of earlier interest were related to lower levels of later boredom. Surprisingly, individuals with higher boredom scores relative to others on average increased in their interest from the second time point onwards. Findings were robust for German and mathematics. Overall, the results show that while boredom and interest have a large phenomenological overlap, they are empirically separable constructs with different levels of stability and influence each other in a distinctive manner throughout their developmental interplay. Implications for research and practice are outlined.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1460620
Database: ERIC
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  Value: <anid>AN0183065736;epv01mar.25;2025Mar27.13:05;v2.2.500</anid> <title id="AN0183065736-1">Is Boredom the Opposite of Interest? A Longitudinal Reciprocal Effect Study </title> <p>After decades of being conceptualised solely as a lack of interest, boredom has recently gained attention as an important construct in its own right. However, there is still a lack of studies focusing on the relations and developmental interplay of these two closely related constructs. This study examines the overall long-term developmental structure and interplay of students' boredom and interest in the school domains of mathematics and German from fifth to eighth grade. We investigated German secondary school students (N = 1471) over four waves of measurement, using self-report questionnaires. Confirmatory factor analyses in preparation to the longitudinal approach revealed a significantly better fit for two- vs. one-factor models, indicating an empirical separability of boredom and interest. This was further supported by different stabilities in our latent cross-lagged models with low autoregressive paths for boredom and high paths for interest. The latent cross-lagged models also revealed that higher levels of earlier interest were related to lower levels of later boredom. Surprisingly, individuals with higher boredom scores relative to others on average increased in their interest from the second time point onwards. Findings were robust for German and mathematics. Overall, the results show that while boredom and interest have a large phenomenological overlap, they are empirically separable constructs with different levels of stability and influence each other in a distinctive manner throughout their developmental interplay. Implications for research and practice are outlined.</p> <p>Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s10648-025-09991-5.</p> <p>Academic emotions like boredom and motivational orientations like interest play a crucial role in students' academic engagement and performance (Grazia et al., [<reflink idref="bib34" id="ref1">34</reflink>]; Renninger & Hidi, [<reflink idref="bib80" id="ref2">80</reflink>]; Tze et al., [<reflink idref="bib104" id="ref3">104</reflink>]). Both boredom and interest are among the most frequent and intense experiences students report during classes (Ainley, [<reflink idref="bib2" id="ref4">2</reflink>]; Goetz et al., [<reflink idref="bib29" id="ref5">29</reflink>], [<reflink idref="bib30" id="ref6">30</reflink>]; Nett et al., [<reflink idref="bib68" id="ref7">68</reflink>]; Pekrun et al., [<reflink idref="bib71" id="ref8">71</reflink>]) and they influence learning processes and academic success (Goetz et al., [<reflink idref="bib26" id="ref9">26</reflink>]; Harackiewicz et al., [<reflink idref="bib36" id="ref10">36</reflink>]; Pekrun et al., [<reflink idref="bib71" id="ref11">71</reflink>], [<reflink idref="bib72" id="ref12">72</reflink>]). In interviews of American high school dropouts, 52% of the respondents reported that factors of school engagement, like boredom in class, were major 'turning point reasons' for leaving school (McDermott et al., [<reflink idref="bib59" id="ref13">59</reflink>]).</p> <p>Besides dropping out of school, there are other detrimental effects of boredom like aggression, depression, reduced performance, and decreased motivation (Goetz et al., [<reflink idref="bib29" id="ref14">29</reflink>]; Tze et al., [<reflink idref="bib104" id="ref15">104</reflink>]; van Tilburg & Igou, [<reflink idref="bib105" id="ref16">105</reflink>]). Interest, on the other hand, has positive effects on intrinsic motivation, curiosity, and exploration (Silvia, [<reflink idref="bib98" id="ref17">98</reflink>]), willingness for exertion and commitment towards an activity or school subject (Mueller, [<reflink idref="bib63" id="ref18">63</reflink>]), the use of deeper learning strategies (Hidi et al., [<reflink idref="bib40" id="ref19">40</reflink>]) and on academic achievement (Schiefele et al., [<reflink idref="bib93" id="ref20">93</reflink>]).</p> <p>Some researchers conceptualised boredom as the absence of interest (Ainley, [<reflink idref="bib2" id="ref21">2</reflink>]) and in educational psychology, 'boredom only recently gained attention from researchers as a construct separate from interest' (Tanaka & Murayama, [<reflink idref="bib103" id="ref22">103</reflink>], p. 1122). Although 'the emotion of boredom has sparked considerable interest in research on teaching and learning' (Goetz et al., [<reflink idref="bib27" id="ref23">27</reflink>], p. 911) and the significance of the interplay of emotional and motivational constructs is widely acknowledged, most studies have examined boredom and interest independently or only investigated their cross-sectional relationships (Pekrun et al., [<reflink idref="bib71" id="ref24">71</reflink>]; Sparfeldt et al., [<reflink idref="bib100" id="ref25">100</reflink>]). There is a lack of longitudinal studies on boredom and interest that could help researchers gain a better understanding of the specific relationships between these two constructs over time. Over the course of the school years, students' boredom increases (Ahmed et al., [<reflink idref="bib1" id="ref26">1</reflink>]; Pekrun et al., [<reflink idref="bib74" id="ref27">74</reflink>]) and their academic interest declines (Scherrer & Preckel, [<reflink idref="bib91" id="ref28">91</reflink>]). Research on their longitudinal associations is therefore not only theoretically meaningful but might also be of high practical relevance for supporting students' emotional and motivational development in school. The present study aims to close this gap by investigating the longitudinal relationships of boredom and interest in two subjects (i.e. mathematics and German) with a large sample of secondary school students over four waves of measurement.</p> <hd id="AN0183065736-2">Boredom and Interest in Academic Settings</hd> <p></p> <hd id="AN0183065736-3">Definition</hd> <p></p> <hd id="AN0183065736-4">Boredom</hd> <p>Boredom is the subject of many different areas of research, from cognitive psychology to clinical or organisational psychology, each of which offers a distinct form of definition. Across the different approaches, boredom is regarded an unpleasant emotional state, which is created by a discrepancy between the internal needs of a person and their external environment, resulting in a lack of mental stimulation and the wish to alter or leave the situation (Eastwood et al., [<reflink idref="bib13" id="ref29">13</reflink>]; Fahlman et al., [<reflink idref="bib14" id="ref30">14</reflink>]; Fisherl, [<reflink idref="bib17" id="ref31">17</reflink>]).</p> <p>Academic boredom in particular is considered 'an affective state composed of unpleasant feelings, lack of stimulation, and low physiological arousal' (Pekrun et al., [<reflink idref="bib71" id="ref32">71</reflink>], p. 532). More specifically, it is characterised by unpleasant, aversive feelings, an altered perception of time, the motivation to change the activity or to leave the situation, reduced arousal, and related facial, vocal, and postural expressions (Pekrun et al., [<reflink idref="bib71" id="ref33">71</reflink>]; Scherer, [<reflink idref="bib89" id="ref34">89</reflink>]; Scherer & Moors, [<reflink idref="bib90" id="ref35">90</reflink>]). These characteristics classify boredom as a unique emotional experience and counter early conceptions of boredom as solely a 'pervasive lack of interest' (Fisherl, [<reflink idref="bib17" id="ref36">17</reflink>], p. 396).</p> <p>Academic boredom is domain-specific (Goetz et al., [<reflink idref="bib25" id="ref37">25</reflink>]; Gogol et al., [<reflink idref="bib32" id="ref38">32</reflink>]). Goetz et al. ([<reflink idref="bib25" id="ref39">25</reflink>]) found very weak between-domains relations for boredom that decreased further with students' age.</p> <hd id="AN0183065736-5">Interest</hd> <p>Interest can be defined as 'the feeling of wanting to give your attention to something or of wanting to be involved with and to discover more about something' (Cambridge Dictionary, [<reflink idref="bib9" id="ref40">9</reflink>]). The concept of interest has a long and rich research tradition in different fields of psychology (Silvia, [<reflink idref="bib99" id="ref41">99</reflink>]). It is described as a state of positive valence that fosters motivation, focuses attention, and facilitates learning (Krapp, [<reflink idref="bib51" id="ref42">51</reflink>]; Renninger & Hidi, [<reflink idref="bib80" id="ref43">80</reflink>]). Academic interest is considered 'the psychological state of engaging or the predisposition to reengage with particular classes of objects, events, or ideas over time', which 'includes both affective and cognitive components' (Hidi & Renninger, [<reflink idref="bib39" id="ref44">39</reflink>], p. 112). Whereas the affective element of interest consists predominantly of positive emotional valence towards the object of interest (Koeller et al., [<reflink idref="bib49" id="ref45">49</reflink>]), the cognitive component corresponds to 'perceptual and representational activities related to engagement' (Hidi & Renninger, [<reflink idref="bib39" id="ref46">39</reflink>], p. 112). Depending on the theory, emphasis is placed on the emotional or motivational component, resulting in an ongoing debate as to whether interest should be characterised as an emotion or as a motivational construct (Renninger & Hidi, [<reflink idref="bib81" id="ref47">81</reflink>]). Academic interest differs from general interest mainly in the specificity of its context and application.</p> <p>Within the academic context, interest is related to constructs like intrinsic motivation or student engagement. Whereas intrinsic motivation refers to the relatively stable cognitive-emotional need to perform a certain activity for its own sake, regardless of external rewards (Deci & Ryan, [<reflink idref="bib12" id="ref48">12</reflink>]), student engagement is a much broader construct that comprises all interactions of students with their academic environment (emotional, cognitive, and behavioural) and is influenced by everyday school experiences (Fredricks et al., [<reflink idref="bib19" id="ref49">19</reflink>]).</p> <p>Consequently, academic interest is consistently directed towards a specific object, as opposed to a particular task, which is characteristic of intrinsic motivation. It is also distinguished by its relative stability, in contrast to the fluctuations observed in student engagement. Furthermore, academic interest encompasses both cognitive and emotional components, but not a behavioural component, which is unique to student engagement (Deci & Ryan, [<reflink idref="bib12" id="ref50">12</reflink>]; Fredricks et al., [<reflink idref="bib19" id="ref51">19</reflink>]; Hidi & Renninger, [<reflink idref="bib39" id="ref52">39</reflink>]).</p> <p>The majority of scholars agree that academic interest is largely domain-specific (Hidi & Renninger, [<reflink idref="bib39" id="ref53">39</reflink>]; Krapp, [<reflink idref="bib52" id="ref54">52</reflink>]). This assumption is supported by a number of empirical studies that have demonstrated that interest and closely related constructs such as task value (Wigfield & Eccles, [<reflink idref="bib107" id="ref55">107</reflink>]) or intrinsic motivation (Ryan & Deci, [<reflink idref="bib85" id="ref56">85</reflink>]) show very weak between-domains relations (e.g. Bong, [<reflink idref="bib5" id="ref57">5</reflink>]; Goetz et al., [<reflink idref="bib25" id="ref58">25</reflink>]).</p> <hd id="AN0183065736-6">Boredom and Interest: Distinct Constructs or Poles of One Dimension?</hd> <p>In considering the relations between boredom and interest, it is important to recognise that boredom is not merely the absence of situational interest, i.e. the 'focused attention and the affective reaction that is triggered in the moment by environmental stimuli' (Hidi & Renninger, [<reflink idref="bib39" id="ref59">39</reflink>], p. 113). Although boredom can arise from a lack of interest in a situation, it cannot be equated with it (Pekrun et al., [<reflink idref="bib71" id="ref60">71</reflink>], [<reflink idref="bib72" id="ref61">72</reflink>]). Whereas a lack of interest is affectively neutral and does not cause emotional strain, boredom is unenjoyable and of negative valence (Goetz et al., [<reflink idref="bib28" id="ref62">28</reflink>]). Connected to these unique emotional components, boredom and lack of interest lead to different motivational consequences (Goetz & Frenzel, [<reflink idref="bib23" id="ref63">23</reflink>]). Boredom, on the one hand, is usually accompanied by an impulse to escape the triggering situation or cease the ongoing unpleasant activity, therefore inducing avoidance motivation. Lack of interest, on the other hand, is affectively neutral and therefore does not provoke a specific motivational reaction. Rather, it implies an absence of approach motivation and does not necessarily induce avoidance motivation (Pekrun et al., [<reflink idref="bib71" id="ref64">71</reflink>]). These unique emotional and motivational components of boredom and interest allow the conclusion that, although the constructs conceptually overlap, boredom is a stand-alone emotional experience distinct from a lack of interest (Pekrun et al., [<reflink idref="bib71" id="ref65">71</reflink>]).</p> <hd id="AN0183065736-7">Development of Boredom and Interest</hd> <p>A common characteristic of both boredom and interest is that they are often first triggered by situations and, over time, can evolve into more stable dispositions (Renninger, [<reflink idref="bib82" id="ref66">82</reflink>]). One central feature of interest during this process is its content or object specificity. According to the person-object-theory of interest (Krapp, [<reflink idref="bib51" id="ref67">51</reflink>], [<reflink idref="bib52" id="ref68">52</reflink>]), interest can be characterised as a relation between a person and an object, i.e. a subjective representation of a certain idea, topic, or school subject. In the beginning, this relation manifests itself in the psychological state of concrete interactions between a person and his or her object of interest (<emph>situational interest</emph>). As the interested learner engages with the object repeatedly over time, it turns into an <emph>individual interest</emph>, a relatively enduring disposition (Hidi et al., [<reflink idref="bib40" id="ref69">40</reflink>]). The detailed processes of this transition are described in the four-phase theory of interest development (Hidi & Renninger, [<reflink idref="bib39" id="ref70">39</reflink>]).</p> <p>In the development of boredom, on the other hand, the specific object or content is just one of various influencing factors. In some theoretical models (Hill & Perkins, [<reflink idref="bib41" id="ref71">41</reflink>]; Robinson, [<reflink idref="bib83" id="ref72">83</reflink>]), characteristics of the domain or task are considered antecedents of boredom, but always in interaction with characteristics of the person (e.g. her extraversion) and characteristics of the environment (e.g. the monotony of the situation or the lack of alternatives). Characteristics of the person that seem to be particularly relevant in achievement situations are the personal value of and the perceived control over the achievement activity. A high value in combination with either very low or very high control results in feelings of boredom (Pekrun, [<reflink idref="bib70" id="ref73">70</reflink>]).</p> <p>Looking at trajectories of boredom and interest over the course of the school years, both constructs seem to be moderately stable. Stabilities found in long-term studies range between <emph>r</emph> = 0.30 to 0.55 for boredom (Frenzel et al., [<reflink idref="bib21" id="ref74">21</reflink>]; Putwain, [<reflink idref="bib78" id="ref75">78</reflink>]) and <emph>r</emph> = 0.33 to 0.86 for interest (Gottfried et al., [<reflink idref="bib33" id="ref76">33</reflink>]; Pekrun et al., [<reflink idref="bib74" id="ref77">74</reflink>]; Scherrer & Preckel, [<reflink idref="bib91" id="ref78">91</reflink>]). Overall, boredom increases in secondary school (within grade 7: Ahmed et al., [<reflink idref="bib1" id="ref79">1</reflink>]; 5th to 6th grade: Pekrun et al., [<reflink idref="bib74" id="ref80">74</reflink>]; 5th to 9th grade: Sakaki et al., [<reflink idref="bib86" id="ref81">86</reflink>]) and students who show a higher initial level of boredom also experience a higher increase in boredom (Feuchter & Preckel, [<reflink idref="bib16" id="ref82">16</reflink>]). The development seems to be asymptotic, and the changes tend to decline in higher grades. Grazia and colleagues ([<reflink idref="bib34" id="ref83">34</reflink>]) found that the extent of change in a school year has an impact on its effects. The steeper the incline in a student's boredom, the greater the negative effects were on their academic outcomes. For interest, a meta-analysis of longitudinal studies documented a steady decline during the K-12 years in intrinsic motivation including interest scales (Scherrer & Preckel, [<reflink idref="bib91" id="ref84">91</reflink>]). Individual studies found that students' interest tends to decline from elementary to secondary school in a curvilinear way (Krapp, [<reflink idref="bib52" id="ref85">52</reflink>]; Pekrun et al., [<reflink idref="bib74" id="ref86">74</reflink>]; Potvin & Hasni, [<reflink idref="bib77" id="ref87">77</reflink>]), plateauing around grade 8 or 9 (Fredricks & Eccles, [<reflink idref="bib18" id="ref88">18</reflink>]; Frenzel et al., [<reflink idref="bib20" id="ref89">20</reflink>]).</p> <p>In conclusion, academic boredom and interest both seem to develop unfavourably during secondary school with an increase in boredom and a decrease in interest.</p> <hd id="AN0183065736-8">The Functional Relationship Between Boredom and Interest</hd> <p></p> <hd id="AN0183065736-9">Theoretical Considerations</hd> <p>Theoretically, different relationships between boredom and interest can be posited. According to Hidi and Renninger's theory of interest development ([<reflink idref="bib39" id="ref90">39</reflink>]), experiences of boredom should impair interest development, and individuals who are high in interest (i.e. who engage intensely in a subject or topic) should encounter less boredom. This implies a negative relationship between interest and boredom.</p> <p>A negative relationship between boredom and interest is also assumed in Pekrun's control-value theory ([<reflink idref="bib70" id="ref91">70</reflink>]), where boredom increases when the subjective value of the task or subject decreases. Hence, low interest as an expression of low subjective value should be related to higher boredom and, vice versa, high interest to lower boredom.</p> <p>Focusing more on students' strategies for coping with boredom, Nett et al. ([<reflink idref="bib67" id="ref92">67</reflink>]) assumed that boredom could lead to more interest via one of four coping strategies, i.e. the cognitive approach strategy. This strategy comprises students to think 'differently to change the perception of the situation' (p. 628), thereby reinforcing the value of the situation and subsequently reducing boredom. Beyond the particular situation that is perceived as boring, this cognitive strategy could enhance motivation and increase interest in the future as well.</p> <p>It could also be conceivable that students who were previously interested but for various reasons experience boredom in a subject could become less interested, as devaluing the subject could help reduce the cognitive dissonance between previous interest and the actual experience of boredom. Students with high interest in a subject could also be more likely to become bored, as they are likely to already be knowledgeable in the matter and have repeated experiences of learning nothing new in lessons. In this context, it is important to note that although being over- or under-challenged is a potential cause of boredom, the resulting boredom is not distinguishable in terms of its characteristics. Rather, it has been demonstrated that the resulting feelings of boredom are phenomenologically identical (Goetz et al., [<reflink idref="bib30" id="ref93">30</reflink>]).</p> <p>In brief, the available theoretical considerations allow for different predictions regarding the relationship between boredom and interest.</p> <hd id="AN0183065736-10">Empirical Results</hd> <p>Cross-sectional studies report negative correlations of medium to large size between boredom and interest. For example, Sparfeldt and colleagues ([<reflink idref="bib100" id="ref94">100</reflink>]) examined 498 elementary school students in grade 4 and found a correlation of <emph>r</emph> = − 0.77 between boredom and interest in mathematics. Slightly lower correlations between the two variables (<emph>r</emph> = − 0.35 in male and <emph>r</emph> = − 0.44 in female students) were found in a student sample of 501 8th graders (Sparfeldt et al., [<reflink idref="bib101" id="ref95">101</reflink>]). Focusing on university students in Japan, Tanaka and Murayama's study ([<reflink idref="bib103" id="ref96">103</reflink>]) revealed a correlation of <emph>r</emph> = − 0.45 between interest in psychology and boredom in class. Pekrun et al. ([<reflink idref="bib71" id="ref97">71</reflink>]) showed that the boredom experienced by German undergraduate students was negatively related (<emph>r</emph> = − 0.61) to their intrinsic motivation, a concept closely related to interest. Drawing on Pekrun's control-value theory of achievement emotions, Peixoto et al. ([<reflink idref="bib69" id="ref98">69</reflink>]) surveyed 1219 Portuguese students in grades 6 and 8 and found that the perceived value of mathematics negatively predicted boredom in a SEM analysis (<emph>β</emph> = − 0.49, <emph>p</emph> < 0.001).</p> <p>Longitudinal results concerning the joint development and relation of boredom and interest are scarce. One exception is Schukajlow ([<reflink idref="bib96" id="ref99">96</reflink>]), who investigated 119 9th graders in German middle track schools and measured their interest before and after a five-lesson unit in mathematics, as well as the boredom experienced during the lessons. Results showed that previous interest in mathematics was not significantly related to boredom experienced during the lessons, but it strongly predicted posttest interest. Students' boredom during the lessons was unrelated to their interest after the lessons.</p> <p>Pekrun and colleagues ([<reflink idref="bib72" id="ref100">72</reflink>]) studied 424 psychology students at a Canadian university over a time span of 1 year. Students were asked to complete a web-based questionnaire at five time points throughout the first and second semesters. Results of structural equation modelling showed that students' interest at the beginning of the semester had mainly negative effects on their later boredom (<emph>β</emph> ranging from − 0.44 at T1, − 0.11 at T2, − 0.15 at T3, 0.10 at T4 to − 0.04 at T5), suggesting that initial interest could be a protective factor against feeling bored and, on the contrary, that a lack of interest could facilitate the arousal of boredom.</p> <p>In conclusion, while boredom and interest exhibit some conceptual overlap, they also demonstrate clear distinctions in their conceptualisation and emergence. While boredom has a negative emotional component, lack of interest is experienced as emotionally neutral (Pekrun et al., [<reflink idref="bib71" id="ref101">71</reflink>]). Consequently, their motivational consequences differ. While boredom provokes avoidance motivation, low or a lack of interest provokes a lack of approach motivation (Goetz & Frenzel, [<reflink idref="bib23" id="ref102">23</reflink>]). Additionally, their development is influenced by different factors. Boredom is influenced by characteristics of the domain, the person, and the environment (Pekrun, [<reflink idref="bib70" id="ref103">70</reflink>]). Interest on the other hand shows a strong object specificity (Hidi, [<reflink idref="bib38" id="ref104">38</reflink>]). These aspects suggest that equating boredom with an absence of interest would be inaccurate. Most empirical findings on the relationship of boredom and interest are cross-sectional and reveal negative relations of medium effect size. The few available longitudinal studies only investigated relatively short time periods. Their findings indicate an impact of interest on boredom, with higher interest leading to lower boredom but not the other way around. However, more research is needed to substantiate these findings.</p> <hd id="AN0183065736-11">The Present Study</hd> <p></p> <hd id="AN0183065736-12">Aims of the Study</hd> <p>Boredom and interest in academic settings are closely related constructs that play a significant role in academic engagement and performance throughout a student's academic career (Grazia et al., [<reflink idref="bib34" id="ref105">34</reflink>]; Renninger & Hidi, [<reflink idref="bib80" id="ref106">80</reflink>]). However, the exact relationships and the mutual influence of these two constructs are still unclear and available empirical findings are limited to negative cross-sectional correlations between boredom and interest and opposing trajectories of increasing boredom and decreasing interest in secondary school. In the present study, we address this research gap by investigating interest and boredom at four points of measurement over the time span of four school years, during a period of significant adolescent development. We use a longitudinal design with four measurement points of boredom and interest to eliminate the constraints associated with cross-sectional research designs and to explore their cross-lagged relationships over time. Academic boredom and interest are organised in a domain-specific manner (Goetz et al., [<reflink idref="bib25" id="ref107">25</reflink>]; Gogol et al., [<reflink idref="bib32" id="ref108">32</reflink>]). In the present study, we select two key domains (mathematics and German) as examples. We also consider the potential relationships between these domains, as these are theoretically plausible. There is strong support that students contrast mathematics and language domains in dimensional comparisons (Moeller, [<reflink idref="bib61" id="ref109">61</reflink>]). The belief of being either a 'math person' or a 'language person' can have a significant impact on students' cross-domain perception of psychological variables (Wan et al., [<reflink idref="bib106" id="ref110">106</reflink>]). It is therefore possible that the development of boredom and interest in German is not independent of their development in mathematics.</p> <hd id="AN0183065736-13">Research Questions and Hypotheses</hd> <p></p> <hd id="AN0183065736-14">Relationship Between Interest and Boredom</hd> <p>Based on our extensive literature review, we posit that boredom is conceptually distinct from interest and that hints at this distinction can be found empirically. Both constructs differ in theoretical conceptions (e.g. control as predictor for boredom but not necessarily for interest; Pekrun, [<reflink idref="bib70" id="ref111">70</reflink>]), emotional components (boredom—aversive, lack of interest or disinterest—neutral; Pekrun et al., [<reflink idref="bib71" id="ref112">71</reflink>]), and motivational consequences (boredom—avoidance motivation, lack of interest or disinterest—not necessarily related to avoidance motivation; Goetz & Frenzel, [<reflink idref="bib23" id="ref113">23</reflink>]). As a preliminary step to our longitudinal analysis of boredom and interest, we therefore tested a one- and a two-factor analytic model against each other. In the two-factor model, the items of boredom and interest loaded on two separate but correlated factors, thereby empirically representing the assumption of two distinct constructs. The second model was a one-factor model, in which one factor explained the common variance of all items, representing the assumption that boredom and interest are the opposing characteristics of one common dimension. The prerequisite tests were conducted for each wave of measurement and subject separately.</p> <hd id="AN0183065736-15">The Developmental Interplay of Boredom and Interest</hd> <p>Our literature review revealed limited empirical findings for the longitudinal relations of boredom and interest (Pekrun, [<reflink idref="bib70" id="ref114">70</reflink>]; Pekrun et al., [<reflink idref="bib71" id="ref115">71</reflink>]; Schukajlow, [<reflink idref="bib96" id="ref116">96</reflink>]). Some of the results indicate that prior interest is negatively related to later boredom, but the effects of boredom on interest are still quite unclear. In our study, we therefore investigated the cross-lagged effects of boredom and interest and thereby, how individual differences in one variable can predict the change in individual differences in the other variable. We expected that higher interest at earlier time points would be related to average decreases in boredom at later time points (H1). As open research question, we explored how higher boredom at earlier time points is related to average changes in interest at later time points (RQ).</p> <hd id="AN0183065736-16">Method</hd> <p></p> <hd id="AN0183065736-17">Sample and Procedure</hd> <p>This study is a secondary analysis of a longitudinal research project (2005–2020) focusing on the motivational development of students in upper track secondary schools in Germany. After elementary school (grades 1 to 4), students in Germany are usually separated into one of three types of secondary school according to their level of academic achievement. Our study refers to the upper track (Gymnasium), which consists of another eight to nine grades and is attended by approximately 40% of the total student cohort (Federal Statistical Office [Statistisches Bundesamt], [<reflink idref="bib15" id="ref117">15</reflink>]).</p> <p>Students completed questionnaires at school over four waves of measurement, 1 month into 5th grade (T1, <emph>n</emph><subs>t1</subs> = 1026, 51.4% male, 48.6% female), as well as after midterm evaluations in 5th grade (T2, <emph>n</emph><subs>t2</subs> = 1044, 51.05% male, 48.85% female, 0.1% without entry), 6th grade (T3, <emph>n</emph><subs>t3</subs> = 1018, 51.4% male, 48.3% female, 0.3% without entry), and 8th grade (T4, <emph>n</emph><subs>t4</subs> = 1001, 49.7% male, 50.3% female). The total sample comprised <emph>N</emph> = 1471 students (53.4% male, 46.2% female, 0.4% without entry). Data were obtained from five different schools, located in two different federal states of Germany. Students' mean age was 10.67 years (<emph>SD</emph> = 0.44) at T1, 10.98 years (<emph>SD</emph> = 0.44) at T2, 12.01 years (<emph>SD</emph> = 0.44) at T3, and 13.97 years (<emph>SD</emph> = 0.45) at T4. Since the study was conducted entirely in German, we assessed German language proficiency. Students primarily had a German language background. For non-native speakers (10.3%), the mean time studying German was 7.55 years (<emph>SD</emph> = 2.33). We therefore assumed there were no language problems involved during data acquisition. To account for domain specificity and to test the robustness of our findings, we conducted all analyses in the domains of mathematics and German.</p> <hd id="AN0183065736-18">Variables and Measures</hd> <p></p> <hd id="AN0183065736-19">Boredom in Mathematics and German</hd> <p>We used two items from the 6-item boredom scale from the AEQ-M (Academic Emotions Questionnaire – Mathematics; Pekrun et al., [<reflink idref="bib75" id="ref118">75</reflink>]), which has been shown to have high structural and convergent validity (e.g. relations with achievement; Goetz et al., [<reflink idref="bib25" id="ref119">25</reflink>]). Items were as follows: 'I find [mathematics/German] to be boring' and 'I find it hard to stay awake during [mathematics/German] class out of sheer boredom'. Research documents that short scales can assess affective-motivational constructs in school settings reliably and validly (Gogol et al., [<reflink idref="bib31" id="ref120">31</reflink>]). The selection of the two items was based on factor loadings as an item-level index of internal item quality (Stanton et al., [<reflink idref="bib102" id="ref121">102</reflink>]). Students responded to the items on a 5-point Likert-type rating scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating higher levels of boredom. To assess the reliability of the scale, we used McDonald's omega, which delivers an unbiased estimation of reliability, as it takes item-specific measurement errors and the strength of association between items and construct into account (Hayes & Coutts, [<reflink idref="bib37" id="ref122">37</reflink>]). For details regarding item statistics (<emph>M</emph>, <emph>SD</emph>, Skewness, Kurtosis, item-total correlations, item wording), see Online Resource 1.</p> <hd id="AN0183065736-20">Interest in Mathematics and German</hd> <p>Students rated their interest in mathematics and German on a 3-item scale developed in the Project for the Analysis of Learning and Achievement in Mathematics (PALMA, see Pekrun et al., [<reflink idref="bib76" id="ref123">76</reflink>]). The scale covers interest in mathematics and German itself ('I am interested in [mathematics/German]', 'Engaging in [mathematics/German] is among my favourite activities'), and interest in mathematics and German classroom instruction ('I'm having fun in [mathematics/German] class'). Students once again responded on a Likert-type rating scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating a greater interest. As a measure of reliability, McDonald's omega (<emph>ω</emph>) was assessed here as well. For details regarding item statistics (<emph>M</emph>, <emph>SD</emph>, Skewness, Kurtosis, item-total correlations, item wording), see Online Resource 1.</p> <hd id="AN0183065736-21">Data Analyses</hd> <p>We used latent variables within a structural equation modelling (SEM) approach. All models were estimated using Mplus 8.10 (Muthén & Muthén, [<reflink idref="bib65" id="ref124">65</reflink>]−[<reflink idref="bib65" id="ref125">65</reflink>]). Estimation was set to full information maximum likelihood estimation with robust chi-squares and standard errors (MLR; see Hox et al., [<reflink idref="bib42" id="ref126">42</reflink>]). MLR estimation is robust to multivariate non-normality (Kaplan, [<reflink idref="bib46" id="ref127">46</reflink>]), treats Likert-type rating scale responses as continuous variables (Beauducel & Herzberg, [<reflink idref="bib4" id="ref128">4</reflink>]), and offers protection to unmodeled heterogeneity in multilevel data (Hox et al., [<reflink idref="bib42" id="ref129">42</reflink>]). Without hypotheses on school- or class-level variables, we used the 'type = complex' option in Mplus to account for the hierarchical nature of the data (Muthén & Muthén, [<reflink idref="bib66" id="ref130">66</reflink>]–[<reflink idref="bib66" id="ref131">66</reflink>]). Individual data was nested in different classrooms within different schools, yielding a total of 43 clusters. We used Mplus's full information maximum likelihood (FIML) algorithm to handle missing data on all outcome variables. An analysis of missing data patterns, using Mplus's 'patterns' output option (Muthén & Muthén, [<reflink idref="bib66" id="ref132">66</reflink>]–[<reflink idref="bib66" id="ref133">66</reflink>]) yields a covariance coverage of 48.9–72.1% (for items measuring boredom in mathematics) and 48.8–72.0% (boredom in German). For items assessing interest in mathematics, 47.5–72.3% of covariance was covered by the sample and 48.7–72.0% for interest in German. The FIML procedure has been shown to produce satisfactory estimates under all types of missing data patterns (Schlomer et al., [<reflink idref="bib94" id="ref134">94</reflink>]; see also Buhi et al., [<reflink idref="bib8" id="ref135">8</reflink>]) and is commonly used in SEM (Luedtke et al., [<reflink idref="bib55" id="ref136">55</reflink>]).</p> <hd id="AN0183065736-22">Confirmatory Factor Analyses</hd> <p>In preparation to our longitudinal SEM approach, testing the developmental interplay of boredom and interest, we first assessed the empirical separability of both constructs using confirmatory factor analyses (CFA). We used the effects coding method for model identification to obtain a meaningful scaling metric for latent variables (Little et al., [<reflink idref="bib54" id="ref137">54</reflink>]). Two-factor models with correlated factors were compared to single-factor models (with all five items loading on the same general factor), for mathematics and German separately. In model comparison, we used the respective comparative fit indexes (CFI), as well as Bayesian information criteria (BIC) at each wave of measurement, with higher CFI values and lower BIC values indicating better model fit. Satorra-Bentler scaled chi-square difference tests were conducted as well to compare the nested CFA models (Satorra & Bentler, [<reflink idref="bib88" id="ref138">88</reflink>]).</p> <hd id="AN0183065736-23">Measurement Invariance Testing</hd> <p>Invariant measurement of latent variables over time is an important prerequisite for longitudinal analyses. A level of at least metric measurement invariance is required for the interpretation of stability and cross-lagged path coefficients within latent cross-lagged models, which we used to address H1 and RQ. A level of at least scalar measurement invariance is needed to adequately interpret and compare latent factor means over time (Kleinke et al., [<reflink idref="bib48" id="ref139">48</reflink>]). We therefore established longitudinal measurement invariance of boredom and interest in the domains of mathematics and German using a step-up approach (Brown, [<reflink idref="bib6" id="ref140">6</reflink>]). Correlated uniqueness was specified for identical items over all four waves of measurement (Marsh & Hau, [<reflink idref="bib57" id="ref141">57</reflink>]). We used the effects coding method for model identification here as well that allows for cross-construct comparisons on the basis of scalar measurement invariance (Little et al., [<reflink idref="bib54" id="ref142">54</reflink>]).</p> <p>First, configural measurement invariance was tested by computing longitudinal CFA models for both boredom and interest for each subject. Metric invariance was tested by constraining factor loadings of corresponding items to be identical over all waves of measurement. Lastly, we tested scalar measurement invariance by further constraining corresponding item intercepts to be identical over time.</p> <p>To assess overall model fit, we turned to Hu and Bentler's ([<reflink idref="bib43" id="ref143">43</reflink>]) cut-off criteria for minimising type II error while keeping type I error at an acceptable rate (CFI close to 0.95, RMSEA close to 0.06, and SRMR close to 0.08). For pairwise comparisons between models of adjacent measurement invariance levels, we applied Chen's ([<reflink idref="bib10" id="ref144">10</reflink>]) recommendations on critical differences in goodness-of-fit indices (GFI). According to Chen ([<reflink idref="bib10" id="ref145">10</reflink>]), a ΔCFI < − 0.01 along with a ΔRMSEA of < 0.015 and a ΔSRMR < 0.03 between respective invariance models indicate metric invariance whereas a ΔCFI < − 0.01 along with a ΔRMSEA of < 0.015 and a ΔSRMR < 0.01 indicate scalar invariance. After testing longitudinal measuring invariance for both constructs in both domains, we integrated all four latent variables (boredom and interest in mathematics and German) into the same model.</p> <hd id="AN0183065736-24">Latent Cross-Lagged Models</hd> <p>To address H1 and RQ, we used latent cross-lagged models separately for mathematics and German (2-variable latent cross-lagged models), as well as in combination (4-variable latent cross-lagged model) with cross-lagged paths for boredom and interest at all four waves of measurement. First-order stability paths were evaluated as preliminary analyses. Correlated uniqueness was specified for identical items over all four waves of measurement. The method for model identification was effects coding (Little et al., [<reflink idref="bib54" id="ref146">54</reflink>]). The appropriate level of measurement invariance for each latent variable, resulting from measurement invariance testing, was included in model estimation. To investigate H1 and RQ, we inspected cross-lagged path coefficients. To assess overall model fit for both latent cross-lagged models, we once again evaluated model fit according to Hu and Bentler's ([<reflink idref="bib43" id="ref147">43</reflink>]) recommendations.</p> <hd id="AN0183065736-25">Results</hd> <p></p> <hd id="AN0183065736-26">Confirmatory Factor Analyses</hd> <p>At each wave of measurement, model fit for two-factor models with boredom and interest as separate but correlated factors was superior to the respective single-factor models for both mathematics and German (see Table 1). Specifically, CFI values were higher for two-factor models of boredom and interest than for single-factor models at all waves of measurement (mathematics: range of ΔCFI = 0.022–0.091; German: range of ΔCFI = 0.012–0.093). Similarly, BIC values for two-factor models were at all times lower than for single-factor models (mathematics: range of ΔBIC = − 51.970 to − 194.133; German: − 53.648 to − 207.758). The Satorra-Bentler scaled chi-square difference tests were statistically significant (<emph>p</emph> < 0.01; see Table 1), confirming that the fit of the two-factor models was better than that of the single-factor models. Boredom and interest therefore constituted correlated but separable factors. McDonald's <emph>ω</emph> was calculated for the latent factors of boredom and interest for each wave of measurement. The results were between <emph>ω</emph> = 0.79 and 0.85 and indicated acceptable to good reliability (see Table 1 for details). Within the two-factor models, negative correlations between boredom and interest increased over the four waves of measurement (mathematics: <emph>r</emph><subs>t1</subs> = − 0.67 to <emph>r</emph><subs>t4</subs> = − 0.87; German: <emph>r</emph><subs>t1</subs> = − 0.70 to <emph>r</emph><subs>t4</subs> = − 0.90). Measurement models showing standardised factor loadings are depicted in Fig. 1.</p> <p>Table 1 Comparison of model fit between single- and two-factor CFA models for boredom and academic interest</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2"><p>Model</p></th><th align="left" colspan="7"><p>Fit index</p></th><th align="left" colspan="2"><p>Reliability</p></th><th align="left" colspan="4"><p>Comparison</p></th></tr><tr><th align="left"><p><italic>χ</italic><sup>2</sup></p></th><th align="left"><p><italic>df</italic></p></th><th align="left"><p>SCF</p></th><th align="left"><p>CFI</p></th><th align="left"><p>RMSEA</p></th><th align="left"><p>SRMR</p></th><th align="left"><p>BIC</p></th><th align="left"><p><italic>ω</italic><sub>BO</sub></p></th><th align="left"><p><italic>ω</italic><sub>INT</sub></p></th><th align="left"><p>ΔCFI</p></th><th align="left"><p>ΔBIC</p></th><th align="left"><p>∆<italic>χ</italic><sup>2a</sup></p></th><th align="left"><p><italic>∆df</italic></p></th></tr></thead><tbody><tr><td align="left" colspan="14"><p> Mathematics</p></td></tr><tr><td align="left" colspan="14"><p>T1</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>116.74<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.7134</p></td><td align="left"><p>.90</p></td><td align="left"><p>.15</p></td><td align="left"><p>.06</p></td><td char="." align="char"><p>13,914.14</p></td><td align="left"><p>.89</p></td><td align="left" /><td align="left" rowspan="2"><p> −.09</p></td><td align="left" rowspan="2"><p> − 158.56</p></td><td align="left" rowspan="2"><p>146.79<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>18.57<sup>**</sup></p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.8599</p></td><td align="left"><p>.99</p></td><td align="left"><p>.06</p></td><td align="left"><p>.02</p></td><td char="." align="char"><p>13,755.59</p></td><td align="left"><p>.83</p></td><td align="left"><p>.85</p></td></tr><tr><td align="left" colspan="14"><p>T2</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>159.53<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.2690</p></td><td align="left"><p>.83</p></td><td align="left"><p>.17</p></td><td align="left"><p>.06</p></td><td char="." align="char"><p>14,238.70</p></td><td align="left"><p>.88</p></td><td align="left" /><td align="left" rowspan="2"><p> −.17</p></td><td align="left" rowspan="2"><p> − 193.13</p></td><td align="left" rowspan="2"><p> − 52.02</p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>1.36</p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.7390</p></td><td align="left"><p>1.00</p></td><td align="left"><p>.00</p></td><td align="left"><p>.01</p></td><td char="." align="char"><p>14,045.56</p></td><td align="left"><p>.84</p></td><td align="left"><p>.83</p></td></tr><tr><td align="left" colspan="14"><p>T3</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>176.01<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.6857</p></td><td align="left"><p>.89</p></td><td align="left"><p>.19</p></td><td align="left"><p>.06</p></td><td char="." align="char"><p>14,145.31</p></td><td align="left"><p>.88</p></td><td align="left" /><td align="left" rowspan="2"><p> −.07</p></td><td align="left" rowspan="2"><p> − 194.14</p></td><td align="left" rowspan="2"><p>90.38<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>61.67<sup>**</sup></p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.5510</p></td><td align="left"><p>.96</p></td><td align="left"><p>.12</p></td><td align="left"><p>.04</p></td><td char="." align="char"><p>13,951.17</p></td><td align="left"><p>.83</p></td><td align="left"><p>.84</p></td></tr><tr><td align="left" colspan="14"><p>T4</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>109.75<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.6103</p></td><td align="left"><p>.92</p></td><td align="left"><p>.15</p></td><td align="left"><p>.05</p></td><td char="." align="char"><p>13,631.59</p></td><td align="left"><p>.89</p></td><td align="left" /><td align="left" rowspan="2"><p> −.02</p></td><td align="left" rowspan="2"><p> − 51.97</p></td><td align="left" rowspan="2"><p>29.92<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>77.49<sup>**</sup></p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.5210</p></td><td align="left"><p>.95</p></td><td align="left"><p>.14</p></td><td align="left"><p>.04</p></td><td char="." align="char"><p>13,579.62</p></td><td align="left"><p>.79</p></td><td align="left"><p>.84</p></td></tr><tr><td align="left"><p> German</p></td><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" colspan="8" /><td align="left" /><td align="left" /></tr><tr><td align="left" colspan="14"><p>T1</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>123.91<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.9420</p></td><td align="left"><p>.89</p></td><td align="left"><p>.15</p></td><td align="left"><p>.07</p></td><td char="." align="char"><p>13,603.47</p></td><td align="left"><p>.90</p></td><td align="left" /><td align="left" rowspan="2"><p> −.09</p></td><td align="left" rowspan="2"><p> − 207.76</p></td><td align="left" rowspan="2"><p>46.09<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>20.55<sup>**</sup></p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.2629</p></td><td align="left"><p>.99</p></td><td align="left"><p>.06</p></td><td align="left"><p>.02</p></td><td char="." align="char"><p>13,395.72</p></td><td align="left"><p>.85</p></td><td align="left"><p>.85</p></td></tr><tr><td align="left" colspan="14"><p>T2</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>114.05<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.4828</p></td><td align="left"><p>.91</p></td><td align="left"><p>.15</p></td><td align="left"><p>.05</p></td><td char="." align="char"><p>14,068.00</p></td><td align="left"><p>.89</p></td><td align="left" /><td align="left" rowspan="2"><p> −.09</p></td><td align="left" rowspan="2"><p> − 150.72</p></td><td align="left" rowspan="2"><p>68.64<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>8.96</p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.2793</p></td><td align="left"><p>1.00</p></td><td align="left"><p>.04</p></td><td align="left"><p>.01</p></td><td char="." align="char"><p>13,917.28</p></td><td align="left"><p>.82</p></td><td align="left"><p>.84</p></td></tr><tr><td align="left" colspan="14"><p>T3</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>153.06<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.3285</p></td><td align="left"><p>.92</p></td><td align="left"><p>.17</p></td><td align="left"><p>.05</p></td><td char="." align="char"><p>13,911.43</p></td><td align="left"><p>.88</p></td><td align="left" /><td align="left" rowspan="2"><p> −.06</p></td><td align="left" rowspan="2"><p> − 150.30</p></td><td align="left" rowspan="2"><p>115.58<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>34.94<sup>**</sup></p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.3206</p></td><td align="left"><p>.98</p></td><td align="left"><p>.09</p></td><td align="left"><p>.03</p></td><td char="." align="char"><p>13,761.13</p></td><td align="left"><p>.83</p></td><td align="left"><p>.82</p></td></tr><tr><td align="left" colspan="14"><p>T4</p></td></tr><tr><td align="left"><p> Single-factor model</p></td><td char="." align="char"><p>70.45<sup>**</sup></p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.5872</p></td><td align="left"><p>.98</p></td><td align="left"><p>.12</p></td><td align="left"><p>.03</p></td><td char="." align="char"><p>13,313.10</p></td><td align="left"><p>.89</p></td><td align="left" /><td align="left" rowspan="2"><p> −.01</p></td><td align="left" rowspan="2"><p> − 53.65</p></td><td align="left" rowspan="2"><p>23.64<sup>**</sup></p></td><td align="left" rowspan="2"><p>1</p></td></tr><tr><td align="left"><p> Two-factor model</p></td><td char="." align="char"><p>38.17<sup>**</sup></p></td><td align="left"><p>4</p></td><td char="." align="char"><p>1.3440</p></td><td align="left"><p>.99</p></td><td align="left"><p>.09</p></td><td align="left"><p>.02</p></td><td char="." align="char"><p>13,259.45</p></td><td align="left"><p>.81</p></td><td align="left"><p>.83</p></td></tr></tbody></table> </ephtml> </p> <p>Sample sizes were <emph>n</emph><subs><emph>T1</emph></subs> = 1024, <emph>n</emph><subs><emph>T2</emph></subs> = 1032, <emph>n</emph><subs><emph>T3</emph></subs> = 1001, and <emph>n</emph><subs><emph>T4</emph></subs> = 986 (mathematics) and <emph>n</emph><subs><emph>T1</emph></subs> = 1022, <emph>n</emph><subs><emph>T2</emph></subs> = 1027, <emph>n</emph><subs><emph>T3</emph></subs> = 996, and <emph>n</emph><subs><emph>T4</emph></subs> = 978 (German). <emph>BO</emph>, latent boredom; <emph>INT</emph>, latent academic interest; <emph>χ</emph><sups><emph>2</emph></sups>, chi-square statistic; <emph>df</emph>, degrees of freedom; <emph>SCF</emph>, scaling correction factor, as computed by Mplus 8.10 (Muthén & Muthén, [<reflink idref="bib65" id="ref148">65</reflink>]–[<reflink idref="bib65" id="ref149">65</reflink>]); <emph>CFI</emph>, comparative fit index; <emph>RMSEA</emph>, random mean square error of approximation; <emph>SRMR</emph>, standardised root mean square residual; <emph>BIC</emph>, Bayesian information criterion. McDonald's <emph>ω</emph> was calculated as <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mfrac><msup><mfenced close=")" open="("><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>p</mi></msubsup><mspace width="0.277778em" /><msub><mi>λ</mi><mrow><mi mathvariant="italic">ij</mi></mrow></msub></mfenced><mn>2</mn></msup><mrow><msup><mfenced close=")" open="("><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>p</mi></msubsup><mspace width="0.277778em" /><msub><mi>λ</mi><mrow><mi mathvariant="italic">ij</mi></mrow></msub></mfenced><mn>2</mn></msup><mo>+</mo><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn><mspace width="0.277778em" /></mrow><mi>p</mi></msubsup><msub><mi>e</mi><mi>i</mi></msub></mrow></mfrac></math> </ephtml> , where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>λ</mi><mtext>ij</mtext></msub></math> </ephtml> is the standardised factor loading of item <emph>i</emph> on factor <emph>j</emph>, and <emph>e</emph><subs><emph>ij</emph></subs> is the standardised item residual for item <emph>i</emph> regarding factor <emph>j</emph> (Brunner et al., [<reflink idref="bib7" id="ref150">7</reflink>]; see also McDonald, [<reflink idref="bib60" id="ref151">60</reflink>]). <sups>**</sups><emph>p</emph> <.01. <sups>a</sups>Satorra-Bentler-scaled ∆<emph>χ</emph><sups>2</sups>; for calculation formula, see Satorra and Bentler ([<reflink idref="bib88" id="ref152">88</reflink>])</p> <p>Graph: Fig. 1 Standardised factor loadings for single (a, c) and two-factor CFA models (b, d) of latent boredom and academic interest. In b and d, standardised cross-construct correlations are displayed, as well. Item residuals and variances of latent variables are omitted. BO-M, latent boredom in mathematics (operationalised as general factor in a); INT-M, latent interest in mathematics; BO-G, latent boredom in German (operationalised as general factor in c); INT-G, latent interest in German. bo-[m/g]−1 = 'I find [mathematics/German] to be boring'. bo-[m/g]−2 = 'I find it hard to stay awake during [mathematics/German] class out of sheer boredom'. int-[m/g]−1 = 'I am interested in [mathematics/German]'. int-[m/g]−2 = 'I'm having fun in [mathematics/German] class'. int-[m/g]−3 = 'Engaging in [mathematics/German] is among my favourite activities'. For a comprehensive overview of GFI and sample sizes, see Table 1</p> <hd id="AN0183065736-27">Measurement Invariance Testing</hd> <p>All latent variables revealed at least metric measurement invariance (see Table 2). Beyond this, boredom showed scalar measurement invariance in both domains. For boredom in mathematics, the critical difference in RMSEA between the metric and the scalar measurement invariance model (ΔRMSEA < 0.015; see Chen, [<reflink idref="bib10" id="ref153">10</reflink>]) was surpassed (ΔRMSEA of = 0.020). It is noteworthy, however, that this relatively large difference was due to a very low RMSEA of 0.000 for the metric model. The resulting RMSEA of 0.020 for the scalar model indicated good model fit (Hu & Bentler, [<reflink idref="bib43" id="ref154">43</reflink>]). A level of scalar measurement invariance could also be inferred for interest in German (ΔCFI = − 0.005; ΔRMSEA = 0.005; ΔSRMR = 0.002), but not in mathematics (ΔCFI = − 0.082; ΔRMSEA = 0.038; ΔSRMR = 0.035). When the intercept equality restrictions were removed for two items at T2 of the scalar model, partially scalar measurement invariance could be established for interest in mathematics (see Table 2). Therefore, stability and cross-lagged path coefficients within the latent cross-lagged models, as well as mean-level changes in latent variables, could be computed and interpreted for both boredom and interest (Kleinke et al., [<reflink idref="bib48" id="ref155">48</reflink>]).</p> <p>Table 2 Investigation of longitudinal measurement invariance of latent variables for mathematics and German</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2"><p>Model</p></th><th align="left" rowspan="2"><p><italic>χ</italic><sup>2</sup></p></th><th align="left" rowspan="2"><p><italic>df</italic></p></th><th align="left" rowspan="2"><p>CFI</p></th><th align="left" rowspan="2"><p>RMSEA</p></th><th align="left" rowspan="2"><p>SRMR</p></th><th align="left" colspan="6"><p>Model comparison</p></th></tr><tr><th align="left"><p>Compare</p></th><th align="left"><p>∆<italic>χ</italic><sup>2a</sup></p></th><th align="left"><p><italic>∆df</italic></p></th><th align="left"><p><italic>∆</italic>CFI</p></th><th align="left"><p><italic>∆</italic>RMSEA</p></th><th align="left"><p><italic>∆</italic>SRMR</p></th></tr></thead><tbody><tr><td align="left" colspan="12"><p>Mathematics</p></td></tr><tr><td align="left" colspan="12"><p> Boredom</p></td></tr><tr><td align="left"><p> configural</p></td><td char="." align="char"><p>4.396</p></td><td align="left"><p>2</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.03</p></td><td align="left"><p>.01</p></td><td align="left" /><td char="." align="char" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> metric</p></td><td char="." align="char"><p>6.730</p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.02</p></td><td align="left"><p>.02</p></td><td align="left"><p>configural vs. metric</p></td><td char="." align="char"><p>1.72</p></td><td align="left"><p>3</p></td><td align="left"><p> +.001</p></td><td align="left"><p> −.013</p></td><td align="left"><p> +.003</p></td></tr><tr><td align="left"><p> scalar</p></td><td char="." align="char"><p>10.210</p></td><td align="left"><p>8</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.01</p></td><td align="left"><p>.02</p></td><td align="left"><p>metric vs. scalar</p></td><td char="." align="char"><p>3.22</p></td><td align="left"><p>3</p></td><td align="left"><p>.000</p></td><td align="left"><p> −.002</p></td><td align="left"><p> +.001</p></td></tr><tr><td align="left" colspan="12"><p> Interest</p></td></tr><tr><td align="left"><p> configural</p></td><td char="." align="char"><p>123.404<sup>**</sup></p></td><td align="left"><p>30</p></td><td char="." align="char"><p>.98</p></td><td align="left"><p>.05</p></td><td align="left"><p>.03</p></td><td align="left" /><td char="." align="char" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> metric</p></td><td char="." align="char"><p>150.604<sup>**</sup></p></td><td align="left"><p>36</p></td><td char="." align="char"><p>.97</p></td><td align="left"><p>.05</p></td><td align="left"><p>.04</p></td><td align="left"><p>configural vs. metric</p></td><td char="." align="char"><p>27.15<sup>**</sup></p></td><td align="left"><p>6</p></td><td align="left"><p> −.005</p></td><td align="left"><p>.000</p></td><td align="left"><p> +.011</p></td></tr><tr><td align="left"><p> scalar</p></td><td char="." align="char"><p>476.768<sup>**</sup></p></td><td align="left"><p>42</p></td><td char="." align="char"><p>.89</p></td><td align="left"><p>.09</p></td><td align="left"><p>.08</p></td><td align="left"><p>metric vs. scalar</p></td><td char="." align="char"><p>190.30<sup>**</sup></p></td><td align="left"><p>6</p></td><td align="left"><p> −.082</p></td><td align="left"><p> +.038</p></td><td align="left"><p> +.035</p></td></tr><tr><td align="left"><p> partially scalar<sup>b</sup></p></td><td char="." align="char"><p>184.942<sup>**</sup></p></td><td align="left"><p>40</p></td><td char="." align="char"><p>.96</p></td><td align="left"><p>.05</p></td><td align="left"><p>.04</p></td><td align="left"><p>metric vs. partially scalar</p></td><td char="." align="char"><p>29.10<sup>**</sup></p></td><td align="left"><p>4</p></td><td align="left"><p> −.008</p></td><td align="left"><p> +.004</p></td><td align="left"><p> −.001</p></td></tr><tr><td align="left" colspan="12"><p>German</p></td></tr><tr><td align="left" colspan="12"><p> Boredom</p></td></tr><tr><td align="left"><p> configural</p></td><td char="." align="char"><p>1.435</p></td><td align="left"><p>2</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.00</p></td><td align="left"><p>.01</p></td><td align="left" /><td char="." align="char" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> metric</p></td><td char="." align="char"><p>2.837</p></td><td align="left"><p>5</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.00</p></td><td align="left"><p>.01</p></td><td align="left"><p>configural. vs. metric</p></td><td char="." align="char"><p>1.82</p></td><td align="left"><p>3</p></td><td align="left"><p>.000</p></td><td align="left"><p>.000</p></td><td align="left"><p> +.001</p></td></tr><tr><td align="left"><p> scalar</p></td><td char="." align="char"><p>12.439</p></td><td align="left"><p>8</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.02</p></td><td align="left"><p>.01</p></td><td align="left"><p>metric vs. scalar</p></td><td char="." align="char"><p>14.16<sup>**</sup></p></td><td align="left"><p>3</p></td><td align="left"><p> −.002</p></td><td align="left"><p> +.020</p></td><td align="left"><p> +.003</p></td></tr><tr><td align="left" colspan="12"><p> Interest</p></td></tr><tr><td align="left"><p> configural</p></td><td char="." align="char"><p>50.354<sup>*</sup></p></td><td align="left"><p>30</p></td><td char="." align="char"><p>1.00</p></td><td align="left"><p>.02</p></td><td align="left"><p>.02</p></td><td align="left" /><td char="." align="char" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> metric</p></td><td char="." align="char"><p>68.845<sup>**</sup></p></td><td align="left"><p>36</p></td><td char="." align="char"><p>.99</p></td><td align="left"><p>.03</p></td><td align="left"><p>.03</p></td><td align="left"><p>configural. vs. metric</p></td><td char="." align="char"><p>19.40<sup>**</sup></p></td><td align="left"><p>6</p></td><td align="left"><p> −.003</p></td><td align="left"><p> +.003</p></td><td align="left"><p> +.007</p></td></tr><tr><td align="left"><p> scalar</p></td><td char="." align="char"><p>96.829<sup>**</sup></p></td><td align="left"><p>42</p></td><td char="." align="char"><p>.99</p></td><td align="left"><p>.03</p></td><td align="left"><p>.03</p></td><td align="left"><p>metric vs. scalar</p></td><td char="." align="char"><p>27.41<sup>**</sup></p></td><td align="left"><p>6</p></td><td align="left"><p> −.005</p></td><td align="left"><p> +.005</p></td><td align="left"><p> +.002</p></td></tr></tbody></table> </ephtml> </p> <p>Sample sizes were <emph>n</emph><subs>Boredom</subs> = 1420 and <emph>n</emph><subs>Interest</subs> = 1420 for mathematics and <emph>n</emph><subs>Boredom</subs> = 1417 and <emph>n</emph><subs>Interest</subs> = 1418 for German. <emph>χ</emph><sups>2</sups>, chi-square statistic; <emph>df</emph>, degrees of freedom; <emph>CFI</emph>, comparative fit index; <emph>RMSEA</emph>, random mean square error of approximation; <emph>SRMR</emph>, standardised root mean square residual. <sups>*</sups><emph>p</emph> <.05. <sups>**</sups><emph>p</emph> <.01. <sups>a</sups>Satorra-Bentler-scaled ∆<emph>χ</emph><sups>2</sups>; for calculation formula, see Satorra and Bentler ([<reflink idref="bib88" id="ref156">88</reflink>]). <sups>b</sups>Model of partially scalar measurement invariance: intercepts for item 2 ('I'm having fun in mathematics class') and item 3 ('Engaging in mathematics is among my favourite activities') at T2 were estimated free of constraints</p> <p>Latent means and correlations were taken from a combined longitudinal model, featuring both domains of mathematics and German, with correlated latent factors and expanded longitudinal correlated uniqueness assumptions for item indicators based on the respective constraints from measurement invariance testing. The model showed a good model fit (<emph>χ</emph><sups>2</sups> = 1,424.065, <emph>df</emph> = 574, <emph>p</emph> = 0.000, CFI = 0.952, RMSEA = 0.032, SRMR = 0.045; see also Table 3). Table 4 shows latent correlations from the combined model for both domains as well as estimated latent means and standard errors.</p> <p>Table 3 Model fits for main analytical models</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Model</p></th><th align="left"><p><italic>N</italic></p></th><th align="left"><p><italic>χ</italic><sup>2</sup></p></th><th align="left"><p>SCF</p></th><th align="left"><p><italic>df</italic></p></th><th align="left"><p>CFI</p></th><th align="left"><p>RMSEA</p></th><th align="left"><p>SRMR</p></th></tr></thead><tbody><tr><td align="left"><p>2-V. Cross-lagged Model, Math<sup>b</sup></p></td><td align="left"><p>1420</p></td><td align="left"><p>557.721<sup>**</sup></p></td><td align="left"><p>1.2496</p></td><td align="left"><p>140</p></td><td align="left"><p>.946</p></td><td align="left"><p>.046</p></td><td align="left"><p>.053</p></td></tr><tr><td align="left"><p>2-V. Cross-lagged Model, German</p></td><td align="left"><p>1418</p></td><td align="left"><p>334.161<sup>**</sup></p></td><td align="left"><p>1.1970</p></td><td align="left"><p>142</p></td><td align="left"><p>.977</p></td><td align="left"><p>.031</p></td><td align="left"><p>.045</p></td></tr><tr><td align="left"><p>4-V. Longitudinal CFA, combined</p></td><td align="left"><p>1421</p></td><td align="left"><p>1,424.065<sup>***</sup></p></td><td align="left"><p>1,1660</p></td><td align="left"><p>574</p></td><td align="left"><p>.952</p></td><td align="left"><p>.032</p></td><td align="left"><p>.045</p></td></tr><tr><td align="left"><p>4-V. Cross-lagged Model, combined</p></td><td align="left"><p>1421</p></td><td align="left"><p>1,612.486<sup>***</sup></p></td><td align="left"><p>1.1637</p></td><td align="left"><p>626</p></td><td align="left"><p>.944</p></td><td align="left"><p>.033</p></td><td align="left"><p>.054</p></td></tr></tbody></table> </ephtml> </p> <p>2-V. Cross-lagged Model, Math = 2-variable latent cross-lagged model for mathematics. 2-V. Cross-lagged Model, German = 2-variable latent cross-lagged model for mathematics. 4-V. Longitudinal CFA, combined = 4-variable longitudinal measurement invariance model for mathematics and German. 4-V. Cross-lagged Model, combined = 4-variable latent cross-lagged model for mathematics and German. <sups>**</sups><emph>p</emph> <.01. <sups>***</sups><emph>p</emph> <.001. <sups>b</sups>Model of partially scalar measurement invariance: intercepts for item 2 ('I'm having fun in mathematics class') and item 3 ('Engaging in mathematics is among my favourite activities') at T2 were estimated free of constraints</p> <p>Table 4 Latent means, standard errors, and correlations for all constructs from longitudinal CFAs with correlated uniqueness and (partially) scalar measurement invariance over time (N = 1421)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p><italic>M</italic></p></th><th align="left"><p><italic>SE</italic></p></th><th align="left"><p>BO-G@T1</p></th><th align="left"><p>BO-G@T2</p></th><th align="left"><p>BO-G@T3</p></th><th align="left"><p>BO-G@T4</p></th><th align="left"><p>INT-G@T1</p></th><th align="left"><p>INT-G@T2</p></th><th align="left"><p>INT-G@T3</p></th><th align="left"><p>INT-G@T4</p></th><th align="left"><p>BO-M@T1</p></th><th align="left"><p>BO-M@T2</p></th><th align="left"><p>BO-M@T3</p></th><th align="left"><p>BO-M@T4</p></th><th align="left"><p>INT-M@T1</p></th><th align="left"><p>INT-M@T2</p></th><th align="left"><p>INT-M@T3</p></th><th align="left"><p>INT-M@T4</p></th></tr></thead><tbody><tr><td align="left"><p>BO-G@T1</p></td><td char="." align="char"><p>2.43</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-G@T2</p></td><td char="." align="char"><p>2.58</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p><bold>0.515</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-G@T3</p></td><td char="." align="char"><p>2.87</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p><bold>0.297</bold></p></td><td char="." align="char"><p><bold>0.403</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-G@T4</p></td><td char="." align="char"><p>3.04</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p><bold>0.220</bold></p></td><td char="." align="char"><p><bold>0.177</bold></p></td><td char="." align="char"><p><bold>0.374</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-G@T1</p></td><td char="." align="char"><p>5.46</p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p><bold> − 0.711</bold></p></td><td char="." align="char"><p><bold> − 0.470</bold></p></td><td char="." align="char"><p><bold> − 0.342</bold></p></td><td char="." align="char"><p><bold> − 0.300</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-G@T2</p></td><td char="." align="char"><p>5.31</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p><bold> − 0.512</bold></p></td><td char="." align="char"><p><bold> − 0.758</bold></p></td><td char="." align="char"><p><bold> − 0.435</bold></p></td><td char="." align="char"><p><bold> − 0.257</bold></p></td><td char="." align="char"><p><bold>0.682</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-G@T3</p></td><td char="." align="char"><p>4.94</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p><bold> − 0.299</bold></p></td><td char="." align="char"><p><bold> − 0.406</bold></p></td><td char="." align="char"><p><bold> − 0.819</bold></p></td><td char="." align="char"><p><bold> − 0.352</bold></p></td><td char="." align="char"><p><bold>0.528</bold></p></td><td char="." align="char"><p><bold>0.572</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-G@T4</p></td><td char="." align="char"><p>4.86</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p><bold> − 0.155</bold></p></td><td char="." align="char"><p><bold> − 0.121</bold></p></td><td char="." align="char"><p><bold> − 0.253</bold></p></td><td char="." align="char"><p><bold> − 0.894</bold></p></td><td char="." align="char"><p><bold>0.384</bold></p></td><td char="." align="char"><p><bold>0.311</bold></p></td><td char="." align="char"><p><bold>0.413</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-M@T1</p></td><td char="." align="char"><p>0.80</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p><bold>0.324</bold></p></td><td char="." align="char"><p><bold>0.282</bold></p></td><td char="." align="char"><p><bold>0.080</bold></p></td><td char="." align="char"><p> − 0.033</p></td><td char="." align="char"><p><bold> − 0.221</bold></p></td><td char="." align="char"><p><bold> − 0.174</bold></p></td><td char="." align="char"><p> − 0.022</p></td><td char="." align="char"><p>0.073</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-M@T2</p></td><td char="." align="char"><p>0.85</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p><bold>0.205</bold></p></td><td char="." align="char"><p><bold>0.433</bold></p></td><td char="." align="char"><p><bold>0.180</bold></p></td><td char="." align="char"><p>0.045</p></td><td char="." align="char"><p><bold> − 0.188</bold></p></td><td char="." align="char"><p><bold> − 0.210</bold></p></td><td char="." align="char"><p> − 0.065</p></td><td char="." align="char"><p>0.006</p></td><td char="." align="char"><p><bold>0.601</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-M@T3</p></td><td char="." align="char"><p>1.09</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p><bold>0.152</bold></p></td><td char="." align="char"><p><bold>0.214</bold></p></td><td char="." align="char"><p><bold>0.284</bold></p></td><td char="." align="char"><p>0.039</p></td><td char="." align="char"><p><bold> − 0.144</bold></p></td><td char="." align="char"><p><bold> − 0.169</bold></p></td><td char="." align="char"><p><bold> − 0.185</bold></p></td><td char="." align="char"><p>0.019</p></td><td char="." align="char"><p><bold>0.375</bold></p></td><td char="." align="char"><p><bold>0.409</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>BO-M@T4</p></td><td char="." align="char"><p>1.53</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.041</p></td><td char="." align="char"><p><bold>0.132</bold></p></td><td char="." align="char"><p><bold>0.138</bold></p></td><td char="." align="char"><p><bold>0.182</bold></p></td><td char="." align="char"><p> − 0.037</p></td><td char="." align="char"><p> − 0.071</p></td><td char="." align="char"><p><bold> − 0.122</bold></p></td><td char="." align="char"><p><bold> − 0.113</bold></p></td><td char="." align="char"><p><bold>0.224</bold></p></td><td char="." align="char"><p><bold>0.332</bold></p></td><td char="." align="char"><p><bold>0.383</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-M@T1</p></td><td char="." align="char"><p>2.63</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p><bold> − 0.150</bold></p></td><td char="." align="char"><p><bold> − 0.138</bold></p></td><td char="." align="char"><p> − 0.049</p></td><td char="." align="char"><p> − 0.021</p></td><td char="." align="char"><p><bold>0.239</bold></p></td><td char="." align="char"><p><bold>0.162</bold></p></td><td char="." align="char"><p><bold>0.071</bold></p></td><td char="." align="char"><p>0.031</p></td><td char="." align="char"><p><bold> − 0.728</bold></p></td><td char="." align="char"><p><bold> − 0.533</bold></p></td><td char="." align="char"><p><bold> − 0.376</bold></p></td><td char="." align="char"><p><bold> − 0.333</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-M@T2</p></td><td char="." align="char"><p>2.47</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p><bold> − 0.139</bold></p></td><td char="." align="char"><p><bold> − 0.176</bold></p></td><td char="." align="char"><p><bold> − 0.116</bold></p></td><td char="." align="char"><p> − 0.001</p></td><td char="." align="char"><p><bold>0.212</bold></p></td><td char="." align="char"><p><bold>0.203</bold></p></td><td char="." align="char"><p><bold>0.093</bold></p></td><td char="." align="char"><p>0.002</p></td><td char="." align="char"><p><bold> − 0.527</bold></p></td><td char="." align="char"><p><bold> − 0.747</bold></p></td><td char="." align="char"><p><bold> − 0.434</bold></p></td><td char="." align="char"><p><bold> − 0.330</bold></p></td><td char="." align="char"><p><bold>0.766</bold></p></td><td char="." align="char" /><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-M@T3</p></td><td char="." align="char"><p>2.21</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p> − 0.082</p></td><td char="." align="char"><p><bold> − 0.110</bold></p></td><td char="." align="char"><p><bold> − 0.102</bold></p></td><td char="." align="char"><p>0.021</p></td><td char="." align="char"><p><bold>0.168</bold></p></td><td char="." align="char"><p><bold>0.134</bold></p></td><td char="." align="char"><p><bold>0.179</bold></p></td><td char="." align="char"><p>0.013</p></td><td char="." align="char"><p><bold> − 0.335</bold></p></td><td char="." align="char"><p><bold> − 0.360</bold></p></td><td char="." align="char"><p><bold> − 0.782</bold></p></td><td char="." align="char"><p><bold> − 0.436</bold></p></td><td char="." align="char"><p><bold>0.511</bold></p></td><td char="." align="char"><p><bold>0.612</bold></p></td><td char="." align="char" /><td align="left" /></tr><tr><td align="left"><p>INT-M@T4</p></td><td char="." align="char"><p>1.74</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p> − 0.002</p></td><td char="." align="char"><p> − 0.017</p></td><td char="." align="char"><p> − 0.039</p></td><td char="." align="char"><p> − 0.059</p></td><td char="." align="char"><p>0.011</p></td><td char="." align="char"><p>0.010</p></td><td char="." align="char"><p><bold>0.096</bold></p></td><td char="." align="char"><p>0.096</p></td><td char="." align="char"><p><bold> − 0.252</bold></p></td><td char="." align="char"><p><bold> − 0.295</bold></p></td><td char="." align="char"><p><bold> − 0.362</bold></p></td><td char="." align="char"><p><bold> − 0.888</bold></p></td><td char="." align="char"><p><bold>0.423</bold></p></td><td char="." align="char"><p><bold>0.474</bold></p></td><td char="." align="char"><p><bold>0.551</bold></p></td><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Correlations in boldface are significant at an <emph>α</emph>-level of.05. <emph>BO-G/-M</emph>, boredom in German/mathematics; <emph>INT-G/-M</emph>, interest in German/mathematics. T1–T4 represent waves of measurement</p> <p>Within subject domains, all latent construct correlations were significant at an <emph>α</emph>-level of 0.05, with generally higher absolute correlations within mathematics (0.23 ≤|<emph>ρ</emph>|≤ 0.89) compared to German (0.12 ≤|<emph>ρ</emph>|≤ 0.89). Negative associations between same-domain interest and boredom (German: − 0.89 ≤ <emph>ρ</emph> ≤ − 0.12; mathematics: − 0.89 ≤ <emph>ρ</emph> ≤ − 0.25) were generally stronger than positive associations between same-domain same-construct associations (German: 0.18 ≤ <emph>ρ</emph> ≤ 0.68; mathematics: 0.23 ≤ <emph>ρ</emph> ≤ 0.77). Between subject domains, not all correlations reached statistical significance. Boredom-boredom correlations were overall highest in absolute values (0.03 ≤|<emph>ρ</emph>|≤ 0.43), followed by interest-interest correlations (0.00 ≤|<emph>ρ</emph>|≤ 0.24) and mostly negative correlations between German interest and mathematics boredom (0.01 ≤|<emph>ρ</emph>|≤ 0.22). Mathematics interest and German boredom showed weaker associations, most of them negative (0.00 ≤|<emph>ρ</emph>|≤ 0.18).</p> <p>Our combined measurement invariance model for both domains showed tendencies for subject-specific boredom to increase, and subject-specific interest to decrease over time. The estimated latent mean values of boredom steadily inclined from T1 to T4 (German: <emph>M</emph><subs>BO-G[T1]</subs> = 2.43, <emph>M</emph><subs>BO-G[T2]</subs> = 2.58, <emph>M</emph><subs>BO-G[T3]</subs> = 2.87, <emph>M</emph><subs>BO-G[T4]</subs> = 3.04; mathematics: <emph>M</emph><subs>BO-M[T1]</subs> = 0.80, <emph>M</emph><subs>BO-M[T2]</subs> = 0.85, <emph>M</emph><subs>BO-M[T3]</subs> = 1.09, <emph>M</emph><subs>BO-M[T4]</subs> = 1.53). Interest means declined from T1 to T4 (German: <emph>M</emph><subs>INT-G[T1]</subs> = 5.46, <emph>M</emph><subs>INT-G[T2]</subs> = 5.31, <emph>M</emph><subs>INT-G[T3]</subs> = 4.94, <emph>M</emph><subs>INT-G[T4]</subs> = 4.86; mathematics: <emph>M</emph><subs>INT-M[T1]</subs> = 2.63, <emph>M</emph><subs>INT-M[T2]</subs> = 2.47, <emph>M</emph><subs>INT-M[T3]</subs> = 2.21, <emph>M</emph><subs>INT-M[T4]</subs> = 1.74). Mean values of boredom and interest were consistently higher in German compared to mathematics.</p> <hd id="AN0183065736-28">Developmental Interplay of Interest and Boredom: Latent Cross-Lagged Model Results</hd> <p>Overall model fit was good for the 2-variable latent cross-lagged models (mathematics: <emph>χ</emph><sups>2</sups> = 557.721, <emph>df</emph> = 140, <emph>p</emph> = 0.000, CFI = 0.946, RMSEA = 0.046, SRMR = 0.053; German: <emph>χ</emph><sups>2</sups> = 334.161, <emph>df</emph> = 142, <emph>p</emph> = 0.000, CFI = 0.977, RMSEA = 0.031, SRMR = 0.045), as well as the combined 4-variable latent cross-lagged model (<emph>χ</emph><sups>2</sups> = 1,612.486, <emph>df</emph> = 626, <emph>p</emph> = 0.000, CFI = 0.944, RMSEA = 0.033, SRMR = 0.054). The significant <emph>χ</emph><sups>2</sups>-statistics were due to the large sample sizes (<emph>n</emph><subs>mathematics</subs> = 1420, <emph>n</emph><subs>German</subs> = 1418, <emph>n</emph><subs>combined</subs> = 1421). Standardised latent stability and cross-lagged path coefficients, using Mplus's STDYX standardisation (Muthén & Muthén, [<reflink idref="bib66" id="ref157">66</reflink>]−[<reflink idref="bib66" id="ref158">66</reflink>]), are depicted in Fig. 2 for 2-variable latent cross-lagged models and in Fig. 3 for the 4-variable latent cross-lagged model.</p> <p>Graph: Fig. 2 Standardised stability and cross-lagged path coefficients for 2-variable latent cross-lagged models across T1–T4 in German (a) and mathematics (b). Measurement models and cross-construct correlations at respective waves of measurement are omitted. Significant paths (i.e. p <.05) in boldface. Model-estimated latent means and standard errors are depicted inside latent variable circles. Circle outlines indicate explained variance (R2). BO-G, boredom in German; INT-G, interest in German; BO-M, boredom in mathematics; INT-M, interest in Mathematics. T1–T4 represent waves of measurement</p> <p>Graph: Fig. 3 Standardised stability and cross-lagged path coefficients for 4-variable latent cross-lagged model across T1–T4. Measurement models and cross-construct correlations at respective waves of measurement are omitted. Significant paths (i.e. p <.05) in boldface. Model-estimated latent means and standard errors are depicted inside latent variable circles. Circle outlines indicate explained variance (R2). BO-G, boredom in German; INT-G, interest in German; BO-M, boredom in mathematics; INT-M, interest in mathematics. T1–T4 represent waves of measurement</p> <hd id="AN0183065736-29">Construct Stability</hd> <p>In the mathematics model, interest displayed a generally much higher level of stability than boredom (boredom: <emph>β</emph><subs>BO-M[T1] →BO-M[T2]</subs> = 0.53, <emph>β</emph><subs>BO-M[T2] →BO-M[T3]</subs> = 0.32, <emph>β</emph><subs>BO-M[T3] →BO-M[T4]</subs> = 0.26; interest: <emph>β</emph><subs>INT-M[T1] →INT-M[T2]</subs> = 0.80, <emph>β</emph><subs>INT-M[T2] →INT-M[T3]</subs> = 0.76, <emph>β</emph><subs>INT-M[T3] →INT-M[T4]</subs> = 0.71). Stability for both boredom and interest declined over the four waves of measurement. All stability path coefficients for the mathematics model were significant.</p> <p>In the German model, interest was also more stable than boredom. Stability of boredom also decreased over time (<emph>β</emph><subs>BO-G[T1] →BO-G[T2]</subs> = 0.41, <emph>β</emph><subs>BO-G[T2] →BO-G[T3]</subs> = 0.23, <emph>β</emph><subs>BO-G[T3] →BO-G[T4]</subs> = 0.24). Interest in German displayed a different trajectory regarding stability, increasing from T1 to T3 (<emph>β</emph><subs>INT-G[T1] →INT-G[T2]</subs> = 0.68, <emph>β</emph><subs>INT-G[T2] →INT-G[T3]</subs> = 0.74) and remaining nearly constant from T3 to T4 (<emph>β</emph><subs>INT-G[T3] →INT-G[T4]</subs> = 0.71).</p> <p>In the combined 4-variable latent cross-lagged model for both domains, interest showed high and increasing stability coefficients over time for both domains (German: 0.71 ≤ <emph>β</emph> ≤ 0.76; mathematics: 0.78 ≤ <emph>β</emph> ≤ 0.88). Boredom, on the other the hand, only showed significant stability from T1 to T2 in both domains (<emph>β</emph><subs>BO-G[T1] →BO-G[T2]</subs> = 0.29, <emph>β</emph><subs>BO-M[T1] →BO-M[T2]</subs> = 0.34). Subsequent stability paths for German boredom were of moderate size (cf. Keith, [<reflink idref="bib47" id="ref159">47</reflink>]) but did not reach statistical significance (<emph>β</emph><subs>BO-G[T2] →BO-G[T3]</subs> = 0.18, <emph>β</emph><subs>BO-G[T3] →BO-G[T4]</subs> = 0.17).</p> <hd id="AN0183065736-30">Cross-Lagged Relations</hd> <p>For mathematics, earlier interest consistently predicted changes in later boredom. That is, higher levels of interest at an earlier stage were related to decreases in the levels of boredom at later times (<emph>β</emph><subs>INT-M[T1] →BO-M[T2]</subs> = − 0.14; <emph>β</emph><subs>INT-M[T2] →BO-M[T3]</subs> = − 0.22; <emph>β</emph><subs>INT-M[T3] →BO-M[T4]</subs> = − 0.24). Effect sizes for this inverse relationship between interest and boredom increased over time. Interestingly, earlier boredom was associated with positive changes in later interest (<emph>β</emph><subs>BO-M[T1] →INT-M[T2]</subs> = 0.04; <emph>β</emph><subs>BO-M[T2] →INT-M[T3]</subs> = 0.17; <emph>β</emph><subs>BO-M[T3] →INT-M[T4]</subs> = 0.17). Individuals with higher boredom scores relative to others at an earlier time point on average increased in their interest at a later time point. However, the effect size of the cross-lagged path coefficient between boredom at T1 and interest at T2 was negligible and not statistically significant (<emph>p</emph> = 0.407).</p> <p>In German, there was also an inverse relationship between earlier interest and changes in later boredom (<emph>β</emph><subs>INT-G[T1] →BO-G[T2]</subs> = − 0.18; <emph>β</emph><subs>INT-G[T2] →BO-G[T3]</subs> = − 0.28; <emph>β</emph><subs>INT-G[T3] →BO-G[T4]</subs> = − 0.19), meaning that high levels of interest in German at an earlier point of measurement were related to decreases in boredom at a later point of measurement. Prediction of boredom at T4 by interest at T3 was not significant, however (<emph>p</emph> = 0.101). As in mathematics, earlier boredom in German was associated with positive changes in later interest (<emph>β</emph><subs>BO-G[T2] →INT-G[T3]</subs> = 0.17; <emph>β</emph><subs>BO-G[T3] →INT-G[T4]</subs> = 0.33), except for the cross-lagged path between T1 and T2 (<emph>β</emph><subs>BO-G[T1] →INT-G[T2]</subs> = − 0.03, <emph>p</emph> = 0.614). The prediction of interest in German at T3 by earlier boredom in German at T2 was not significant (<emph>p</emph> = 0.054).</p> <p>Cross-lagged relation patterns from the combined 4-variable latent cross-lagged model for both domains mirrored results from the 2-variable latent cross-lagged models with a few exceptions. German boredom at T2 was significantly predicted by all study variables at T1 (<emph>β</emph><subs>BO-G[T1] →BO-G[T2]</subs> = 0.29, <emph>β</emph><subs>INT-G[T1] →BO-G[T2]</subs> = − 0.24, <emph>β</emph><subs>BO-M[T1] →BO-G[T2]</subs> = 0.34, <emph>β</emph><subs>INT-M[T1] →BO-G[T2]</subs> = 0.21). Interest in mathematics positively predicted changes in boredom in German from T1 to T2. Mathematics interest at T1 no longer predicted mathematics boredom at T2 significantly (<emph>β</emph> = − 0.06, n.s.). Taken together, except for both waves of measurement in grade 5, there was limited evidence of cross-domain predictive patterns.</p> <p>In sum, regarding our hypothesis H1 and our research question, the following picture emerges: As expected in H1, earlier interest was significantly and inversely related to changes in later boredom, except for German between T3 and T4. For both models, earlier boredom was associated with positive changes in later interest throughout most waves. Changes in interest in both subjects at T2 could not be predicted by boredom at T1.</p> <hd id="AN0183065736-31">Discussion</hd> <p>The aim of the present study was to investigate the relationship between academic boredom and interest and to clarify their developmental interplay over the time span of the first 4 years of secondary school. We assessed boredom and interest in math and German in a large sample of secondary school students. The findings suggest that boredom and interest are empirically distinct. Confirmatory analyses revealed a superior model fit for two- vs. one-factor models of boredom and interest. Both constructs demonstrated different stabilities in the longitudinal model with boredom showing very low and interest showing high stabilities over time. These findings support that boredom and interest are related but separable constructs. Findings from latent cross-lagged models showed inverse relationships between earlier interest and later boredom in both domains, in line with our hypothesis (H1). Surprisingly, earlier boredom predicted an increase in later interest from the second measurement point onward. Results were robust for the two domains. When the two domains were integrated into one single model, the results were largely consistent with the findings from the individual domains. There was minimal evidence to suggest any between-domain relations.</p> <hd id="AN0183065736-32">Relationship Between Boredom and Interest</hd> <p>Our confirmatory factor analyses and our cross-lagged panel models offered some valuable insights into the relationship between boredom and interest. The results of the confirmatory factor analyses at all four measurement points indicated a preference for two separate models of boredom and interest. These findings are consistent with the theoretical argument that boredom is not simply an emotionally neutral state as defined by a lack of interest, but a unique emotional experience with a negative valence (Pekrun et al., [<reflink idref="bib71" id="ref160">71</reflink>]). While a lack of interest may be an important antecedent of boredom, as indicated by the substantial negative correlations between both factors in the two-factor model and our longitudinal findings, lack of interest is also associated with an unwillingness to engage in an achievement activity or a task and can be affectively neutral. Boredom, on the other hand, is mostly experienced as unpleasant and leads to a desire to escape from the situation (i.e. avoidance motivation).</p> <p>Interestingly, the negative correlation between the boredom and interest factor in the two-factor model increased over the four measurement points. This suggests that the two constructs do not develop in parallel but rather increasingly diverge in their extent of oppositeness during secondary school. In light of this finding, it is important to distinguish between the statistical and the content level. While the statistical dependency of boredom and interest (i.e. their correlation) increases, they become more opposed in terms of content. This is in accordance with the differential distinctiveness hypothesis, which assumes that motivational factors that are initially differentiated become more differentiated with age and cognitive development, whereas initially closely linked and less differentiated constructs tend to decrease less in their correlations or even become more positively correlated (Marsh & Ayotte, [<reflink idref="bib56" id="ref161">56</reflink>]). Originating initially from self-concept research, this hypothesis was corroborated by Goetz and colleagues ([<reflink idref="bib25" id="ref162">25</reflink>]) for between- and within-domain relations of different academic emotions, including boredom.</p> <p>Additionally, our longitudinal findings revealed a more constant influence of interest on boredom than in reverse. This indicates that the effects are not exactly inverse, which would have been expected if boredom was simply a low value on the interest scale. Furthermore, stabilities of both constructs differed and developed differently with lower and decreasing stabilities for boredom and rather high and comparable stabilities for interest. This finding aligns with prior research (Gottfried et al., [<reflink idref="bib33" id="ref163">33</reflink>]; Pekrun et al., [<reflink idref="bib73" id="ref164">73</reflink>]). It suggests that boredom and interest are not two opposing sides of a single construct. Otherwise, we would have expected similar stabilities.</p> <p>The empirical separability of boredom and interest is a complex issue that has yet to be fully resolved. It is also part of a much larger debate about the dimensionality of certain psychological constructs. The debate centres on the question whether closely negatively related constructs are better understood as a one-dimensional construct with a positive to negative bi-polarity or as two-dimensional, i.e. one positive and one negative construct with each a low to high intensity. Other examples of this debate regard positive and negative mood (Green et al., [<reflink idref="bib35" id="ref165">35</reflink>]), well-being and ill-being (Zhao & Tay, [<reflink idref="bib108" id="ref166">108</reflink>]), or optimism and pessimism (Kubzansky et al., [<reflink idref="bib53" id="ref167">53</reflink>]).</p> <p>With regard to the relationship between boredom and interest, questions remain open at both the content and methodological levels. Our study did not set out to answer these questions, and we had no measures in place to explicitly prove it. However, our findings indicate that boredom is not simply the absence of interest, i.e. the opposite pole on one dimension. In other words, a student cannot only be classified as either bored or interested, but could be described as exhibiting any combination of high and low levels of boredom and interest. Nevertheless, further clarification is required regarding the precise nature of these combinations and the manner in which distinct components of boredom and interest relate.</p> <hd id="AN0183065736-33">Functional Relations over Time</hd> <p>The findings of our latent cross-lagged models showed, as expected (H1), higher interest at earlier time points was significantly related to a decrease in boredom at later time points over and above the stability effect of prior boredom. This negative relationship between the subjective value of an achievement-related behaviour or domain and boredom has been corroborated by many studies across different domains, age groups, and learning environments (Goetz et al., [<reflink idref="bib24" id="ref168">24</reflink>]; Pekrun et al., [<reflink idref="bib71" id="ref169">71</reflink>], [<reflink idref="bib72" id="ref170">72</reflink>]; Putwain et al., [<reflink idref="bib79" id="ref171">79</reflink>]). Our results are thus well-aligned with this empirical evidence.</p> <p>The results of the cross-lagged effect of boredom on interest, on the other hand, were rather surprising. Prior boredom was related to an increase in interest in mathematics from grades 5 to 6 and grades 6 to 8, and in German from grades 6 to 8, controlling for prior interest. The mechanisms that could explain this effect are rather speculative at this point. One potential explanation is that students who perceive themselves as competent and high achieving in a school subject are likely to experience feelings of under-challenge at some point, which may result in boredom due to under-challenge but also higher interest in this subject (c.f., Goetz et al., [<reflink idref="bib27" id="ref172">27</reflink>]). The mediating processes may include the following. Students' feelings of not being sufficiently challenged could be accompanied by high control cognitions regarding the subject. In a school context, where extrinsically motivated behaviour and performance goals are triggered, a subject that appears to be easily mastered may be perceived as attractive due to the perceived ease of success. This may then increase the level of interest in the domain. This would be consistent with prior empirical evidence indicating a reciprocal relationship between interest and achievement (e.g. Koeller et al., [<reflink idref="bib50" id="ref173">50</reflink>]).</p> <p>This effect could potentially also be explained by boredom due to over-challenge. Students who experience boredom due to over-challenge often do not perform well. The low achievement could result in a higher level of engagement with the subject (e.g. via private lessons, more engaged learning, more frequent interactions with classmates and teachers), resulting over time in a higher interest.</p> <p>An additional, tentative rationale for the positive relation between prior boredom and later interest is that it represents a coping mechanism for dealing with boredom. Cognitive and behavioural strategies to cope with boredom can focus on either approach or avoidance (Nett et al., [<reflink idref="bib67" id="ref174">67</reflink>], [<reflink idref="bib68" id="ref175">68</reflink>]). Our result that prior boredom leads to an increase in later interest could be an expression of a cognitive approach strategy to deal with boredom, in which students change their view of the boredom-inducing topic or domain by reappraising it as more interesting. This strategy was previously described and investigated by Sansone and colleagues ([<reflink idref="bib87" id="ref176">87</reflink>]), who emphasised the importance of interest as a self-regulatory mechanism for students to sustain or increase their motivation with uninteresting tasks. In three empirical studies, they found the strategy was used by students especially when there was a reason to continuously or repeatedly engage with the given task—a situation with which students commonly are confronted at school, where most tasks and activities are compulsory and consecutive. More recent investigations (Daniels et al., [<reflink idref="bib11" id="ref177">11</reflink>]; Nett et al., [<reflink idref="bib67" id="ref178">67</reflink>]) found that <emph>reappraisers</emph>, i.e. students who prefer cognitive approach strategies to deal with boring class content, were less likely to be bored in the future and showed more favourable academic emotions and cognitions overall compared to students preferring other strategies.</p> <p>However, our analyses were conducted at the construct level and, as a result, can only be interpreted as trends. Consequently, they do not allow for any conclusions to be drawn about individual adaptations. Furthermore, the intervals between our time points are too long to reliably infer from observed changes to coping strategies. Given the length of our time intervals, which ranged from half a school year to two school years, it is to be expected that moderating or mediating factors, such as academic achievement or self-concept, will be present.</p> <p>In conclusion, this line of thought is somewhat conjectural and requires further theoretical elaboration and empirical investigation. This should include analyses of the relations between boredom and interest with achievement level, reasons for boredom, and/or academic self-concept as moderators.</p> <p>By examining two different domains, we were able to control for possible dimensional contrasts and verify the robustness of our findings across the two subjects. Latent correlations in our analyses indicated that the correlations between constructs within domains were considerably higher and more consistently significant than those between domains. This finding is indicative of domain-specificity in students' emotional-motivational experiences, a conclusion that aligns with prior research (Goetz et al., [<reflink idref="bib24" id="ref179">24</reflink>]). The size of the between-domain correlations decreases over time, whereas the magnitude of the within-domain correlations increases. This pattern is consistent with previous findings that school subjects perceived as different are perceived as even more different as students get older (Goetz et al., [<reflink idref="bib26" id="ref180">26</reflink>]).</p> <p>In the context of our longitudinal 4-variable model, almost no path coefficients between domains reached statistical significance. As far as possible contrasts between domains are concerned, we could not find any substantial ones for our constructs. This could indicate that the development of boredom and interest in German is largely independent of the development of boredom and interest in mathematics. However, dimensional comparisons and contrast effects between domains, as posited in the dimensional comparison theory (DCT; Moeller & Marsh, [<reflink idref="bib62" id="ref181">62</reflink>]), have already been substantiated in numerous studies (Moeller, [<reflink idref="bib61" id="ref182">61</reflink>]). The DCT was initially applied primarily in the context of research on academic self-concept. However, as Moeller ([<reflink idref="bib61" id="ref183">61</reflink>]) suggested in his review, it is increasingly extended to other psychological constructs, like test anxiety (Arens et al., [<reflink idref="bib3" id="ref184">3</reflink>]), intrinsic value (Gaspard et al., [<reflink idref="bib22" id="ref185">22</reflink>]), and interest and motivational beliefs (Wan et al., [<reflink idref="bib106" id="ref186">106</reflink>]). In our context of boredom and interest, it is possible that contrast effects may not occur directly but rather via the mediation of academic self-concept and academic achievement, as some studies regarding interest and intrinsic value have indicated (Schneider & Wolff, [<reflink idref="bib95" id="ref187">95</reflink>]; Schurtz et al., [<reflink idref="bib97" id="ref188">97</reflink>]). Further research is required to investigate the applicability of the DCT to variables beyond the academic self-concept and their interaction with self-concept and achievement.</p> <p>Most importantly, the overall structure of our results remained consistent for both mathematics and German. This was evident in both the confirmatory factor analysis (i.e. a two-factor model fitting better than a general factor model, increasing correlations of both factors) as well as in our longitudinal models (i.e. high stability of interest, lower of boredom; negative effects of interest on boredom, positive of boredom on subsequent interest). These findings indicate that the results can be generalised across mathematical and verbal subjects.</p> <hd id="AN0183065736-34">Limitations and Implications</hd> <p>This study features several strengths, including a longitudinal research design controlling for autoregressive effects, latent variable modelling correcting for measurement error, the investigation of two different domains, and a developmental perspective on the functional relations of boredom and interest in a relevant age group of students. Despite these strengths, the results must be interpreted considering several limitations.</p> <hd id="AN0183065736-35">Limitations</hd> <p>Our sample consisted of German students exclusively from the highest track of the secondary school system (i.e. Gymnasium), which might have led to a reduced variance in our sample. Future research could profit from extending the samples to different kinds of schools and school systems in other countries to investigate if the results can be replicated.</p> <p>A methodological limitation was our reliance solely on student self-reports. While there are advantages to using self-report instruments, there are also impediments, like the probability that some students may not have answered the questionnaires truthfully to avoid social judgment, leading to inaccurate statements (Robinson & Clore, [<reflink idref="bib84" id="ref189">84</reflink>]). Although the instruments used in this study to assess students' self-reports are established and validated with acceptable reliability, research on a multifaceted construct like boredom could benefit from different data sources. Additional approaches like classroom observations, teacher reports, physiological measures, or experience sampling could increase the validity and help gain a more complete picture of the relations of boredom and interest. Furthermore, we used a 2-item measure for boredom. While research has shown that short scales can assess affective-motivational constructs reliably and validly (Gogol et al., [<reflink idref="bib31" id="ref190">31</reflink>]), future studies may benefit from assessing boredom via multiple items representing the component structure of boredom (Pekrun et al., [<reflink idref="bib71" id="ref191">71</reflink>]). In doing so, a more nuanced understanding could be achieved of how individual facets of boredom (e.g. motivational component, physiological component) and interest (e.g. lack of interest, disinterest) interact, which could in turn facilitate a more comprehensive understanding of the separability and dimensionality of these two constructs.</p> <p>With our study, we wanted to clarify the relationship of boredom and interest and to contribute to the understanding of their mutual development over time. We therefore focused on these two constructs alone and did not include other variables or covariates (e.g. age, gender) in the analyses. It is known, however, that motivational variables, especially academic self-concept, are related to academic emotions and can have a mediating influence on their developmental functions (Pekrun, [<reflink idref="bib70" id="ref192">70</reflink>]). Assessing the interplay of boredom and interest within a broader theoretical framework, like Pekrun's control-value theory of achievement emotions ([<reflink idref="bib70" id="ref193">70</reflink>]) or the dimensional comparison theory (Moeller, [<reflink idref="bib61" id="ref194">61</reflink>]), could help deepen the understanding of the concepts and their development.</p> <hd id="AN0183065736-36">Implications for Research and Practice</hd> <p>Our results hold an important message for future research. When assessing students' interest, researchers should also include boredom measures, and the assessment of boredom should ideally be accompanied by information about interest. The simplified view that data about boredom automatically allows conclusions about students' (lack of) interest and vice versa should be reconsidered. With the combination of these two interrelated but distinct variables, future studies could provide more accurate and differentiated findings about students' motivational and emotional experience at school.</p> <p>With regard to the separability and dimensionality of boredom and interest, further research is needed. As mentioned above, future studies could examine in detail how different facets of boredom (see the component process model of emotions, Scherer, [<reflink idref="bib89" id="ref195">89</reflink>]) and interest (e.g. lack of interest, disinterest) relate to one another. It could be helpful to compare different measurement instruments used to assess boredom, interest, and their facets and, in addition to factor-analytical analyses, also to investigate whether boredom, lack of interest, and disinterest differentially predict affective-motivational or achievement outcomes.</p> <p>A further fruitful addition would be to include not only the intensity of boredom but also its possible causes (i.e. under-challenge or over-challenge). As mentioned above, there are theoretical considerations that could explain the unexpected positive effect of boredom on later interest with mechanisms involving feeling over- or under-challenged by a school subject.</p> <p>Furthermore, the domain-specificity of boredom and interest, along with its associated mechanisms such as dimensional comparisons, could be more comprehensively evaluated by incorporating additional domains beyond mathematics and German, or by employing an integrative model that assesses both the generality and domain specificity of affective motivational constructs (see Gogol et al., [<reflink idref="bib32" id="ref196">32</reflink>]).</p> <p>Given that a lot of research questions in the field of boredom and interest are still unanswered and empirical longitudinal research is still scarce, any considerations about practical implications are rather speculative. Our results, and particularly the positive effect of earlier boredom on changes in later interest, however, can be seen as a reminder to avoid a simplistic view of achievement emotions as solely positive or negative and instead foster a perspective of academic boredom as a useful indicator for students and teachers alike. As suggested by Mugon and colleagues ([<reflink idref="bib64" id="ref197">64</reflink>]), boredom should not be seen as the cause of learning problems, but merely as a symptom of them. In this way, boredom can be understood as a sign that students' attention is not engaged, allowing teachers to change factors in the learning environment that may enhance students' attention, or to help students understand and interpret boredom as valuable feedback.</p> <p>Raising awareness for boredom and its consequences during teacher training and qualification may be needed to better prepare educational practitioners for seeing boredom as a signal that their students are not successfully engaging in an activity perceived as meaningful.</p> <p>Our results show that higher interest reduces the development of later boredom and that the emergence of boredom results in an increase in interest. One implication that can therefore be deduced is the potential for using interest to reduce boredom. The approach of enhancing the perceived intrinsic value of a subject or task and fostering students' interest to reduce boredom has empirical support: not only is it beneficial for the prevention and reduction of boredom, but it is also known to have positive consequences for learning, motivation, and achievement (Krapp, [<reflink idref="bib51" id="ref198">51</reflink>]; Marsh et al., [<reflink idref="bib58" id="ref199">58</reflink>]; Scherrer et al., [<reflink idref="bib92" id="ref200">92</reflink>]; Silvia, [<reflink idref="bib98" id="ref201">98</reflink>]).</p> <p>Strategies for educators could focus on utility-value interventions that animate students to consider their long-term goals and how current tasks and activities contribute to them (Hulleman et al., [<reflink idref="bib45" id="ref202">45</reflink>]) as well as stressing the relevance of the subject to students' daily lives (Hidi & Renninger, [<reflink idref="bib39" id="ref203">39</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib44" id="ref204">44</reflink>]). Finding personal meaning and relevance in a topic is an important factor in the process of advancing from situational to individual interest and predicts successive interest and performance (Hidi & Renninger, [<reflink idref="bib39" id="ref205">39</reflink>]).</p> <p>Reducing boredom by fostering students' interest should be an important goal for educators. Yet, more research about the relations of boredom and interest is needed, including the possible causes of boredom (under- or over-challenge), in order to develop evidence-based intervention programmes.</p> <hd id="AN0183065736-37">Conclusion</hd> <p>Our study provides evidence of the differential relationships between boredom and interest. The findings support the theoretically well-founded assumption that academic boredom and interest are two interrelated but distinct constructs and not just opposite poles of the same construct. In line with other studies, we found that interest leads to less boredom in the long term. As an unexpected result, we also found that earlier boredom was related to higher later interest. This finding calls into question earlier interpretations of negative cross-sectional correlations as an indicator for inverse relations between both constructs. It needs further theoretical elaboration as well as empirical investigation and highlights the importance of a differentiated assessment of boredom and interest in future research.</p> <hd id="AN0183065736-38">Acknowledgements</hd> <p>This project was supported by the Ministry of Education, Science, Adolescence, and Culture of Rheinland-Pfalz, Germany (9421C-Tgb.-Nr. 2926/14).</p> <hd id="AN0183065736-39">Funding</hd> <p>Open Access funding enabled and organized by Projekt DEAL.</p> <hd id="AN0183065736-40">Data Availability</hd> <p>The data used in this study come from a longitudinal project led by Franzis Preckel that focuses on the development of motivation and self-concept in secondary school students. This project is supported by the Ministry of Education, Science, Youth and Culture of Rhineland-Palatinate, Germany (9421C-Tgb.-Nr. 2926/14). The study was approved by the Supervision and Services Directorate of Rhineland-Palatinate ("Aufsichts- und Dienstleistungsdirektion"; protocol number: 32-03 405/29/05). Due to ethical restrictions, we cannot provide the data.</p> <hd id="AN0183065736-41">Declarations</hd> <p></p> <hd id="AN0183065736-42">Conflict of Interest</hd> <p>The authors declare no competing interests.</p> <hd id="AN0183065736-43">Supplementary Information</hd> <p>Below is the link to the electronic supplementary material.</p> <p>Graph: Supplementary file1 (DOCX 28 KB)</p> <hd id="AN0183065736-44">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0183065736-45"> <title> References </title> <blist> <bibl id="bib1" idref="ref26" type="bt">1</bibl> <bibtext> Ahmed W, van der Werf G, Kuyper H, Minnaert A. Emotions, self-regulated learning, and achievement in mathematics: A growth curve analysis. 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Header DbId: eric
DbLabel: ERIC
An: EJ1460620
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
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IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Is Boredom the Opposite of Interest? A Longitudinal Reciprocal Effect Study
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Katharina+Luisa+Boehme%22">Katharina Luisa Boehme</searchLink><br /><searchLink fieldCode="AR" term="%22Thomas+Goetz%22">Thomas Goetz</searchLink><br /><searchLink fieldCode="AR" term="%22Markus+Feuchter%22">Markus Feuchter</searchLink><br /><searchLink fieldCode="AR" term="%22Franzis+Preckel%22">Franzis Preckel</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-5768-8702">0000-0002-5768-8702</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Educational+Psychology+Review%22"><i>Educational Psychology Review</i></searchLink>. 2025 37(1).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 35
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Mathematics%22">Secondary School Mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Teachers%22">Secondary School Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22German%22">German</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Education%22">Mathematics Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Interests%22">Student Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Patterns%22">Psychological Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Native+Language+Instruction%22">Native Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Attitude+Measures%22">Attitude Measures</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10648-025-09991-5
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1040-726X<br />1573-336X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: After decades of being conceptualised solely as a lack of interest, boredom has recently gained attention as an important construct in its own right. However, there is still a lack of studies focusing on the relations and developmental interplay of these two closely related constructs. This study examines the overall long-term developmental structure and interplay of students' boredom and interest in the school domains of mathematics and German from fifth to eighth grade. We investigated German secondary school students (N = 1471) over four waves of measurement, using self-report questionnaires. Confirmatory factor analyses in preparation to the longitudinal approach revealed a significantly better fit for two- vs. one-factor models, indicating an empirical separability of boredom and interest. This was further supported by different stabilities in our latent cross-lagged models with low autoregressive paths for boredom and high paths for interest. The latent cross-lagged models also revealed that higher levels of earlier interest were related to lower levels of later boredom. Surprisingly, individuals with higher boredom scores relative to others on average increased in their interest from the second time point onwards. Findings were robust for German and mathematics. Overall, the results show that while boredom and interest have a large phenomenological overlap, they are empirically separable constructs with different levels of stability and influence each other in a distinctive manner throughout their developmental interplay. Implications for research and practice are outlined.
– 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: EJ1460620
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1460620
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10648-025-09991-5
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 35
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Secondary School Mathematics
        Type: general
      – SubjectFull: Secondary School Students
        Type: general
      – SubjectFull: Secondary School Teachers
        Type: general
      – SubjectFull: German
        Type: general
      – SubjectFull: Mathematics Education
        Type: general
      – SubjectFull: Student Interests
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Native Language Instruction
        Type: general
      – SubjectFull: Attitude Measures
        Type: general
      – SubjectFull: Germany
        Type: general
    Titles:
      – TitleFull: Is Boredom the Opposite of Interest? A Longitudinal Reciprocal Effect Study
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Katharina Luisa Boehme
      – PersonEntity:
          Name:
            NameFull: Thomas Goetz
      – PersonEntity:
          Name:
            NameFull: Markus Feuchter
      – PersonEntity:
          Name:
            NameFull: Franzis Preckel
    IsPartOfRelationships:
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          Dates:
            – D: 01
              M: 03
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1040-726X
            – Type: issn-electronic
              Value: 1573-336X
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Educational Psychology Review
              Type: main
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