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Utility-Value Intervention in School: Students' Migration and Parental Educational Backgrounds as Moderators

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Title: Utility-Value Intervention in School: Students' Migration and Parental Educational Backgrounds as Moderators
Language: English
Authors: Weidinger, Anne F. (ORCID 0000-0002-8863-4416), Gaspard, Hanna, Harackiewicz, Judith M., Paschke, Patrick, Bergold, Sebastian, Steinmayr, Ricarda
Source: Journal of Experimental Education. 2022 90(2):364-382.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 19
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Grade 9
Junior High Schools
Middle Schools
Descriptors: Foreign Countries, High School Students, Grade 9, Migrants, Migration, Parent Background, Intervention, Mathematics Achievement
Geographic Terms: Germany
DOI: 10.1080/00220973.2020.1855407
ISSN: 0022-0973
Abstract: A growing body of research suggests that utility-value interventions can promote students' academic motivation and achievement. Moreover, there is evidence that minimal interventions are particularly useful for ethnic minority and first-generation students at college. Whether this is also the case with high school students belonging to minorities and having low parental educational background, is unclear. In a double-blind randomized field experiment with N = 439 academic-track students from 9th grade in Germany, we investigated whether a short version of an established utility-value intervention (i.e., quotations evaluation intervention) would promote the students' utility, attainment, and intrinsic values in math and their math test performance after the intervention. Moreover, we investigated if such short-term intervention effects were moderated by students' migration background and parental educational background. We found significant positive main effects of the intervention on the students' utility and attainment values in math compared to a control group. The effect on attainment value was especially pronounced for students with migration background whose parents held no university entrance certificate. We discuss the practical relevance of these findings and highlight challenges for future research in this field.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1328433
Database: ERIC
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  Value: <anid>AN0155516690;jxe01apr.22;2022Mar04.01:03;v2.2.500</anid> <title id="AN0155516690-1">Utility-Value Intervention in School: Students' Migration and Parental Educational Backgrounds as Moderators </title> <p>A growing body of research suggests that utility-value interventions can promote students' academic motivation and achievement. Moreover, there is evidence that minimal interventions are particularly useful for ethnic minority and first-generation students at college. Whether this is also the case with high school students belonging to minorities and having low parental educational background, is unclear. In a double-blind randomized field experiment with N = 439 academic-track students from 9th grade in Germany, we investigated whether a short version of an established utility-value intervention (i.e., quotations evaluation intervention) would promote the students' utility, attainment, and intrinsic values in math and their math test performance after the intervention. Moreover, we investigated if such short-term intervention effects were moderated by students' migration background and parental educational background. We found significant positive main effects of the intervention on the students' utility and attainment values in math compared to a control group. The effect on attainment value was especially pronounced for students with migration background whose parents held no university entrance certificate. We discuss the practical relevance of these findings and highlight challenges for future research in this field.</p> <p>Keywords: Expectancy-value theory; task value; intervention; mathematics; migration background; parental educational background</p> <p>STUDENTS WHO FIND math interesting, important, and useful achieve better in this domain on average and are more likely to choose advanced math courses and math-intensive careers than students who value math less (see <emph>expectancy-value theory</emph>, Eccles et al., [<reflink idref="bib14" id="ref1">14</reflink>]; Wigfield et al., [<reflink idref="bib60" id="ref2">60</reflink>]). This is also true when accounting for the students' current math achievement, cognitive abilities, gender, and family background (e.g., Guo et al., [<reflink idref="bib22" id="ref3">22</reflink>]; Lauermann et al., [<reflink idref="bib34" id="ref4">34</reflink>]; Trautwein et al., [<reflink idref="bib53" id="ref5">53</reflink>]; Weidinger et al., [<reflink idref="bib55" id="ref6">55</reflink>]). There are still relatively few students who decide to pursue a science, technology, engineering, and mathematics (STEM) major or to work in a STEM field after compulsory schooling, resulting in a considerable lack of STEM fields professionals (e.g., Chen, [<reflink idref="bib8" id="ref7">8</reflink>]; German Economic Institute, [<reflink idref="bib21" id="ref8">21</reflink>]). Therefore, it is crucial to foster positive value beliefs in math already before adolescents leave school (see Wigfield et al., [<reflink idref="bib59" id="ref9">59</reflink>]). Interventions that emphasized the utility of math, for example by presenting students interview quotations of young adults who describe situations in which math was useful to them, have been shown to improve the students' value beliefs in math (Gaspard et al., [<reflink idref="bib20" id="ref10">20</reflink>]) and their math achievement (Brisson et al., [<reflink idref="bib5" id="ref11">5</reflink>]). A study with university students revealed that effects of a utility-value intervention were particularly strong for students with migration background and low parental educational background (i.e., first-generation college students; Harackiewicz et al., [<reflink idref="bib24" id="ref12">24</reflink>]). Such interventions are thus a promising approach for closing achievement gaps that are due to social background (see Harackiewicz et al., [<reflink idref="bib24" id="ref13">24</reflink>]). However, whether these student characteristics moderate utility-value intervention effects also in the school context has not been investigated yet (see Rosenzweig & Wigfield, [<reflink idref="bib41" id="ref14">41</reflink>]). The present study, first, aimed to implement the quotations evaluation task that was previously used as part of a longer intervention (Brisson et al., [<reflink idref="bib5" id="ref15">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref16">20</reflink>]) within a double-blind randomized field experiment and to investigate its short-term effects on high school students' value beliefs and math test performance. The second aim of this study was to investigate whether short-term intervention effects on math values and math test performance were moderated by students' migration background and parental educational background. Identifying individual moderators that impact the effectiveness of utility-value interventions in school will provide intervention researchers and practitioners with an estimate of how effective interventions will be in certain groups of students (see Rosenzweig & Wigfield, [<reflink idref="bib41" id="ref17">41</reflink>]), and with information about whether such interventions can be means to close gaps in motivation and achievement for disadvantaged students.</p> <hd id="AN0155516690-2">Utility-value interventions for students</hd> <p>Utility-value interventions target students' value beliefs and their engagement in academic tasks by helping students think about the value of their course material beyond the immediate situation (see Harackiewicz et al., [<reflink idref="bib24" id="ref18">24</reflink>]). Utility-value interventions are grounded in <emph>expectancy-value theory</emph> (Eccles et al., [<reflink idref="bib14" id="ref19">14</reflink>]; Wigfield et al., [<reflink idref="bib60" id="ref20">60</reflink>]). According to this theory, students' achievement and choices in a specific domain such as math are determined by their value beliefs and their expectations of success in this domain (Eccles et al., [<reflink idref="bib14" id="ref21">14</reflink>]). Eccles and her colleagues distinguish three value components that should be positively associated with students' achievement, namely intrinsic value (i.e., enjoyment of doing a task), attainment value (i.e., personal importance of doing well on a task), and utility value (i.e., perceived usefulness of doing well on a task; for details, see Eccles et al., [<reflink idref="bib15" id="ref22">15</reflink>]). Utility-value interventions focus on the utility value component because this component is considered to be conceptually closest to extrinsic motivation (see Eccles et al., [<reflink idref="bib15" id="ref23">15</reflink>]; Wigfield & Eccles, [<reflink idref="bib58" id="ref24">58</reflink>]), and therefore should be most responsive to external interventions (see Harackiewicz et al., [<reflink idref="bib27" id="ref25">27</reflink>], [<reflink idref="bib24" id="ref26">24</reflink>]). Math-related tasks have high utility value if students believe that these tasks are useful and relevant not only for the immediate situation, but also for the students' future plans and goals. Although utility-value interventions mainly target students' utility value, effects on the other value components are possible, too. For example, if students find math tasks useful and thus connect them to relevant personal goals, they might do these tasks in a more internally regulated way, which can deepen their interest in math and can promote the importance of math tasks for the students' identity (Vansteenkiste et al., [<reflink idref="bib54" id="ref27">54</reflink>]; for more details, see Harackiewicz et al., [<reflink idref="bib27" id="ref28">27</reflink>]).</p> <p>Utility-value interventions mostly are curricular interventions in which students either are provided with arguments for the usefulness of a topic (i.e., directly communicated utility; e.g., Durik et al., [<reflink idref="bib13" id="ref29">13</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref30">20</reflink>]; Rosenzweig et al., [<reflink idref="bib40" id="ref31">40</reflink>]) or generate arguments by themselves by writing short essays about the personal relevance of course material (i.e., self-generated utility; e.g., Harackiewicz et al., [<reflink idref="bib24" id="ref32">24</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref33">28</reflink>]; Hulleman et al., [<reflink idref="bib29" id="ref34">29</reflink>]). The majority of studies on the effects of utility-value interventions was conducted as field or laboratory experiments with university students in the domains of math, physics, biology, or psychology (e.g., Durik et al., [<reflink idref="bib13" id="ref35">13</reflink>]; Harackiewicz et al., [<reflink idref="bib24" id="ref36">24</reflink>]; Hulleman et al., [<reflink idref="bib29" id="ref37">29</reflink>]; Rosenzweig et al., [<reflink idref="bib42" id="ref38">42</reflink>]; for an overview, see Harackiewicz & Priniski, [<reflink idref="bib25" id="ref39">25</reflink>]). However, there are some studies that investigated middle and high school students, mostly in the domains of math (Brisson et al., [<reflink idref="bib5" id="ref40">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref41">20</reflink>]; Rosenzweig et al., [<reflink idref="bib40" id="ref42">40</reflink>]; Woolley et al., [<reflink idref="bib62" id="ref43">62</reflink>][<reflink idref="bib1" id="ref44">1</reflink>]) or science (Harackiewicz et al., [<reflink idref="bib26" id="ref45">26</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref46">28</reflink>]; Shin et al., [<reflink idref="bib45" id="ref47">45</reflink>]). Findings indicated that utility-value interventions not only enhanced high school students' utility value, but also their intrinsic and attainment values (Gaspard et al., [<reflink idref="bib20" id="ref48">20</reflink>]), their interest (Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref49">28</reflink>]), competence beliefs (Brisson et al., [<reflink idref="bib5" id="ref50">5</reflink>]), the number of STEM courses taken (Harackiewicz et al., [<reflink idref="bib26" id="ref51">26</reflink>]), and their achievement (Brisson et al., [<reflink idref="bib5" id="ref52">5</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref53">28</reflink>]; Woolley et al., [<reflink idref="bib62" id="ref54">62</reflink>]).</p> <p>Importantly, interventions in which high school students should evaluate interview quotations of young adults who describe situations in which math was useful to them ("quotations evaluation task") proved to be more effective for this age group than writing interventions in which students had to generate own arguments ("essay task"; e.g., Brisson et al., [<reflink idref="bib5" id="ref55">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref56">20</reflink>]; Rosenzweig et al., [<reflink idref="bib40" id="ref57">40</reflink>]). In the study by Gaspard et al. ([<reflink idref="bib20" id="ref58">20</reflink>]), the 90-min quotations evaluation intervention that high school students received within the classroom consisted of two elements: a psychoeducational presentation for the whole class and a relevance-inducing task for individual students. Additionally, students received two reinforcements (embedded into a homework diary) 1 week and 4 weeks after the intervention. Students' task values in math were assessed 6 weeks and 5 months after the intervention.</p> <p>Although this intervention has been successful in enhancing high school students' motivation and achievement in math, there are still open questions to be addressed. For instance, how can the quotations evaluation task be implemented more easily in math class (see Rosenzweig et al., [<reflink idref="bib40" id="ref59">40</reflink>])? Do positive effects of the quotations evaluation task occur immediately after students received this intervention or after some incubation time (see Gaspard et al., [<reflink idref="bib20" id="ref60">20</reflink>])? Does the effectiveness of the quotations evaluation task differ between different groups of students (see Rosenzweig & Wigfield, [<reflink idref="bib41" id="ref61">41</reflink>])? Our first aim thus was to investigate whether a short version of the quotations evaluation task, which is easier to be implemented in school, is also effective in a randomized controlled experiment in the classroom. Therefore, we used the quotations evaluation task without the psychoeducational presentation to the whole class (see also Rosenzweig et al., [<reflink idref="bib40" id="ref62">40</reflink>]) and tested its short-term effects on the students' values and math test performance. Because our intervention thus was less extensive, it might be less effective than a combination of presentation and quotation evaluation task. Our second aim was to find out whether the effectiveness of the quotations evaluation task would differ between different groups of students. Therefore, we wanted to test potential individual moderators of intervention effects, namely the students' migration and parental educational backgrounds.</p> <hd id="AN0155516690-3">Do migration background and parental educational background moderate intervention effects?</hd> <p>Investigating whether students' migration background and parental educational background moderate the utility-value intervention effect in school, is both reasonable from a theoretical and fruitful from a practical point of view. First, studies with students from Germany indicated that students with migration background and/or low parental educational background (in what follows, simply low parental education) are at special risk for low achievement in math (e.g., OECD, [<reflink idref="bib38" id="ref63">38</reflink>]; Stubbe et al., [<reflink idref="bib51" id="ref64">51</reflink>]). According to the rational-choice model by Boudon ([<reflink idref="bib4" id="ref65">4</reflink>]), family background (e.g., students' migration background and parental education) has primary and secondary effects on students' achievement that result in so-called primary and secondary inequalities in school. Primary effects refer to direct effects of family background on students' competence acquisition that are for example due to unfavorable conditions at home (e.g., little parental support) and language difficulties. Secondary effects of family background occur because of different educational decisions by parents with different backgrounds. For example, on average, students whose parents have low educational backgrounds perceive costs for high education to be higher than the benefit of being highly educated (e.g., Ditton, [<reflink idref="bib12" id="ref66">12</reflink>]). Moreover, it is likely that parents with migration background and/or low educational background do less often talk about the relevance of math with their child than parents without migration background and/or higher educational background because the former have little access to this information compared to the latter, for example, because of little insight into the German educational system (e.g., Becker, [<reflink idref="bib2" id="ref67">2</reflink>]). Therefore, students with migration background and/or low parental education should especially benefit from an intervention that emphasizes the relevance of math for the students' lives.</p> <p>Second, theories on identity-based motivation (Nurra & Oyserman, [<reflink idref="bib37" id="ref68">37</reflink>]; Oyserman & Destin, [<reflink idref="bib39" id="ref69">39</reflink>]), goal congruity (e.g., Diekman et al., [<reflink idref="bib10" id="ref70">10</reflink>], [<reflink idref="bib11" id="ref71">11</reflink>]), and cultural mismatch (e.g., Stephens et al., [<reflink idref="bib50" id="ref72">50</reflink>]; Stephens & Townsend, [<reflink idref="bib49" id="ref73">49</reflink>]) suggest that students pursue and persist at tasks that are congruent with their identity, goals, and norms, respectively, whereas a mismatch between identity, goals, or norms and the educational context can result in low achievement motivation and disengagement (for more details, see Harackiewicz et al., [<reflink idref="bib24" id="ref74">24</reflink>]). There is empirical evidence that both ethnic minority students (e.g., students with migration background) and students with low parental education (e.g., first-generation college students) are likely to pursue communal and interdependent goals in educational contexts, like working with, caring for, or helping others and forming social connections in school (e.g., Harackiewicz et al., [<reflink idref="bib24" id="ref75">24</reflink>]; Smith et al., [<reflink idref="bib46" id="ref76">46</reflink>]; Stephens et al., [<reflink idref="bib48" id="ref77">48</reflink>]). At the same time, these goals are typically perceived to be less pronounced in STEM fields than in other domains (e.g., Diekman et al., [<reflink idref="bib10" id="ref78">10</reflink>], [<reflink idref="bib11" id="ref79">11</reflink>]). Therefore, it might be possible that students with migration background and low parental education are particularly at risk of poor achievement in STEM because of a mismatch between their identity, goals, or norms and STEM domains, like math (see Harackiewicz et al., [<reflink idref="bib24" id="ref80">24</reflink>]; Stephens et al., [<reflink idref="bib50" id="ref81">50</reflink>]). At the same time, however, they might particularly benefit from a utility-value intervention that provides a connection between STEM courses and the students' own lives and goals (see Nurra & Oyserman, [<reflink idref="bib37" id="ref82">37</reflink>]). Thus, effects of utility-value interventions might be stronger in students having a migration background and parents with low education than in students without migration background and with parents who are highly educated.</p> <p>In line with these theoretical considerations, findings from a double-blind randomized experiment with university students indicated that a utility-value intervention (here: essay-task in the domain of biology) helped college students whose parents had not obtained a 4-year college degree and who belonged to an ethnic minority (Harackiewicz et al., [<reflink idref="bib24" id="ref83">24</reflink>]) to improve their achievement at the end of the semester relative to their fellow students. This was also the case when effects of prior achievement were controlled for. Thus, writing about utility value seemed particularly useful for first-generation students belonging to an ethnic minority (Harackiewicz et al., [<reflink idref="bib24" id="ref84">24</reflink>]), so-called underrepresented students. Using data from the same project as Gaspard et al. ([<reflink idref="bib20" id="ref85">20</reflink>]), Häfner et al. ([<reflink idref="bib23" id="ref86">23</reflink>]) did not find that the students' migration background and parental educational background moderated effects of the quotations evaluation task in school. However, they did not investigate whether intervention effects were stronger for students with both migration background and low parental education. Therefore, it is an open question whether findings by Harackiewicz et al. ([<reflink idref="bib24" id="ref87">24</reflink>]) can be generalized to high school students. Because high school students with migration background on average have a lower social status (e.g., lower parental education) than students without migration background (e.g., OECD, [<reflink idref="bib38" id="ref88">38</reflink>]), there is a substantial number of students in this category in German schools and the number has increased in recent years (e.g., Federal Statistical Office, [<reflink idref="bib16" id="ref89">16</reflink>], [<reflink idref="bib18" id="ref90">18</reflink>]).</p> <hd id="AN0155516690-4">The present study</hd> <p>The first aim of this study was to test the research question (RQ) whether the quotations evaluation task that was previously used as part of a longer intervention (Brisson et al., [<reflink idref="bib5" id="ref91">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref92">20</reflink>]) is effective within a double-blind randomized field experiment (RQ1) with 9th-grade students from academic track schools in Germany ("Gymnasium"). More precisely, we investigated the following hypotheses (H): A short version of the quotation intervention will promote the students' utility values (H1a), attainment values (H1b), and intrinsic values (H1c) in math compared to a control condition.</p> <p>The second aim was to investigate the research question whether a short form of the quotation intervention in math will have positive short-term effects on the students' math performance (RQ2). We did not set up a hypothesis because previous studies mainly investigated longer-term intervention effects on achievement such as course grades or standardized achievement tests conducted several months after the intervention (e.g., Brisson et al., [<reflink idref="bib5" id="ref93">5</reflink>]; Harackiewicz et al., [<reflink idref="bib24" id="ref94">24</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref95">28</reflink>]), so that we know little about short-term effects in this respect. However, in order to better understand the process initiated by quotation interventions which in the long run led to better achievement, it is important to know what has changed immediately after the intervention. For example, it might be possible that students who received the intervention are more engaged in the subsequent math test and therefore immediately achieve better in this test. By contrast, it might be that the students' achievement does not change immediately, but only in the long-run mediated by a steeper learning curve as a result of higher motivation and engagement in math.</p> <p>The third aim was to investigate the research question whether the students' migration background and parental educational background will moderate the intervention effects on the students' value beliefs and math test performance (RQ3). With respect to theoretical considerations (see also Harackiewicz et al., [<reflink idref="bib24" id="ref96">24</reflink>]), we expected that the quotations evaluation task promotes math values and achievement particularly in students belonging to an ethnic minority (i.e., students with migration background; H3a), in students whose parents have low educational backgrounds (H3b), and in groups of students with both migration background and low parental education (H3c).</p> <hd id="AN0155516690-5">Method</hd> <p></p> <hd id="AN0155516690-6">Research design and procedure</hd> <p>We conducted an experimental study with a 2 (control vs. intervention group) × 2 (without vs. with migration background) × 2 (high vs. low parental education) design. Students were randomly assigned within classrooms to either the intervention or control condition. Students and the investigators were blind to the assignment. The variables of interest were assessed by trained master students and research assistants during regular math classes (90 min). As an introduction, the investigators thanked the students for their willingness to participate and informed them that we were interested in what the students knew and thought about math. Afterwards, the participants filled out a questionnaire in which they indicated their age, gender, migration background, parents' educational background, and the math grade on their last report card. Then, students either received a short version of the quotations evaluation intervention or they worked on the control task, both taking 25 minutes. After the experimental manipulation, all students completed a questionnaire on their value beliefs in math. Finally, students completed two standardized math tests.</p> <hd id="AN0155516690-7">Participants</hd> <p>A sample of 439 students from 21 ninth-grade classrooms of six academic-track schools in Germany participated in the study; 292 students (Cohort 1) participated in the academic year 2017/18 and 147 students (Cohort 2) participated in the academic year 2018/19. Cohorts 1 and 2 did not differ significantly from each other in any variables assessed in this study, except for prior math grade, which, on average, was slightly better in Cohort 2 (<emph>M</emph> = 4.38, <emph>SD</emph> = 1.06) than in Cohort 1 (<emph>M</emph> = 4.14, <emph>SD</emph> = 1.08); <emph>F</emph><subs>(<reflink idref="bib1" id="ref97">1</reflink>, 427)</subs> = 4.44, <emph>p</emph> =.036, η<sups>2</sups><emph><subs>pat</subs></emph> =.010. Therefore, we estimated all models presented in this study with cohort as an additional covariate. On average, students were 14.68 years old (<emph>SD</emph> = 0.80) at the time they participated in this study, and 51.7% were female. Two-hundred three students (46.24%) indicated that they had a migration background and 88 students (20.05%) had no parent who obtained a university entrance certificate (i.e., low parental education); 57 students (12.98%) fell in both categories. The sample was approximately representative of students attending academic track schools in the cities were the data was collected with respect to gender, migration background and parental educational background (e.g., Federal Statistical Office, [<reflink idref="bib17" id="ref98">17</reflink>]; IT.NRW, [<reflink idref="bib31" id="ref99">31</reflink>]). The randomization resulted in 226 students assigned to the intervention condition and 213 students to the control condition. We verify that the project is in accordance with established ethical guidelines for psychological research. Approval by an ethics committee was not required as per the institution's guidelines and applicable regulations in the federal state where the study was conducted. However, the responsible school administrations approved the study design and the data collection procedure beforehand. Participation was voluntary and parents' written informed consent was required before students could participate. Participation rate was high (≈80%).</p> <hd id="AN0155516690-8">Manipulation material</hd> <p>Students in the intervention condition received the quotations evaluation task developed by Gaspard and her colleagues (Brisson et al., [<reflink idref="bib5" id="ref100">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref101">20</reflink>]). Students read six quotations by young adults who described everyday situations in which math had been useful to them. Students then indicated for each quotation whether they had heard it before. Finally, students wrote about what they found convincing in each quotation and ranked quotations according to their personal relevance. Students in the control condition listed topics they had learned something about in math class during the last year. Then, they summarized what they had learned about each topic. Finally, they designed tasks for each topic. Booklets with materials for the intervention and control conditions looked very similar to avoid that the students would become aware of the experimental manipulation during the experiment.</p> <hd id="AN0155516690-9">Measures</hd> <p></p> <hd id="AN0155516690-10">Students' value beliefs in math</hd> <p>Students' value beliefs in math were assessed with an established German scale (German Scale for the Assessment of School Related Values, SESSW; Steinmayr & Spinath, [<reflink idref="bib47" id="ref102">47</reflink>]). The SESSW relies on the expectancy-value theory by Eccles et al. ([<reflink idref="bib14" id="ref103">14</reflink>]), and its psychometric properties have been shown to be good (Steinmayr & Spinath, [<reflink idref="bib47" id="ref104">47</reflink>]). The SESSW assesses utility, attainment, and intrinsic values with three items each. Students indicated on a scale ranging from 1 (<emph>totally disagree</emph>) to 5 (<emph>totally agree</emph>) how much they valued math (utility value: "How useful is what you learn in math? ," "Math will be useful in my future," "The things I learn in math will be of use in my future life"; attainment value: "Being good at math is important to me," "To be good at math means a lot to me," "Attainment in math is important to me"; intrinsic value: "I like math," "I enjoy doing things in math," and "I find math interesting"). Internal consistencies (Cronbach's α) in this study were high (see Table 1).</p> <p>Table 1. Means (M), standard deviations (SD), reliabilities (Cronbach's α), and intercorrelations between variables (overall sample).</p> <p> <ephtml> <table><thead><tr><td>Variables</td><td>Descriptive statistics</td><td>Intercorrelations</td></tr><tr><td><italic>M</italic></td><td><italic>SD</italic></td><td>α</td><td><italic>n</italic></td><td char=".">2</td><td char=".">3</td><td char=".">4</td><td char=".">5</td><td char=".">6</td><td char=".">7</td><td char=".">8</td><td char=".">9</td><td char=".">10</td><td char=".">11</td></tr></thead><tbody valign="top"><tr><td>1. Utility values<sup>a</sup></td><td>3.24</td><td>0.94</td><td>.83</td><td>439</td><td>.46***</td><td char=".">.48***</td><td>.17***</td><td>.15**</td><td>.24***</td><td><.01</td><td>.05</td><td>−.06</td><td>.05</td><td>.05</td></tr><tr><td>2. Attainment values<sup>a</sup></td><td>3.64</td><td>0.91</td><td>.92</td><td>439</td><td /><td char=".">.55***</td><td>.25***</td><td>.15**</td><td>.35***</td><td>−.08</td><td><.01</td><td>−.08</td><td>.15**</td><td>.03</td></tr><tr><td>3. Intrinsic values<sup>a</sup></td><td>3.04</td><td>1.16</td><td>.92</td><td>439</td><td /><td /><td>.31***</td><td>.24***</td><td>.47***</td><td>.04</td><td>.04</td><td>−.04</td><td>.03</td><td>.01</td></tr><tr><td>4. KRW score<sup>b</sup></td><td>26.77</td><td>8.59</td><td>.90</td><td>439</td><td /><td /><td /><td>.59***</td><td>.54***</td><td>−.08</td><td>.09</td><td>.03</td><td>−.07</td><td>.09</td></tr><tr><td>5. TIMSS score<sup>c</sup></td><td>25.19</td><td>5.95</td><td>.82</td><td>439</td><td /><td /><td /><td /><td>.46***</td><td>−.15**</td><td>.17**</td><td>< −.01</td><td>−.25***</td><td>.17***</td></tr><tr><td>6. Prior math grade<sup>d</sup></td><td>4.22</td><td>1.08</td><td>—</td><td>429</td><td /><td /><td /><td /><td /><td>−.04</td><td>−.02</td><td>.10*</td><td>−.04</td><td>.12*</td></tr><tr><td>7. Age</td><td>14.68</td><td>0.80</td><td>—</td><td>439</td><td /><td /><td /><td /><td /><td /><td>.05</td><td><.01</td><td>.04</td><td>−.24***</td></tr><tr><td>8. Gender<sup>e</sup></td><td>—</td><td>—</td><td>—</td><td>439</td><td /><td /><td /><td /><td /><td /><td /><td>−.04</td><td>−.10*</td><td>.11*</td></tr><tr><td>9. Cohort<sup>f</sup></td><td>—</td><td>—</td><td>—</td><td>439</td><td /><td /><td /><td /><td /><td /><td /><td /><td>−.09</td><td>−.02</td></tr><tr><td>10. Migration background<sup>g</sup></td><td>—</td><td>—</td><td>—</td><td>439</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>−.21***</td></tr><tr><td>11. Parental education<sup>h</sup></td><td>—</td><td>—</td><td>—</td><td>422</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>—</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note. N</emph> = 439.</p> <ulist> <item>2 <sups>a</sups>Range: 1 to 5.</item> <item>3 <sups>b</sups>Range: 0 to 50.</item> <item>4 <sups>c</sups>Range: 0 to 42.</item> <item>5 <sups>d</sups>Range: 1 (insufficient/fail) to 6 (excellent).</item> <item>6 <sups>e</sups>0 = female vs. 1 = male.</item> <item>7 <sups>f</sups>0 = Cohort 1 vs. 1 = Cohort 2.</item> <item>8 <sups>g</sups>0 = no migration background vs. 1 = migration background.</item> <item>9 <sups>h</sups>0 = low parental education vs. 1 = high parental education.</item> <item>10 *<emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <hd id="AN0155516690-11">Students' math test performance</hd> <p>We used two different standardized achievement tests to assess the students' math test performance. The first test was the KRW test (<emph>K</emph>onventions- und <emph>R</emph>egel<emph>W</emph>issen), a supplemental test of the German mathematic test for 9th grade (DEMAT 9; Schmidt et al., [<reflink idref="bib44" id="ref105">44</reflink>]). The KRW test is a speed test (3:30 minutes), which assesses knowledge of basic mathematical conventions and rules with 50 relatively easy math tasks (resulting in an overall score ranging from 0 to 50). Its content is not strictly tied to the math curriculum in the 9th grade. Instead, the overall score can be seen as an indicator of the students' basic math skills. The internal consistency of the KRW was high (see Table 1).</p> <p>The second standardized math test consisted of 42 items from the Third International Mathematics and Science Study (TIMSS; Baumert et al., [<reflink idref="bib1" id="ref106">1</reflink>]). The TIMSS math achievement test was constructed so as to represent the common contents of the national 8th-grade curricula of all participating countries. We measured the students' math performance as a composite of their performance in the following domains: algebra, data representation and analysis, number sense, geometry, measurement, and proportionality. Each domain was assessed by seven items, so that the TIMSS score could range from 0 to 42. Internal consistency of the overall score was high (see Table 1).</p> <hd id="AN0155516690-12">Students' migration background</hd> <p>Students indicated their country of birth and the countries of birth of their parents, as well as which language they themselves mostly spoke at home. We created a dummy variable for the students' migration background: 0 (no migration background) vs. 1 (migration background). We assigned students a 1 on this variable if they or at least one parent was not born in Germany, or if they mostly did not speak German at home (see MSB, [<reflink idref="bib35" id="ref107">35</reflink>]).[<reflink idref="bib2" id="ref108">2</reflink>]</p> <hd id="AN0155516690-13">Parental educational background</hd> <p>Students indicated which educational levels their parents had attained, marking one of the following options: 1 (<emph>no school leaving certificate</emph>), 2 (<emph>lowest school leaving certificate</emph>), 3 (<emph>school leaving certificate from the middle school track</emph>), 4 (<emph>school leaving certificate which allows to study at universities of applied science, "Fachabitur"</emph>), 5 (<emph>school leaving certificate which allows to study at all universities, "Abitur"</emph>), 6 (<emph>other</emph>). We created a dummy variable for parental educational background: 0 (no parent with university entrance certificate: response options 0 to 3) vs. 1 (at least one parent with university entrance certificate: response options 4 and 5). Students for whom neither parent obtained a university entrance certificate are labeled students with low parental education in the following. Students with at least one parent who obtained a university entrance certificate are labeled students with high parental education.</p> <hd id="AN0155516690-14">Covariates (prior math grade, age, gender, cohort)</hd> <p>Besides cohort (0 = Cohort 1 vs. 1 = Cohort 2), their age and gender (0 = female vs. 1 = male), students also indicated their last report card grade in math. This grade mainly relied on the students' performance on written math exams, which were administered at several time points of the school year. Math grade additionally included the evaluation of oral participation in math class. In Germany, grades range from 1 (<emph>excellent</emph>) to 6 (<emph>insufficient/fail</emph>). We recoded the students' grades for all analysis, so that higher values denoted better grades.</p> <hd id="AN0155516690-15">Statistical analyses</hd> <p>All statistical analyses were computed in SPSS 26.0 and Mplus 6.12 (Muthén & Muthén, [<reflink idref="bib36" id="ref109">36</reflink>]-2011). In a first step, we used analyses of variances (ANOVAs) or Pearson's chi-square tests for a randomization check (i.e., equivalence of the experimental groups concerning prior math grade, age, gender ratio, ratio of students with migration background, and ratio of students with high parental education). In a second step, we checked whether the requirements for multiple regression analyses were met (i.e., zero conditional mean of errors, homoscedasticity of errors, independence and normal distribution of errors, and linear independence of predictors; e.g., Williams et al., [<reflink idref="bib61" id="ref110">61</reflink>]). Because our data met all requirements, we used multiple regression analyses in M<emph>plus</emph> to test our research questions (RQ1, RQ2, RQ3). More precisely, we performed a regression analysis for each dependent variable (i.e., utility, attainment, and intrinsic values, KRW and TIMSS scores) with treatment (control group vs. intervention group), migration background (without vs. with migration background), and parental educational background (low vs. high parental education) as well as their interactions as predictors. Moreover, we considered the students' previous report card grade in math, their gender, age, and cohort as covariates (see Gaspard et al., [<reflink idref="bib20" id="ref111">20</reflink>]). For ten students, information on their prior math grade was missing. For 17 students, information on their parents' education was missing (i.e., seven students did not answer the questions on their parents' educational levels and ten students chose option 6 [other] for both parents without giving more detailed information on which kind of other educational levels were attained by their parents). We conducted all regression analyses using full information maximum likelihood (FIML) estimation implemented in M<emph>plus</emph> to improve the accuracy and the power of the analyses (see Schafer & Graham, [<reflink idref="bib43" id="ref112">43</reflink>]). Because interaction terms are computed before FIML estimation in M<emph>plus</emph>, the regression analyses were based on data of 422 students (control group: <emph>n</emph> = 217; intervention group: <emph>n</emph> = 205). We <emph>z</emph>-standardized all variables before entering them into the regression analyses. To evaluate the goodness of fit of all estimated models, we used the following cutoff criteria for the fit indices that we computed: the root mean square error of approximation (RMSEA) along with associated 90% confidence interval should be lower than.06, the standardized root-mean-square residual (SRMR) should be lower than.08, and the comparative fit index (CFI) should be higher than.95 (see West et al., [<reflink idref="bib56" id="ref113">56</reflink>]).</p> <hd id="AN0155516690-16">Results</hd> <p></p> <hd id="AN0155516690-17">Descriptive statistics</hd> <p>Table 1 shows means, standard deviations, and reliabilities (Cronbach's α) of constructs, as well as intercorrelations between all variables for the overall sample. Correlations between the students' utility, attainment, and intrinsic values were strong and positive. Students' values correlated weakly to moderately and positively with their KRW and TIMSS scores. Students' KRW and TIMSS scores correlated strongly and positively. The correlations between the students' prior math grade on the one hand and the students' values and test scores on the other were positive and moderate to strong. Students' age, gender, migration background and parental education correlated substantially with the students' TIMSS score. Correlations were weak to moderate and positive for gender and parental education, and negative for age and migration background. Moreover, the students' migration background correlated weakly and positively with the students' attainment value. Among the covariates, parental education correlated significantly positively with the students' prior math grade and gender, and significantly negatively with the students' age and migration background. Moreover, we found a significant negative, but weak correlation between the students' gender and migrations background.</p> <p>The randomization check showed that students in the control group did not significantly differ from students in the intervention group with respect to their prior math grade, <emph>F</emph><subs>(<reflink idref="bib1" id="ref114">1</reflink>, 427)</subs> = 0.15, <emph>p</emph> =.698, <emph>d</emph> = −0.04, and age, <emph>F</emph><subs>(<reflink idref="bib1" id="ref115">1</reflink>, 427)</subs> = 0.18, <emph>p</emph> =.670, <emph>d</emph> = −0.04. Moreover, control and intervention groups did not differ in gender distribution (χ<sups>2</sups> = 0.30, <emph>df</emph> = 1, <emph>p</emph> =.585, φ = −.03), migration background (χ<sups>2</sups> = 1.55, <emph>df</emph> = 1, <emph>p</emph> =.213, φ = −.06), and parental education (χ<sups>2</sups> = 0.03, <emph>df</emph> = 1, <emph>p</emph> =.858, φ = −.01).</p> <hd id="AN0155516690-18">Main effects</hd> <p>Table 2 presents results of all regression analyses. Regression models showed a good fit to the data and explained between 18% (utility value) and 33% (KRW score) of variance in dependent variables. In all models, the students' math grade was the strongest predictor with significant positive effects on value components and test scores (.24 ≤ β ≤.55). Being male was a significant predictor of KRW (β =.10) and TIMSS scores (β =.16). Students' age was a significant negative predictor of the students' TIMSS score (β = −.14), indicating that on average, younger students achieved higher TIMSS scores than older students. Cohort 2 was a significant negative predictor of the students' intrinsic value (β = −.06) and attainment value (β = −.10).</p> <p>Table 2. Effects of utility-value intervention, students' migration background, parental educational background, prior math grade, age, gender, and cohort on value components (utility, attainment, and intrinsic values) and test performance in math (KRW and TIMSS scores).</p> <p> <ephtml> <table><thead><tr><td /><td>Dependent variable</td></tr><tr><td /><td>Utility value</td><td>Attainment value</td><td>Intrinsic value</td><td>KRW score</td><td>TIMSS score</td></tr><tr><td>Predictors</td><td>β (<italic>SE</italic>)</td><td>95% CI</td><td>β (<italic>SE</italic>)</td><td>95% CI</td><td>β (<italic>SE</italic>)</td><td>95% CI</td><td>β (<italic>SE</italic>)</td><td>95% CI</td><td>β (<italic>SE</italic>)</td><td>95% CI</td></tr></thead><tbody valign="top"><tr><td>Treat</td><td>.33 (.04)***</td><td>[.25,.42]</td><td>.10 (.05)*</td><td>[.01,.18]</td><td>.00 (.04)</td><td>[−.09,.08]</td><td>−.04 (.04)</td><td>[−.12,.04]</td><td>−.04 (.04)</td><td>[−.12,.05]</td></tr><tr><td>Mig</td><td>.08 (.05)</td><td>[−.01,.17]</td><td>.15 (.05)**</td><td>[.06,.24]</td><td>.02 (.04)</td><td>[−.07,.10]</td><td>−.02 (.04)</td><td>[−.10,.06]</td><td>−.21 (.04)***</td><td>[−.29, −.12]</td></tr><tr><td>Edu</td><td>.04 (.05)</td><td>[−.06,.13]</td><td>−.02 (.05)</td><td>[−.12,.07]</td><td>−.04 (.05)</td><td>[−.14,.05]</td><td>−.03 (.05)</td><td>[−.12,.06]</td><td>−.01 (.05)</td><td>[−.09,.08]</td></tr><tr><td>Treat*Mig</td><td>.05 (.05)</td><td>[−.04,.14]</td><td>.06 (.05)</td><td>[−.03,.14]</td><td>.03 (.04)</td><td>[−.05,.12]</td><td>.04 (.04)</td><td>[−.04,.12]</td><td>.04 (.04)</td><td>[−.04,.12]</td></tr><tr><td>Treat*Edu</td><td>−.01 (.05)</td><td>[−.10,.08]</td><td>.00 (.05)</td><td>[−.09,.10]</td><td>.03 (.05)</td><td>[−.06,.12]</td><td>−.04 (.04)</td><td>[−.12,.05]</td><td>.04 (.04)</td><td>[−.05,.12]</td></tr><tr><td>Mig*Edu</td><td>.00 (.05)</td><td>[−.09,.09]</td><td>.02 (.05)</td><td>[−.07,.11]</td><td>.00 (.04)</td><td>[−.09,.09]</td><td>.06 (.04)</td><td>[−.02,.14]</td><td>.07 (.04)</td><td>[−.01,.16]</td></tr><tr><td>Treat*Mig*Edu</td><td>−.01 (.05)</td><td>[−.10,.09]</td><td>−.10 (.05)*</td><td>[−.19, −.01]</td><td>−.07 (.05)</td><td>[−.16,.02]</td><td>−.04 (.04)</td><td>[−.12,.05]</td><td>−.07 (.04)</td><td>[−.15,.02]</td></tr><tr><td>Prior math grade</td><td>.24 (.04)***</td><td>[.15,.33]</td><td>.36 (.04)***</td><td>[.28,.45]</td><td>.49 (.04)***</td><td>[.42,.56]</td><td>.55 (.03)***</td><td>[.49,.62]</td><td>.46 (.04)***</td><td>[.39,.53]</td></tr><tr><td>Age</td><td>.01 (.05)</td><td>[−.08,.10]</td><td>−.07 (.05)</td><td>[−.16,.02]</td><td>.04 (.04)</td><td>[−.05,.13]</td><td>−.06 (.04)</td><td>[−.14,.02]</td><td>−.14 (.04)**</td><td>[−.22, −.05]</td></tr><tr><td>Male</td><td>.05 (.05)</td><td>[−.04,.14]</td><td>.03 (.04)</td><td>[−.06,.12]</td><td>.04 (.04)</td><td>[−.05,.12]</td><td>.10 (.04)*</td><td>[.03,.18]</td><td>.16 (.04)***</td><td>[.08,.24]</td></tr><tr><td>Cohort 2</td><td>−.06 (.05)</td><td>[−.15,.03]</td><td>−.10 (.04)*</td><td>[−.18, −.01]</td><td>−.09 (.04)*</td><td>[−.17, −.01]</td><td>−.02 (.04)</td><td>[−.10,.06]</td><td>−.04 (.04)</td><td>[−.12,.04]</td></tr><tr><td><italic>R</italic>² (<italic>SE</italic>)</td><td>.18 (.03)***</td><td>.20 (.04)***</td><td>.26 (.04)***</td><td>.33 (.04)***</td><td>.32 (.04)***</td></tr><tr><td>χ² (<italic>df</italic>), <italic>p</italic></td><td>14.456 (10), <italic>p</italic> =.153</td><td>14.603 (10), <italic>p</italic> =.147</td><td>14.605 (10), <italic>p</italic> =.147</td><td>14.803 (10), <italic>p</italic> =.139</td><td>14.119 (10), <italic>p</italic> =.168</td></tr><tr><td>RMSEA [90 % CI]</td><td>.032 [.000.067]</td><td>.033 [.000.067]</td><td>.033 [.000.067]</td><td>.034 [.000.068]</td><td>.031 [.000.066]</td></tr><tr><td>CFI</td><td>.938</td><td>.939</td><td>.957</td><td>.968</td><td>.972</td></tr><tr><td>SRMR</td><td>.020</td><td>.020</td><td>.021</td><td>.022</td><td>.021</td></tr></tbody></table> </ephtml> </p> <ulist> <item>11 <emph>Note</emph>. <emph>N</emph> = 422. Covariates: prior math grade (recoded, so that higher values denoted better grades), age, gender (0 = female vs. 1 = male), cohort (0 = Cohort 1 vs. 1 = Cohort 2). β (<emph>SE</emph>) = standardized regression coefficient with standard error in parentheses; CI = confidence interval. Treat = treatment (0 = control group vs. 1 = intervention group); Mig = migration background (0 = no migration background vs. 1 = migration background); Edu = parental educational background (0 = low parental education vs. 1 = high parental education); <emph>R<sups>2</sups></emph> (<emph>SE</emph>) = portion of explained variance by the regression model with standard error in parentheses; χ<sups>2</sups> (<emph>df</emph>) = chi-square test statistic with degrees of freedom in parentheses; RMSEA = root mean square error of approximation along with its associated confidence interval; CFI = comparative fit index; SRMR = standardized root mean square residual.</item> <item>12 *<emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <p>To test Hypotheses 1a to 1c (RQ1), we inspected the effect of treatment on the students' value components over and above the other predictors in the models. For utility value (H1a), we found a significant positive treatment effect—as compared with the control condition—which was moderate in size (β =.33). Comparing means of the intervention and control groups revealed that utility value was on average significantly higher in the intervention group than in the control group, as depicted in Figure 1. We also found a significant positive treatment effect on attainment value (H1b). However, this effect was only small (β =.10). Students' attainment value was on average significantly higher in the intervention group (<emph>M</emph><subs>IG</subs> = 3.71, <emph>SD</emph><subs>IG</subs> = 0.92) than in the control group (<emph>M</emph><subs>CG</subs> = 3.56, <emph>SD</emph><subs>CG</subs> = 0.89). There was neither a significant treatment effect on the students' intrinsic value (H1c) nor on their KRW and TIMSS scores (RQ2). Besides effects of treatment, we found two significant main effects of the students' migration background—as compared to no migration background. Having a migration background positively predicted the students' attainment value (β =.15) and negatively predicted the students' TIMSS score (β = −.21).</p> <p>Graph: Figure 1. Main effect of utility-value intervention on students' utility value. Note. N = 439. Mean utility value in math is shown for students in the intervention group and students in the control group. Standard deviation in parentheses.</p> <hd id="AN0155516690-19">Interaction effects</hd> <p>To test our research questions about whether intervention effects are stronger for students with migration background and/or low parental education, we inspected the interaction effect of treatment*migration background (H3a), treatment*parental education (H3b), and treatment*migration background*parental education (H3c) from the regression analyses (see Table 2). We did not find any significant interaction effects on the students' utility and intrinsic values or on their KRW or TIMSS scores (<emph>p</emph> >.05). However, we found a significant small interaction effect of treatment*migration background*parental education on the students' attainment value (H3c), β = −.10, which is depicted in Figure 2. As can be seen, the difference in the students' average attainment value between intervention and control groups was particularly large for students with migration background who also had parents with a low educational background. Subsequently, we tested in which of the four subgroups of students in Figure 2 (i.e., Mig + Edu+, Mig- Edu+, Mig + Edu-, and Mig- Edu-) average attainment values significantly differed between intervention and control groups. The differences in average attainment values between intervention and control groups were only significant in the subgroup of students with migration background and low educational background: Mig + Edu-: <emph>t</emph> (<reflink idref="bib55" id="ref116">55</reflink>) = −2.31, <emph>p</emph> =.025, <emph>d</emph> = 0.62. In all other subgroups, the intervention and control groups did not significantly differ with respect to average attainment value: Mig + Edu+: <emph>t</emph> (<reflink idref="bib129" id="ref117">129</reflink>) = −1.73, <emph>p</emph> =.087, <emph>d</emph> = 0.30; Mig- Edu+: <emph>t</emph> (<reflink idref="bib201" id="ref118">201</reflink>) = −1.01, <emph>p</emph> =.315, <emph>d</emph> = 0.14; Mig- Edu-: <emph>t</emph> (<reflink idref="bib29" id="ref119">29</reflink>) = 0.29, <emph>p</emph> =.772, <emph>d</emph> = 0.11.</p> <p>Graph: Figure 2. Interaction effect on students' attainment value. Note. N = 422. Mean attainment value in math is shown for students in the intervention and students in the control group depending on students' migration background and parental educational background. Standard deviation in parentheses. Mig+ = students with migration background; Mig- = students without migration background; Edu+ = students who have at least one parent with university entrance certificate (high parental education); Edu- = students who have no parent with university entrance certificate (low parental education).</p> <p>Because we could not randomly assign students within their classrooms to the four subgroups in Figure 2, we performed an additional robustness check. We tested whether the interaction effect remains significant when taking into account that students were nested within classrooms by using a maximum likelihood estimation with robust standard errors (MLR) for parameter estimation ("type = complex" option in M<emph>plus</emph> with classroom membership as a cluster variable)[<reflink idref="bib3" id="ref120">3</reflink>]. In this case, the interaction effect of treatment*migration background*parental education on the students' attainment value was only marginally significant (β = −.10, <emph>SE</emph> =.06, <emph>p</emph> =.079). Treatment main effects on utility value (β =.33, <emph>SE</emph> =.05, <emph>p</emph> <.001) and attainment value (β =.10, <emph>SE</emph> =.04, <emph>p</emph> =.030) remained significant.</p> <hd id="AN0155516690-20">Discussion</hd> <p>This study was built on previous studies on effects of utility-value interventions on students' values and math test performance (Brisson et al., [<reflink idref="bib5" id="ref121">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref122">20</reflink>]) by using a short version of their quotations evaluation intervention and a within-classroom-randomization design. We investigated short-term intervention effects on students' values and test performance in math and whether these effects were moderated by the students' migration background and parental educational background (see Harackiewicz et al., [<reflink idref="bib24" id="ref123">24</reflink>]). We found significant positive effects of the quotation intervention on the students' utility and attainment values in math. Moreover, we found that the quotation intervention had a significant positive effect on the students' attainment value especially for students with migration background whose parents hold no university entrance certificate. We did not find any intervention effects on the students' intrinsic value and math test performance.</p> <p>In our analyses, we controlled for differences in the students' prior school grades, their age, gender, and cohort. In line with previous studies, prior school grades were positively associated with the students' task values (e.g., Trautwein et al., [<reflink idref="bib53" id="ref124">53</reflink>]; Weidinger et al., [<reflink idref="bib55" id="ref125">55</reflink>]) and achievement (Brookhart et al., [<reflink idref="bib6" id="ref126">6</reflink>]). Gender differences favoring boys in math were small and occurred only in the students' TIMSS scores, but not in their KRW scores (e.g., Hutchison et al., [<reflink idref="bib30" id="ref127">30</reflink>]). Moreover, younger students achieved higher TIMSS scores than older ones. This small main effect might be due to students who repeated a grade because of very poor performance or to students who skipped a grade because of very good performance. In addition, it can also be due to smarter students entering school early (see Bergold & Steinmayr, [<reflink idref="bib3" id="ref128">3</reflink>]; Cahan & Cohen, [<reflink idref="bib7" id="ref129">7</reflink>]).</p> <hd id="AN0155516690-21">Intervention effects in school</hd> <p>We could replicate and extend findings by Gaspard et al. ([<reflink idref="bib20" id="ref130">20</reflink>]): A short version of their quotation intervention on average significantly promoted the students' utility and attainment values in math (see also Rosenzweig et al., [<reflink idref="bib40" id="ref131">40</reflink>]). Findings thus indicate that a 25-minute quotations evaluation intervention without a classroom presentation is successful, too, at least in the short-term. Contrary to Gaspard et al. ([<reflink idref="bib20" id="ref132">20</reflink>]), we did not find significant intervention main effects on the students' intrinsic value. Moreover, we could not observe any significant intervention effects on the students' math test performance, at least in the short term. This contradicts previous findings from studies with high school students (see Brisson et al., [<reflink idref="bib5" id="ref133">5</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref134">28</reflink>]; Woolley et al., [<reflink idref="bib62" id="ref135">62</reflink>]), even though Brisson et al. ([<reflink idref="bib5" id="ref136">5</reflink>]) used the same utility-value intervention (plus the classroom presentation). Utility-value interventions might affect students' intrinsic value and achievement only in the long run (see Brisson et al., [<reflink idref="bib5" id="ref137">5</reflink>]; Gaspard et al., [<reflink idref="bib20" id="ref138">20</reflink>]; Hulleman & Harackiewicz, [<reflink idref="bib28" id="ref139">28</reflink>]), mediated by changes in students' utility and attainment values, engagement, effort, and competence beliefs (e.g., Cole et al., [<reflink idref="bib9" id="ref140">9</reflink>]; Harackiewicz et al., [<reflink idref="bib27" id="ref141">27</reflink>]; Vansteenkiste et al., [<reflink idref="bib54" id="ref142">54</reflink>]). It may take repeated experience with a topic before intervention effects can be observed on intrinsic value and academic performance. We did not conduct a follow-up assessment, but only examined the students' values and test performance right after the intervention and control task, respectively. Thus, we might have missed intervention effects on intrinsic value and on achievement that only appear after some time. Another possible explanation why we only found effects on utility and attainment values might be that we only applied a short version of the intervention, which might be limited in its efficacy per se. Future studies could examine the effectiveness of different quotation interventions differing in their length or in other important features (e.g., long- vs. short-term effects). Still another reason might be that intervention effects were moderated by other factors. As described next, the students' migration background and parental educational background might have played a role in this respect.</p> <hd id="AN0155516690-22">The role of migration background and parental educational background</hd> <p>We found hints that students with both migration background and low parental education particularly benefit from the quotation intervention. However, the interaction effect was very small and we only found an effect on the students' attainment value, but not on the other value components or on achievement. Moreover, this effect did not remain significant when additionally correcting the standard errors for parameter estimation to account for the clustering of data. Thus, it is crucial to replicate this finding in future studies with larger student samples. Given the fact that increasing attention in psychological research has been paid to the size and practical relevance of effects as opposed to the significance of effects (see Funder & Ozer, [<reflink idref="bib19" id="ref143">19</reflink>]), our findings can give an important impulse for future studies on moderators of utility-value intervention effects. The small moderation effect fits well with the study by Harackiewicz et al. ([<reflink idref="bib24" id="ref144">24</reflink>]) who found a small moderation of treatment effects on university students' course grades. Moreover, it is highly consistent with theoretical considerations.</p> <p>According to the theoretical objectives (see Harackiewicz et al., [<reflink idref="bib24" id="ref145">24</reflink>]), students with migration background and low parental education should be especially sensitive to a utility-value intervention that provides a connection between math and the students' own lives because this student group might experience a greater mismatch between their identity, goals, or norms and the educational context than do other students (e.g., Stephens et al., [<reflink idref="bib50" id="ref146">50</reflink>]). Another reason for the moderation might be the immigrant optimism hypothesis according to which immigrants are a selective social group of individuals who expect upward social mobility in the country they immigrated to (see Kao & Tienda, [<reflink idref="bib32" id="ref147">32</reflink>]). The immigrant optimism hypothesis supposes that parents with migration history have elevated hopes for their children's future, for example that their child will be highly educated and successful, and will have better living conditions than the parents themselves had. Studies showed that these parental aspirations could transfer to the children, leading to higher motivation in school (e.g., Kigel et al., [<reflink idref="bib33" id="ref148">33</reflink>]) and higher educational aspirations (e.g., Tjaden & Scharenberg, [<reflink idref="bib52" id="ref149">52</reflink>]) compared to students without migration background. Another side effect of the "immigrant optimism" might be that students with migration history are more sensitive to utility-value interventions than students without migration history because asking them to think about the value of their course material in math might trigger their optimistic educational goals and hopeful identities. It however remains an open question why the interaction effect was found only with respect to attainment value. According to <emph>expectancy-value theory</emph>, math tasks have high attainment value if students view them as central for their own sense of self and social identity (see Eccles et al., [<reflink idref="bib15" id="ref150">15</reflink>]; Wigfield & Cambria, [<reflink idref="bib57" id="ref151">57</reflink>]). Indeed, the students' attainment value may be more responsive to identification themes than the other value components. The utility-value intervention might have helped students with migration background and low parental education to more strongly connect math to their individual needs and own identity which in turn might have enhanced their attainment value in math. Since we did not investigate processes between receiving the intervention and longer-term academic outcomes, this explanation remains a hypothesis that should be tested in future research.</p> <p>However, we found hints that the utility-value intervention enhanced math attainment value in students with migration background and low parental education <emph>in addition to</emph> the positive intervention effects on math utility value that we found for all students. Thus, in the short term, these students more strongly benefited from the intervention with respect to math values than did the majority students. The findings that short-term effects on values are especially pronounced for this group of underrepresented students might be another explanation why utility-value interventions could reduce achievement gaps in the long run between students who are disadvantaged and students belonging to a majority (see Harackiewicz et al., [<reflink idref="bib24" id="ref152">24</reflink>]).</p> <hd id="AN0155516690-23">Strengths, limitations and future directions</hd> <p>This study replicates and extends previous findings on the quotations evaluation intervention by Gaspard et al. ([<reflink idref="bib20" id="ref153">20</reflink>]). Strength of this study is that we investigated the moderating role of the students' migration background and parental educational background. Moreover, we randomly assigned students to the experimental conditions within classrooms, so that potential influences of confounding variables on the class level (e.g., social composition of class, math teacher) could be eliminated. Finally, this study extends knowledge on short-term effects of a utility-value intervention because previous utility-value intervention studies mainly focused on longer-term effects. Our findings indicate that intervention effects on the students' utility value and on attainment value of students with both migration background and low parental education occur immediately after the intervention. However, future studies should include follow-up measures of the students' values and achievement to allow further conclusions regarding the effectiveness of the short version of the quotations evaluation intervention (see Brisson et al., [<reflink idref="bib5" id="ref154">5</reflink>]). Second, like previous studies, we focused on the 9th-grade students from academic track schools in Germany (i.e., 14-15 years; e.g., Gaspard et al., [<reflink idref="bib20" id="ref155">20</reflink>]). Future studies should adopt utility-value interventions to other contexts (see Harackiewicz & Priniski, [<reflink idref="bib25" id="ref156">25</reflink>]), for example to less selective school types, to test the generalizability of the findings for students of the same age who are more diverse with respect to their academic achievement and abilities. Third, we could not experimentally manipulate the moderator variables (i.e., migration and parental educational backgrounds). Future studies could use a longitudinal design in order to at least control for pre-intervention differences in motivation and achievement between students with different social and migration backgrounds. Fourth, some subgroups of students were relatively small in this study (e.g., the "Mig- Edu-"-group). Therefore, we could not differentiate between students with different migration backgrounds. With regard to generalizability, it is necessary to replicate our findings in future studies. Finally, we investigated the moderating roles of the students' migration background and parental educational background, but did not test any processes behind intervention effects. It is an important issue for future studies to investigate mechanisms through which utility-value interventions work (see Harackiewicz et al., [<reflink idref="bib24" id="ref157">24</reflink>]).</p> <hd id="AN0155516690-24">Conclusion</hd> <p>Our study indicates that a short version of the quotations evaluation intervention has moderate short-term effects on 9th-grade students' utility value in math. This is an important finding from a practical point of view because the minimal intervention can easily be integrated by math teachers in math class. Although we need evidence on long-term effects of this short intervention on students' values and achievement in math, this study gave first hints that even very short interventions can have positive effects on students' utility value in math. Another important finding is that the minimal intervention might help disadvantaged students to find more attainment value in math. Whether and how this effect can spill over to students' math achievement and choices of math courses, is an important question for future research in this field.</p> <hd id="AN0155516690-25">Acknowledgements</hd> <p>We would like to thank Filiz Quandel and Zora Nina Kolb for their support in data collection and data preparation.</p> <ref id="AN0155516690-26"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref44" type="bt">1</bibl> <bibtext> In the study by Woolley et al. ([62]), the students received an intervention in all four core subjects (mathematics, science, language arts, and social studies). Authors investigated intervention effects on the students' math and reading test scores.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref67" type="bt">2</bibl> <bibtext> One student received the label "migration background" because his mother was born in a German-speaking country other than Germany (i.e., Austria), whereas the student himself and his father were born in Germany, and the language the family spoke at home also was German. Assigning this student the label "0 = no migration background" in additional analyses did not change the pattern of results. In all other student-mother-father-triads with persons not born in Germany, at least one person (student, mother or father) was born in a non-German-speaking country (i.e., a country other than Germany, Austria, or Switzerland).</bibtext> </blist> <blist> <bibl id="bib3" idref="ref120" type="bt">3</bibl> <bibtext> The intraclass correlation coefficients (ICC) for the moderators were.03 (migration background) and.05 (parental education). The ICC for the dependent variables ranged from.02 (attainment value) to.09 (KRW score).</bibtext> </blist> <blist> <bibl id="bib4" idref="ref65" type="bt">4</bibl> <bibtext> *Hanna Gaspard is now affiliated with the Center for Research on Education and School Development at the TU Dortmund University.</bibtext> </blist> </ref> <ref id="AN0155516690-27"> <title> References </title> <blist> <bibtext> Baumert, J., Lehmann, R., Lehrke, M., Clausen, M., Hosenfeld, I., Neubrand, J., & Günther, W. (1998). Testaufgaben Mathematik TIMSS 7./8. Klasse (Population 2) [Math test items TIMSS grades 7/8 (Population 2)]. Max-Planck-Institut für Bildungsforschung.</bibtext> </blist> <blist> <bibtext> Becker, R. (2010). Soziale Ungleichheit von Bildungschancen und Chancengerechtigkeit – eine Reanalyse mit bildungspolitischen Implikationen [Social inequality in education opportunities and equal opportunities – A reanalysis with implications for education policy]. In R. Becker, & W. Lauterbach (Eds.), Bildung als Privileg [Education as a privilege] (pp. 161 – 189). VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-531-92484-7_6</bibtext> </blist> <blist> <bibtext> Bergold, S., & Steinmayr, R. (2019). Non vitae sed scholae discimus? Schooling fosters intelligence. Neuroforum, 25 (2), 121 – 128. https://doi.org/10.1515/nf-2018-0027</bibtext> </blist> <blist> <bibtext> Boudon, R. (1974). Education, opportunity, and social inequality: Changing prospects in Western society. John Wiley & Sons.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref11" type="bt">5</bibl> <bibtext> Brisson, B. M., Dicke, A.-L., Gaspard, H., Häfner, I., Flunger, B., Nagengast, B., & Trautwein, U. (2017). Short intervention, sustained effects: Promoting students' math competence beliefs, effort, and achievement. American Educational Research Journal, 54 (6), 1048 – 1078. https://doi.org/10.3102/0002831217716084</bibtext> </blist> <blist> <bibl id="bib6" idref="ref126" type="bt">6</bibl> <bibtext> Brookhart, S. M., Guskey, T. R., Bowers, A. J., McMillan, J. H., Smith, J. K., Smith, L. F., Stevens, M. T., & Welsh, M. E. (2016). A century of grading research: Meaning and value in the most common educational measure. Review of Educational Research, 86 (4), 803 – 848. https://doi.org/10.3102/0034654316672069</bibtext> </blist> <blist> <bibl id="bib7" idref="ref129" type="bt">7</bibl> <bibtext> Cahan, S., & Cohen, N. (1989). Age versus schooling effects on intelligence development. Child Development, 60 (5), 1239 – 1249. https://doi.org/10.2307/1130797</bibtext> </blist> <blist> <bibl id="bib8" idref="ref7" type="bt">8</bibl> <bibtext> Chen, X. (2013). STEM attrition: College students' paths into and out of STEM fields (Report No. 2014–001). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. <ulink href="http://nces.ed.gov/pubs2014/2014001rev.pdf">http://nces.ed.gov/pubs2014/2014001rev.pdf</ulink></bibtext> </blist> <blist> <bibl id="bib9" idref="ref140" type="bt">9</bibl> <bibtext> Cole, J. S., Bergin, D. A., & Whittaker, T. A. (2008). Predicting student achievement for low stakes tests with effort and task value. Contemporary Educational Psychology, 33 (4), 609 – 624. https://doi.org/10.1016/j.cedpsych.2007.10.002</bibtext> </blist> <blist> <bibtext> Diekman, A. B., Brown, E. R., Johnston, A. M., & Clark, E. K. (2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological Science, 21 (8), 1051 – 1057. https://doi.org/10.1177/0956797610377342</bibtext> </blist> <blist> <bibtext> Diekman, A. B., Steinberg, M., Brown, E. R., Belanger, A. L., & Clark, E. K. (2017). A goal congruity model of role entry, engagement, and exit: Understanding communal goal processes in STEM gender gaps. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 21 (2), 142 – 175. https://doi.org/10.1177/1088868316642141</bibtext> </blist> <blist> <bibtext> Ditton, H. (2007). Kosten, Nutzen und Erfolgswahrscheinlichkeit [Costs, benefit, and probability of success]. In H. Ditton (Ed.), Kompetenzaufbau und Laufbahnen im Schulsystem [Competence development and careers in the school system] (pp. 89 – 115). Waxmann.</bibtext> </blist> <blist> <bibtext> Durik, A. M., Shechter, O. G., Noh, M., Rozek, C. S., & Harackiewicz, J. M. (2015). What if I can't? Success expectancies moderate the effects of utility value information on situational interest and performance. Motivation and Emotion, 39 (1), 104 – 118. https://doi.org/10.1007/s11031-014-9419-0</bibtext> </blist> <blist> <bibtext> Eccles (Parsons), J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives (pp. 75 – 146). Freeman.</bibtext> </blist> <blist> <bibtext> Eccles, J. S. (2005). Subjective task values and the Eccles et al. model of achievement related choices. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105 – 121). Guilford.</bibtext> </blist> <blist> <bibtext> Federal Statistical Office. (2017). Bevölkerung mit Migrationshintergrund – Ergebnisse des Mikrozensus 2010 [Population with migration background – Results of micro census 2010]. Author.</bibtext> </blist> <blist> <bibtext> Federal Statistical Office. (2018). Bildung und Kultur – Allgemeinbildende Schulen [Education and culture – general schools]. Author.</bibtext> </blist> <blist> <bibtext> Federal Statistical Office. (2020). Bevölkerung mit Migrationshintergrund – Ergebnisse des Mikrozensus 2019 [Population with migration background – Results of micro census 2019]. Author.</bibtext> </blist> <blist> <bibtext> Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2 (2), 156 – 168. https://doi.org/10.1177/2515245919847202</bibtext> </blist> <blist> <bibtext> Gaspard, H., Dicke, A., Flunger, B., Brisson, B., Hafner, I., Nagengast, B., & Trautwein, U. (2015). Fostering adolescents' value beliefs for mathematics with a relevance intervention in the classroom. Developmental Psychology, 51 (9), 1226 – 1240. https://doi.org/10.1037/dev0000028</bibtext> </blist> <blist> <bibtext> German Economic Institute (2018). MINT-Frühjahrsreport 2018 [STEM spring report 2018]. Author. https://arbeitgeber.de/www/arbeitgeber.nsf/419b3f74e0ee5f73c1256fba0038ddb3/f62797525e86f75bc125828d0032ef2c/$FILE/MINT-Fruehjahrsreport_2018.pdf</bibtext> </blist> <blist> <bibtext> Guo, J., Parker, P. D., Marsh, H. W., & Morin, A. J. S. (2015). Achievement, motivation, and educational choices: A longitudinal study of expectancy and value using a multiplicative perspective. Developmental Psychology, 51 (8), 1163 – 1176. https://doi.org/10.1037/a0039440</bibtext> </blist> <blist> <bibtext> Häfner, I., Flunger, B., Dicke, A.-L., Gaspard, H., Brisson, B. M., Nagengast, B., & Trautwein, U. (2017). Robin Hood effects on motivation in math: Family interest moderates the effects of relevance interventions. Developmental Psychology, 53 (8), 1522 – 1539. https://doi.org/10.1037/dev0000337</bibtext> </blist> <blist> <bibtext> Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J., S. (2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class. Journal of Personality and Social Psychology, 111 (5), 745 – 765. https://doi.org/10.1037/pspp0000075</bibtext> </blist> <blist> <bibtext> Harackiewicz, J. M., & Priniski, S. J. (2018). Improving student outcomes in higher education: The science of targeted intervention. Annual Review of Psychology, 69, 409 – 435. https://doi.org/10.1146/annurev-psych-122216-011725</bibtext> </blist> <blist> <bibtext> Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S., & Hyde, J., S. (2012). Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological Science, 23 (8), 899 – 906. https://doi.org/10.1177/0956797611435530</bibtext> </blist> <blist> <bibtext> Harackiewicz, J. M., Tibbetts, Y., Canning, E. A., & Hyde, J., S. (2014). Harnessing values to promote motivation in education. Advances in Motivation and Achievement: A Research Annual, 18, 71 – 105. https://doi.org/10.1108/S0749-742320140000018002</bibtext> </blist> <blist> <bibtext> Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science (New York, N.Y.).), 326 (5958), 1410 – 1412. https://doi.org/10.1126/science.1177067</bibtext> </blist> <blist> <bibtext> Hulleman, C. S., Kosovich, J. J., Barron, K. E., & Daniel, D. B. (2017). Making connections: replicating and extending the utility-value intervention in the classroom. Journal of Educational Psychology, 109 (3), 387 – 404. https://doi.org/10.1037/edu0000146</bibtext> </blist> <blist> <bibtext> Hutchison, J. E., Lyons, I. M., & Ansari, D. (2019). More similar than different: Gender differences in Children's Basic Numerical Skills Are the Exception Not the Rule. Child Development, 90 (1), e66 – e79. https://doi.org/10.1111/cdev.13044</bibtext> </blist> <blist> <bibtext> IT.NRW. State Office for Information and Technology in North Rhine-Westphalia (2019). The percentage of students with migration background. Press release, August, 14, 2019. https://www.it.nrw/anteil-der-schuelerinnen-und-schueler-mit-zuwanderungsgeschichte-nrw-auf-369-prozent-gestiegen-97016</bibtext> </blist> <blist> <bibtext> Kao, G., & Tienda, M. (1995). Optimism and achievement: The educational performance of immigrant youth. Social Science Quarterly, 76, 1 – 19.</bibtext> </blist> <blist> <bibtext> Kigel, R. M., McElvany, N., & Becker, M. (2015). Effects of immigrant background on text comprehension, vocabulary, and reading motivation: A longitudinal study. Learning and Instruction, 35, 73 – 84. https://doi.org/10.1016/j.learninstruc.2014.10.001</bibtext> </blist> <blist> <bibtext> Lauermann, F., Tsai, Y.-M., & Eccles, J. S. (2017). Math-related career aspirations and choices within Eccles et al.'s expectancy-value theory of achievement-related behaviors. Developmental Psychology, 53 (8), 1540 – 1559. https://doi.org/10.1037/dev0000367</bibtext> </blist> <blist> <bibtext> Ministry for Children, Family, Refugees and Integration of the State of North Rhine-Westphalia (MSB) (2018). Statistik-TELEGRAMM 2017/18 [Statistic Telegram 2017/18]. Author.</bibtext> </blist> <blist> <bibtext> Muthén, L. K., & Muthén, B. O. (1998). 2011). Mplus (Version 6.12) [Computer software]. Authors.</bibtext> </blist> <blist> <bibtext> Nurra, C., & Oyserman, D. (2018). From future self to current action: An identity-based motivation perspective. Self and Identity, 17 (3), 343 – 364. https://doi.org/10.1080/15298868.2017.1375003</bibtext> </blist> <blist> <bibtext> OECD. (2018). The resilience of students with an immigrant background: Factors that shape well-being. OECD Publishing. https://doi.org/10.1787/9789264292093-en</bibtext> </blist> <blist> <bibtext> Oyserman, D., & Destin, M. (2010). Identity-based motivation: Implications for intervention. The Counseling Psychologist, 38 (7), 1001 – 1043. https://doi.org/10.1177/0011000010374775</bibtext> </blist> <blist> <bibtext> Rosenzweig, E. Q., Hulleman, C. S., Barron, K. E., Kosovich, J. J., Priniski, S. J., & Wigfield, A. (2019). Promises and pitfalls of adapting utility-value interventions for online math courses. The Journal of Experimental Education, 87 (2), 332 – 352. https://doi.org/10.1080/00220973.2018.1496059</bibtext> </blist> <blist> <bibtext> Rosenzweig, E. Q., & Wigfield, A. (2016). STEM motivation interventions for adolescents: A promising start, but further to go. Educational Psychologist, 51 (2), 146 – 163. https://doi.org/10.1080/00461520.2016.1154792</bibtext> </blist> <blist> <bibtext> Rosenzweig, E. Q., Wigfield, A., & Hulleman, C. S. (2020). More useful or not so bad? Examining the effects of utility value and cost reduction interventions in college physics. Journal of Educational Psychology, 112 (1), 166 – 182. Advance online publication. https://doi.org/10.1037/edu0000370</bibtext> </blist> <blist> <bibtext> Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7 (2), 147 – 177. https://doi.org/10.1037/1082-989X.7.2.147</bibtext> </blist> <blist> <bibtext> Schmidt, S., Ennemoser, M., & Krajewski, K. (2013). Deutscher Mathematiktest für neunte Klassen (DEMAT 9) [German mathematic test for 9th grades]. Hogrefe.</bibtext> </blist> <blist> <bibtext> Shin, D. D., Lee, M., Ha, J. E., Park, J. H., Ahn, H. S., Son, E., Chung, Y., & Bong, M. (2019). Science for all: Boosting the science motivation of elementary school students with utility value intervention. Learning and Instruction, 60, 104 – 116. https://doi.org/10.1016/j.learninstruc.2018.12.003</bibtext> </blist> <blist> <bibtext> Smith, J. L., Cech, E., Metz, A., Huntoon, M., & Moyer, C. (2014). Giving back or giving up: Native American student experiences in science and engineering. Cultural Diversity & Ethnic Minority Psychology, 20 (3), 413 – 429. https://doi.org/10.1037/a0036945</bibtext> </blist> <blist> <bibtext> Steinmayr, R., & Spinath, B. (2010). Konstruktion und erste Validierung einer Skala zur Erfassung subjektiver schulischer Werte (SESSW) [Construction and first validation of a scale assessing subjective educational task values]. Diagnostica, 56 (4), 195 – 211. https://doi.org/10.1026/0012-1924/a000023</bibtext> </blist> <blist> <bibtext> Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C., & Covarrubias, R. (2012). Unseen disadvantage: How American universities' focus on independence undermines the academic performance of first-generation college students. Journal of Personality and Social Psychology, 102 (6), 1178 – 1197. https://doi.org/10.1037/a0027143</bibtext> </blist> <blist> <bibtext> Stephens, N. M., & Townsend, S. S. M. (2015). The norms that drive behavior: Implications for cultural mismatch theory. Journal of Cross-Cultural Psychology, 46 (10), 1304 – 1306. https://doi.org/10.1177/0022022115600264</bibtext> </blist> <blist> <bibtext> Stephens, N. M., Townsend, S. S. M., & Dittmann, A. G. (2019). Social-class disparities in higher education and professional workplaces: The role of cultural mismatch. Current Directions in Psychological Science, 28 (1), 67 – 73. https://doi.org/10.1177/0963721418806506</bibtext> </blist> <blist> <bibtext> Stubbe, T. C., Schwippert, K., & Wendt, H. (2016). Kapitel X. Soziale Disparitäten der Schülerleistungen in Mathematik und Naturwissenschaften [Social disparities in student achievement in mathematics and science]. In H. Wendt, W. Bos, C. Selter, O. Köller, K. Schwippert, D. Kasper (Hrsg.) TIMSS 2015. Mathematische und naturwissenschaftliche Kompetenzen von Grundschulkindern in Deutschland im internationalen Vergleich [An international comparison of mathematical and scientific competencies of primary school children in Germany] (p. 333 – 350). Waxmann.</bibtext> </blist> <blist> <bibtext> Tjaden, J. D., & Scharenberg, K. (2017). Ethnic choice effects at the transition into upper-secondary education in Switzerland. Acta Sociologica, 60 (4), 309 – 324. https://doi.org/10.1177/0001699316679491</bibtext> </blist> <blist> <bibtext> Trautwein, U., Marsh, H. W., Nagengast, B., Lüdtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy-value theory: A latent interaction modeling study. Journal of Educational Psychology, 104 (3), 763 – 777. https://doi.org/10.1037/a0027470</bibtext> </blist> <blist> <bibtext> Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic versus extrinsic goal contents in self-determination theory: Another look at the quality of academic motivation. Educational Psychologist, 41 (1), 19 – 31. https://doi.org/10.1207/s15326985ep4101_4</bibtext> </blist> <blist> <bibtext> Weidinger, A. F., Spinath, B., & Steinmayr, R. (2020). The value of valuing math: Longitudinal links between students' intrinsic, attainment and utility values and grades in math. Motivation Science, Advance online publication. https://doi.org/10.1037/mot0000179</bibtext> </blist> <blist> <bibtext> West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209 – 231). Guilford Press.</bibtext> </blist> <blist> <bibtext> Wigfield, A., & Cambria, J. (2010). Students' achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30 (1), 1 – 35. https://doi.org/10.1016/j.dr.2009.12.001</bibtext> </blist> <blist> <bibtext> Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12 (3), 265 – 310. https://doi.org/10.1016/0273-2297(92)90011-P</bibtext> </blist> <blist> <bibtext> Wigfield, A., Eccles, J. S., Fredricks, J., Simpkins, S., Roeser, R., & Schiefele, U. (2015). Development of achievement motivation and engagement. In R. Lerner (Series ed.), M. Lamb & C. Garcia Coll (Vol. Eds.), Handbook of child psychology and developmental science (7th ed., Vol. 3, pp. 657 – 700). Wiley.</bibtext> </blist> <blist> <bibtext> Wigfield, A., Tonks, S., & Klauda, S. L. (2016). Expectancy-value theory. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation in school. (2nd ed., pp. 55 – 74). Routledge.</bibtext> </blist> <blist> <bibtext> Williams, M. N., Grajales, C. A. G., & Kurkiewicz, D. (2013). Assumptions of multiple regression: Correcting two misconceptions. Practical Assessment, Research, and Evaluation, 18, Article 11. https://doi.org/10.7275/55hn-wk47</bibtext> </blist> <blist> <bibtext> Woolley, M. E., Rose, R. A., Orthner, D. K., Akos, P. T., & Jones-Sanpei, H. (2013). Advancing academic achievement through career relevance in the middle grades: A longitudinal evaluation of CareerStart. American Educational Research Journal, 50 (6), 1309 – 1335. https://doi.org/10.3102/0002831213488818</bibtext> </blist> </ref> <aug> <p>By Anne F. Weidinger; Hanna Gaspard; Judith M. Harackiewicz; Patrick Paschke; Sebastian Bergold and Ricarda Steinmayr</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib14" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib60" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib22" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib34" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib53" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib55" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib21" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib59" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib20" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib24" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib41" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib15" firstref="ref22"></nolink> <nolink nlid="nl13" bibid="bib58" firstref="ref24"></nolink> <nolink nlid="nl14" bibid="bib27" firstref="ref25"></nolink> <nolink nlid="nl15" bibid="bib54" firstref="ref27"></nolink> <nolink nlid="nl16" bibid="bib13" firstref="ref29"></nolink> <nolink nlid="nl17" bibid="bib40" firstref="ref31"></nolink> <nolink nlid="nl18" bibid="bib28" firstref="ref33"></nolink> <nolink nlid="nl19" bibid="bib29" firstref="ref34"></nolink> <nolink nlid="nl20" bibid="bib42" firstref="ref38"></nolink> <nolink nlid="nl21" bibid="bib25" firstref="ref39"></nolink> <nolink nlid="nl22" bibid="bib62" firstref="ref43"></nolink> <nolink nlid="nl23" bibid="bib26" firstref="ref45"></nolink> <nolink nlid="nl24" bibid="bib45" firstref="ref47"></nolink> <nolink nlid="nl25" bibid="bib38" firstref="ref63"></nolink> <nolink nlid="nl26" bibid="bib51" firstref="ref64"></nolink> <nolink nlid="nl27" bibid="bib12" firstref="ref66"></nolink> <nolink nlid="nl28" bibid="bib37" firstref="ref68"></nolink> <nolink nlid="nl29" bibid="bib39" firstref="ref69"></nolink> <nolink nlid="nl30" bibid="bib10" firstref="ref70"></nolink> <nolink nlid="nl31" bibid="bib11" firstref="ref71"></nolink> <nolink nlid="nl32" bibid="bib50" firstref="ref72"></nolink> <nolink nlid="nl33" bibid="bib49" firstref="ref73"></nolink> <nolink nlid="nl34" bibid="bib46" firstref="ref76"></nolink> <nolink nlid="nl35" bibid="bib48" firstref="ref77"></nolink> <nolink nlid="nl36" bibid="bib23" firstref="ref86"></nolink> <nolink nlid="nl37" bibid="bib16" firstref="ref89"></nolink> <nolink nlid="nl38" bibid="bib18" firstref="ref90"></nolink> <nolink nlid="nl39" bibid="bib17" firstref="ref98"></nolink> <nolink nlid="nl40" bibid="bib31" firstref="ref99"></nolink> <nolink nlid="nl41" bibid="bib47" firstref="ref102"></nolink> <nolink nlid="nl42" bibid="bib44" firstref="ref105"></nolink> <nolink nlid="nl43" bibid="bib35" firstref="ref107"></nolink> <nolink nlid="nl44" bibid="bib36" firstref="ref109"></nolink> <nolink nlid="nl45" bibid="bib61" firstref="ref110"></nolink> <nolink nlid="nl46" bibid="bib43" firstref="ref112"></nolink> <nolink nlid="nl47" bibid="bib56" firstref="ref113"></nolink> <nolink nlid="nl48" bibid="bib129" firstref="ref117"></nolink> <nolink nlid="nl49" bibid="bib201" firstref="ref118"></nolink> <nolink nlid="nl50" bibid="bib30" firstref="ref127"></nolink> <nolink nlid="nl51" bibid="bib19" firstref="ref143"></nolink> <nolink nlid="nl52" bibid="bib32" firstref="ref147"></nolink> <nolink nlid="nl53" bibid="bib33" firstref="ref148"></nolink> <nolink nlid="nl54" bibid="bib52" firstref="ref149"></nolink> <nolink nlid="nl55" bibid="bib57" firstref="ref151"></nolink>
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  Data: Utility-Value Intervention in School: Students' Migration and Parental Educational Backgrounds as Moderators
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Weidinger%2C+Anne+F%2E%22">Weidinger, Anne F.</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8863-4416">0000-0002-8863-4416</externalLink>)<br /><searchLink fieldCode="AR" term="%22Gaspard%2C+Hanna%22">Gaspard, Hanna</searchLink><br /><searchLink fieldCode="AR" term="%22Harackiewicz%2C+Judith+M%2E%22">Harackiewicz, Judith M.</searchLink><br /><searchLink fieldCode="AR" term="%22Paschke%2C+Patrick%22">Paschke, Patrick</searchLink><br /><searchLink fieldCode="AR" term="%22Bergold%2C+Sebastian%22">Bergold, Sebastian</searchLink><br /><searchLink fieldCode="AR" term="%22Steinmayr%2C+Ricarda%22">Steinmayr, Ricarda</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Experimental+Education%22"><i>Journal of Experimental Education</i></searchLink>. 2022 90(2):364-382.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 19
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  Label: Publication Date
  Group: Date
  Data: 2022
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  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+9%22">Grade 9</searchLink><br /><searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+9%22">Grade 9</searchLink><br /><searchLink fieldCode="DE" term="%22Migrants%22">Migrants</searchLink><br /><searchLink fieldCode="DE" term="%22Migration%22">Migration</searchLink><br /><searchLink fieldCode="DE" term="%22Parent+Background%22">Parent Background</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Achievement%22">Mathematics Achievement</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
– Name: DOI
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  Data: 10.1080/00220973.2020.1855407
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  Label: ISSN
  Group: ISSN
  Data: 0022-0973
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A growing body of research suggests that utility-value interventions can promote students' academic motivation and achievement. Moreover, there is evidence that minimal interventions are particularly useful for ethnic minority and first-generation students at college. Whether this is also the case with high school students belonging to minorities and having low parental educational background, is unclear. In a double-blind randomized field experiment with N = 439 academic-track students from 9th grade in Germany, we investigated whether a short version of an established utility-value intervention (i.e., quotations evaluation intervention) would promote the students' utility, attainment, and intrinsic values in math and their math test performance after the intervention. Moreover, we investigated if such short-term intervention effects were moderated by students' migration background and parental educational background. We found significant positive main effects of the intervention on the students' utility and attainment values in math compared to a control group. The effect on attainment value was especially pronounced for students with migration background whose parents held no university entrance certificate. We discuss the practical relevance of these findings and highlight challenges for future research in this field.
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  Data: 2022
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  Data: EJ1328433
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      – Text: English
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        PageCount: 19
        StartPage: 364
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: High School Students
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      – SubjectFull: Grade 9
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      – SubjectFull: Mathematics Achievement
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      – SubjectFull: Germany
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      – TitleFull: Utility-Value Intervention in School: Students' Migration and Parental Educational Backgrounds as Moderators
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