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Value-Reappraisal and Goal-Setting Intervention Effects on Attitudes and Performance in College Statistics

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Title: Value-Reappraisal and Goal-Setting Intervention Effects on Attitudes and Performance in College Statistics
Language: English
Authors: Acee, Taylor W.
Source: Journal of Experimental Education. 2023 91(2):298-316.
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: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Goal Orientation, Undergraduate Students, Intervention, Statistics Education, Student Motivation, Academic Achievement, Teaching Methods, Introductory Courses, Task Analysis, Learning Processes, Outcomes of Education, Scores, Tests, Prediction, Competence, Learning Strategies, Questionnaires, Likert Scales, Student Attitudes
Assessment and Survey Identifiers: Motivated Strategies for Learning Questionnaire
DOI: 10.1080/00220973.2021.1993773
ISSN: 0022-0973
1940-0683
Abstract: The purpose of this study was to test the effects of a value-reappraisal intervention (VR) on students' motivation and performance compared to a goal-setting intervention (GS) and information-literacy control condition (C). Eighty-eight female students in an undergraduate introductory statistics course were randomly assigned to one of the three conditions. VR yielded statistically significant increases in students' intrinsic value, endogenous instrumentality, task value, and intentions to continue learning statistics, but not perceived competence. GS and C had no effects on these outcomes. For exam performance, in one course section VR benefited students with lower preintervention exam scores; there were no intervention effects on exam performance in the other section. Examined only for GS, self-reported goal progress predicted changes in perceived competence over two weeks. Theoretical and practical implications are discussed.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1377422
Database: ERIC
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  Value: <anid>AN0161518417;jxe01apr.23;2023Jan30.03:42;v2.2.500</anid> <title id="AN0161518417-1">Value-Reappraisal and Goal-Setting Intervention Effects on Attitudes and Performance in College Statistics </title> <p>The purpose of this study was to test the effects of a value-reappraisal intervention (VR) on students' motivation and performance compared to a goal-setting intervention (GS) and information-literacy control condition (C). Eighty-eight female students in an undergraduate introductory statistics course were randomly assigned to one of the three conditions. VR yielded statistically significant increases in students' intrinsic value, endogenous instrumentality, task value, and intentions to continue learning statistics, but not perceived competence. GS and C had no effects on these outcomes. For exam performance, in one course section VR benefited students with lower preintervention exam scores; there were no intervention effects on exam performance in the other section. Examined only for GS, self-reported goal progress predicted changes in perceived competence over two weeks. Theoretical and practical implications are discussed.</p> <p>Keywords: Attitude change; college statistics; goal setting; intervention; task value</p> <p>Statistical literacy, one's ability to interpret, scrutinize, and reason with statistics, is essential to understanding everyday information including news, advertisements, and medical information (see Schield, [<reflink idref="bib29" id="ref1">29</reflink>]; Tractenberg, [<reflink idref="bib34" id="ref2">34</reflink>]). Statistics are also widely utilized in the social and natural sciences. Therefore, helping to increase students' statistical literacy is critical to fostering an informed citizenry and skilled workforce. Accordingly, statistics courses are required of many students in postsecondary education. Furthermore, postsecondary education reform initiatives in the U.S. have focused on modifying institutional policies and degree plans to allow students, especially those in the social sciences, liberal arts, and humanities, to take statistics coursework rather than traditionally required mathematics courses, such as developmental mathematics and college algebra (Clyburn, [<reflink idref="bib10" id="ref3">10</reflink>]; Yamada & Bryk, [<reflink idref="bib40" id="ref4">40</reflink>]). However, many students have negative attitudes about learning statistics and struggle to master the content in postsecondary statistics courses (Hood et al., [<reflink idref="bib15" id="ref5">15</reflink>]; van Es & Weaver, [<reflink idref="bib36" id="ref6">36</reflink>]). Attitudes toward learning have been identified as malleable and amenable to change through educational intervention (see Acee et al., [<reflink idref="bib4" id="ref7">4</reflink>]; Weinstein & Acee, [<reflink idref="bib38" id="ref8">38</reflink>]). Therefore, helping students to reappraise positively their attitudes about the personal relevance of learning statistics is one approach to fostering students' motivation and performance in postsecondary statistics courses. Goal setting is another approach to motivating students that has been found to support students' self-efficacy and task performance (Schunk & Ertmer, [<reflink idref="bib30" id="ref9">30</reflink>]).</p> <hd id="AN0161518417-2">Literature review</hd> <p>The literature review addresses theory and research on expectancy-value theory (Eccles et al., [<reflink idref="bib12" id="ref10">12</reflink>]), task value interventions (Harackiewicz & Priniski, [<reflink idref="bib14" id="ref11">14</reflink>]), and the value reappraisal model of task-value intervention effects (Acee et al., [<reflink idref="bib4" id="ref12">4</reflink>]). It also addresses literature on self-regulation (Zimmerman, [<reflink idref="bib41" id="ref13">41</reflink>]) and self-regulatory processes involving proximal goal setting and self-evaluation.</p> <hd id="AN0161518417-3">Expectancy-value theory</hd> <p>Eccles et al. ([<reflink idref="bib12" id="ref14">12</reflink>]) expectancy-value theory posits that students' expectations for success and subjective task values are proximal predictors of their achievement-related motivation and performance in educational settings. <emph>Expectations for success</emph> refer to students' appraisals of the likelihood that they will successfully perform an academic task. <emph>Subjective task values</emph> refer to students' appraisals of the importance of an academic task. Eccles et al. have differentiated four categories of reasons for why one might value or devalue an academic task. <emph>Intrinsic value</emph> refers to an individual's interest and enjoyment in the task. <emph>Utility value</emph> refers to the usefulness of the task for reaching current or future goals. <emph>Attainment value</emph> refers to the importance of a task for expressing or confirming one's identity. <emph>Cost</emph> refers to drawbacks to task engagement such as time, effort, negative emotions, and lost opportunities.</p> <p>Longitudinal research has suggested that students' expectancies and values in mathematics and language arts tend to decrease from elementary school through high school (Jacobs et al., [<reflink idref="bib18" id="ref15">18</reflink>]). Research has also shown that students' expectancies and values decline in college (Robinson et al., [<reflink idref="bib27" id="ref16">27</reflink>]). Students with low and/or declining expectancies and values may find it difficult to generate and sustain motivation in their college courses. Empirical evidence supports that expectancies and values predict students' motivation and performance in K-12 (Wigfield & Eccles, [<reflink idref="bib39" id="ref17">39</reflink>]) and postsecondary (Hood et al., [<reflink idref="bib15" id="ref18">15</reflink>]; Robinson et al., [<reflink idref="bib27" id="ref19">27</reflink>]) educational settings. Expectations for success tend to be stronger predictors of academic performance, whereas subject task values tend to be stronger predictors of continued interest in an academic discipline (Robinson et al., [<reflink idref="bib27" id="ref20">27</reflink>]; Wigfield & Eccles, [<reflink idref="bib39" id="ref21">39</reflink>]). To help improve students' motivation and performance, researchers have developed various interventions designed to bolster students' expectancies and values. Two types of interventions are addressed here: task-value interventions, which have been found to influence students' subjective task values, and goal-setting interventions, which have been found to influence students' expectations for success.</p> <hd id="AN0161518417-4">Task-value interventions</hd> <p> <emph>Task-value interventions</emph> specifically target the modification of students' subjective task values (Acee et al., [<reflink idref="bib4" id="ref22">4</reflink>]; Harackiewicz & Priniski, [<reflink idref="bib14" id="ref23">14</reflink>]). Different types of task-value interventions have been examined in the literature such as utility-value interventions (Hulleman et al., [<reflink idref="bib16" id="ref24">16</reflink>]), value-reappraisal interventions (Acee & Weinstein, [<reflink idref="bib3" id="ref25">3</reflink>]), and task-value instructional inductions (Johnson & Sinatra, [<reflink idref="bib19" id="ref26">19</reflink>]).</p> <p>Much of the work in this area has focused on utility-value interventions, which prompt students to consider the personal usefulness of course learning for reaching goals. This line of research has found that direct communication of utility value and self-generation of one's own utility-value connections can lead students to adopt more positive attitudes toward learning (Harackiewicz & Priniski, [<reflink idref="bib14" id="ref27">14</reflink>]), and for students with more at-risk characteristics such as a history of low performance or low self-efficacy, they tend to perform at higher levels in their courses because of the intervention.</p> <p>Value reappraisal interventions (VR) have also been found to have positive effects on students' subjective task values and course performance (Acee & Weinstein, [<reflink idref="bib3" id="ref28">3</reflink>]). However, VR uses somewhat different approaches than utility value interventions. VR targets a broader spectrum of subjective task values including utility value and intrinsic value. Also, VR includes more direction to help guide students in effortfully reflecting about their attitudes and the personal significance of course learning. Albrecht and Karabenick ([<reflink idref="bib6" id="ref29">6</reflink>]) referred to the approach used in VR as directed reappraisal and differentiated it from self-generation because it provided additional scaffolding that directed the value-reappraisal process. Because there is only one study published on VR, there is a need to reproduce and extend research on value-reappraisal interventions.</p> <hd id="AN0161518417-5">Value reappraisal</hd> <p>We used the value-reappraisal model (Acee et al., [<reflink idref="bib4" id="ref30">4</reflink>]) as a guiding conceptual framework for this study (Figure 1). The value-reappraisal model draws from expectancy-value theory, models of persuasion, and research on self-regulation and self-regulated learning. The model describes task-value interventions as being comprised of <emph>task-value messages</emph>, which communicate reasons for why academic tasks may be personally significant to students and/or <emph>task-value activities</emph>, which prompt students to explore actively the potential value of academic tasks to them, or others close to them (Acee et al., [<reflink idref="bib4" id="ref31">4</reflink>]). The model posits that task-value interventions elicit cognitive-affective responses in students that in turn induce attitude change and influence academic outcomes. The model also imposes a metacognitive layer that suggests students have the potential to self-regulate their own attitudes and that incorporating strategy instruction (Weinstein & Acee, [<reflink idref="bib38" id="ref32">38</reflink>]) into a task-value intervention could foster intentional attitude regulation. Finally, the model shows that intervention effects may be moderated by students' baseline individual differences and classroom-level factors.</p> <p>Graph: Figure 1. The value reappraisal model (Acee et al., [<reflink idref="bib4" id="ref33">4</reflink>]) was adapted to emphasize the model elements being investigated in the current study.</p> <p>Drawing from a rich history of persuasion research (see Vogel & Wanke, [<reflink idref="bib37" id="ref34">37</reflink>]), the value-reappraisal model purports that the level of elaboration of a task-value message increases the potential for attitude change. Processing a message favorably increases the potential for attitude-change in the direction advocated in the message and vice versa. In-depth effortful processing of issues relevant to one's subjective task values is thus a key aspect of value reappraisal. Acee and Weinstein ([<reflink idref="bib3" id="ref35">3</reflink>]) proposed general categories of value-reappraisal strategies that could be used to engage students in effortfully reappraising the value of academic tasks. These include (a) generating rationales for why course learning is personally significant, (b) imagining future possible selves and situations in which course learning could be valuable, and (c) contrasting the pros and cons of engaging in learning tasks.</p> <p>The value-reappraisal intervention (Acee & Weinstein, [<reflink idref="bib3" id="ref36">3</reflink>]) utilized task-value messages that addressed intrinsic, utility, and attainment reasons for why developing statistical knowledge and skills could be personally valuable. The intervention also asked students to complete task-value activities that guided students in using value reappraisal strategies to reflect about the personal significance of developing knowledge and skills in statistics.</p> <p>In a randomized experiment with 82 students, Acee and Weinstein ([<reflink idref="bib3" id="ref37">3</reflink>]) found that VR increased students' ratings on the Motivated Strategies for Learning Questionnaire task-value scale (Pintrich et al., [<reflink idref="bib26" id="ref38">26</reflink>]) and on Husman et al. ([<reflink idref="bib17" id="ref39">17</reflink>]) <emph>endogenous instrumentality</emph> scale (like utility value, this scale measured students' beliefs that learning course content, rather than passing or earning high marks, was useful for attaining future goals). Statistically significant increases were observed on these measures approximately two weeks after receiving the intervention. VR had a positive effect on exam performance in one instructor's section of the course and no effect in the other section, which suggested unspecified classroom-level moderation. More research is needed on VR to determine the reproducibility of its effects on these and other outcomes, including intrinsic value and intentions to continue learning statistics. It is also important to compare VR to alternative motivational interventions requiring similar demands on students' time and effort. In the current study, a brief goal-setting intervention was investigated in comparison to VR. Literature underlying the development of that intervention is discussed next.</p> <hd id="AN0161518417-6">Goal setting and self evaluation</hd> <p>Self-regulated learning refers to the proactive and intentional management of one's thoughts, feelings, and behaviors to optimize learning and reach personally valued goals. Zimmerman's ([<reflink idref="bib41" id="ref40">41</reflink>]) model of self-regulated learning emphasizes three cyclical phases. Forethought involves setting goals and strategically planning how to reach those goals. Performance/volitional control refers to implementing action toward goals and monitoring those actions to optimize learning and performance. Self-reflection involves self-evaluating goal progress, identifying causes of one's successes and failures, and experiencing and managing affective reactions and self-judgments related to one's goal. Because self-regulatory phases are cyclical, self-reflection in turn informs future forethought about related goals.</p> <p>Goal setting and self-evaluation are two self-regulatory strategies that have been found to boost students' self-efficacy and performance on learning tasks (see Schunk et al., [<reflink idref="bib31" id="ref41">31</reflink>]). Research on goal setting has found the impact of goals on learning and achievement to be dependent on various goal properties (Austin & Vancouver, [<reflink idref="bib7" id="ref42">7</reflink>]) such as goal specificity (Acee et al., [<reflink idref="bib2" id="ref43">2</reflink>]), difficulty (Locke & Latham, [<reflink idref="bib22" id="ref44">22</reflink>]), proximity (Bandura & Schunk, [<reflink idref="bib9" id="ref45">9</reflink>]), and whether the goal targets a learning process or outcome (Kitsantas et al., [<reflink idref="bib21" id="ref46">21</reflink>]). Taken together, this research suggests benefits to breaking down distal goals into specific and moderately difficult proximal subgoals that target learning processes rather than outcomes. Research has also suggested that self-evaluating goal progress can increase students' self-efficacy (Schunk & Ertmer, [<reflink idref="bib30" id="ref47">30</reflink>]) and computer skill acquisition (Kitsantas et al., [<reflink idref="bib21" id="ref48">21</reflink>]). Frequent self-evaluation may direct learners' attention to improving learning processes and strengthen self-efficacy when they evaluate goal progress favorably (Schunk & Ertmer, [<reflink idref="bib30" id="ref49">30</reflink>]; Stock & Cervone, [<reflink idref="bib33" id="ref50">33</reflink>]). In sum, these findings suggest motivational and learning benefits of interventions designed to help students set goals with useful properties and self-evaluate their goal progress.</p> <p>However, the literature on goal-setting interventions in education has primarily focused on learning tasks that can be completed in a single session. Preliminary findings from a longitudinal within-subjects research study investing an electronic goal-setting intervention (Marzouk et al., [<reflink idref="bib23" id="ref51">23</reflink>]) suggested that engaging in proximal goal setting and self-evaluating goal progress over four weeks increased measures of metacognitive awareness; however, course performance was not examined. There is a research gap of studies investigating electronic goal-setting interventions designed to support exam studying over multiple weeks.</p> <hd id="AN0161518417-7">Study rationale and research questions</hd> <p>The purpose of this study was to examine the effects of electronic value-reappraisal (VR) and goal-setting (GS) interventions on students' values, expectancies, intentions, and performance compared to a control condition (C). The research questions were as follows:</p> <p></p> <ulist> <item> Do VR and GS affect students' subjective task values (i.e., intrinsic value, endogenous instrumentality, and task value), intentions to continue learning statistics, perceived competence, and exam performance?</item> <p></p> <item> It was hypothesized that VR would influence subjective task values, intentions, and exam performance, whereas GS would affect perceived competence and exam performance. C was not expected to influence study outcomes.</item> <p></p> <item> Do course section and/or preintervention exam moderate intervention effects on exam performance.</item> <p></p> <item> Given past research (Acee et al., [<reflink idref="bib4" id="ref52">4</reflink>]; Harackiewicz & Priniski, [<reflink idref="bib14" id="ref53">14</reflink>]), there was expected potential for course section and preintervention exam to moderate VR intervention effects on exam performance.</item> <p></p> <item> For students in GS, did their ratings of goal progress predict changes in perceived competence from pretest to 2-week delayed posttest?</item> <p></p> <item> Based on theory (Bandura, [<reflink idref="bib8" id="ref54">8</reflink>]; Schunk & Ertmer, [<reflink idref="bib30" id="ref55">30</reflink>]), self-evaluations of goal progress were expected to help explain changes in perceived competence over time.</item> </ulist> <hd id="AN0161518417-8">Methods</hd> <p></p> <hd id="AN0161518417-9">Participants</hd> <p>Ninety-three female undergraduate students were assigned to the study as part of a research participation requirement that was part of their introductory statistics course[<reflink idref="bib1" id="ref56">1</reflink>]. Given gender differences in expectancy-value constructs (Simpkins et al., [<reflink idref="bib32" id="ref57">32</reflink>]) and research showing some gender differences in personal relevance intervention effects (Gaspard et al., [<reflink idref="bib13" id="ref58">13</reflink>]), gender was thought to potentially influence intervention results. With limited sample size available through the subject pool, female students were recruited. Generalizations to men would have been limited, given that the course enrolled approximately 18% male students. Two students assigned to the study did not show up to any study sessions and were removed. Three students did not complete the 2-week delayed posttest measures and were removed. The final sample comprised 88 female students who completed their assigned intervention and all the study measures.[<reflink idref="bib2" id="ref59">2</reflink>] Participants enrolled in one of two course sections taught by different instructors (Section A, <emph>n</emph> = 50; Section B, <emph>n</emph> = 38). The ethnic composition of the sample was as follows: African American (<emph>n</emph> = 11); Asian (<emph>n</emph> = 17); Caucasian (<emph>n</emph> = 42); Hispanic (<emph>n</emph> = 17) and, both Caucasian and Hispanic (<emph>n</emph> = 1). Data on students' year in college was as follows: sophomore (<emph>n</emph> = 19); junior (<emph>n</emph> = 42); and senior (<emph>n</emph> = 27). The sample had an average age of 20.51 (<emph>SD</emph> = 1.57) and students' ages ranged from 18 to 30.</p> <hd id="AN0161518417-10">Research design</hd> <p>Participants were stratified on course section and year in university and randomly assigned to one of three conditions: VR (n = 31), GS (n = 27), and C (n = 30). The repeated measures design included a pretest, immediate posttest, and 2-week delayed posttest for self-reported measures of perceived competence, task value, endogenous instrumentality, intrinsic value, and intentions to continue learning statistics. For students who received GS, goal progress data were collected 3, 7, 10, and 14 days after receiving the intervention. The first course exam given after the administration of the intervention was used as a performance outcome.</p> <hd id="AN0161518417-11">Procedures</hd> <p>Table 1 depicts the study procedures. Students were asked to attend two sessions held in a computer lab on campus. For Session 1, after informed consent, students were asked to log onto a computer and complete the pretest measures. From a designated website, students downloaded the file containing the intervention or control condition to which they were randomly assigned. When students finished, the researcher checked that the file was saved, and the work was complete. If incomplete, students were given an opportunity to finish; students sometimes asked logistical questions, but never about the content of their responses to the intervention. Next, students were asked to complete the immediate posttest measures, which were identical to the pretest measures. After approximately 2 weeks, students came back for Session 2, in which they completed the 2-week delayed posttest measures followed by a set of demographic items.</p> <p>Table 1. Overview of study procedures.</p> <p> <ephtml> <table><thead><tr><td>Stage of project</td><td>Week of the semester</td><td>Activity</td></tr></thead><tbody valign="top"><tr><td>Preintervention course exams</td><td>Weeks 4 and 7</td><td><list list-type="Bullet"><list-item><p>Course Section A: exam given on week 4</p></list-item><list-item><p>Course Section B: exam given on week 7</p></list-item></list></td></tr><tr><td>Session 1</td><td>Weeks 8–9</td><td><list list-type="Bullet"><list-item><p>Students took pretest measures</p></list-item><list-item><p>Students completed VR, GS, or Control</p></list-item><list-item><p>Students took immediate posttest measures</p></list-item></list></td></tr><tr><td>2-Week delay</td><td>Weeks 8–11</td><td><list list-type="Bullet"><list-item><p>GS completed 1st, 2nd, and 3rd self-evaluation</p></list-item></list></td></tr><tr><td>Session 2</td><td>Weeks 10–11</td><td><list list-type="Bullet"><list-item><p>GS completed 4th self-evaluation</p></list-item><list-item><p>Students took 2-week delayed posttest measures</p></list-item><list-item><p>Students took demographic survey</p></list-item></list></td></tr><tr><td>Post-intervention course exams</td><td>Weeks 13 and 15</td><td><list list-type="Bullet"><list-item><p>Course Section A: exam given on week 13</p></list-item><list-item><p>Course Section B: exam given on week 15</p></list-item></list></td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph> Pretest and posttest self-report measures included task-value, endogenous instrumentality, intrinsic value, intentions to continue learning strategies, and perceived competence.</p> <hd id="AN0161518417-12">Description of intervention and control conditions</hd> <p>Intervention materials were in the form of Microsoft Word files downloaded from a designated website. For each condition, students read a series of reading passages and completed activities. Students typed their responses to the activities directly into the file. The number of passages, activities, and approximate time it took to complete each condition were as follows: VR (7 passages, 7 activities, 80 min.), GS (5 passages, 5 activities, 80 min.), and C (3 modules, 3 activities, 80 min.).</p> <hd id="AN0161518417-13">Value-reappraisal intervention (VR)</hd> <p>VR contained task-value messages about the importance of becoming an intelligent consumer of statistics in everyday life (attainment value), academic and professional uses of statistics (utility value), and the intrinsic enjoyment of learning statistics (intrinsic value; Acee & Weinstein, [<reflink idref="bib3" id="ref60">3</reflink>]). Furthermore, task-value writing activities prompted students to explore the value of learning statistics using value reappraisal strategies (i.e., generating rationales, imagining possible future selves and situations, and contrasting pros and cons of learning task engagement). Also, awareness of attitudes and strategies for regulating attitudes were addressed. Students were asked to read seven short passages, and each passage was followed by a brief writing activity. The value reappraisal intervention (VR) tested in this study used the same information and approaches as the one described in Acee and Weinstein ([<reflink idref="bib3" id="ref61">3</reflink>], pp. 498–499); however, four revisions were made. First, minor revisions were made to the wording of the intervention, so it was clearer. Second, the opening section about attitudes was revised to focus on students' introductory statistics course, rather than college courses in general. Third, in the section on intrinsic value, three additional examples were provided, and an additional activity asked students to choose strategies that they could use to increase their intrinsic value for their statistics course. Fourth, the intervention took approximately 5 minutes longer to complete.</p> <hd id="AN0161518417-14">Goal-setting intervention (GS)</hd> <p>The purpose of GS was to help students set goals for exam studying and self-evaluate goal progress. The intervention was implemented approximately five weeks prior to the exam to facilitate early exam preparation through engagement in goal setting and self-evaluation of goals over two weeks. Students who participated in GS read five short passages related to goal setting and completed five activities designed to help them set and revise goals. GS asked students to choose two exam learning objectives and set proximal subgoals for reaching those learning objectives that could be accomplished within two weeks. For each learning objective, students were asked to set two goals for what to study and two goals for how to study[<reflink idref="bib3" id="ref62">3</reflink>]. Students were also guided in revising their goals so that they were more useful. Instruction focused on setting specific and measurable goals that were challenging yet realistic and within a specified timeframe. Additionally, GS participants were given hard and electronic copies of their learning objectives and proximal subgoals (for example student responses, see Online Supplement A). Students were also asked to self-evaluate their goals 3, 7, 10, and 14 days after the intervention. Self-evaluation involved students rating their level of goal progress and reflecting about their goal striving and how to advance goal progress (see Online Supplement B for a detailed description of GS).</p> <hd id="AN0161518417-15">Information literacy control condition (C)</hd> <p>Students who received C completed three online information literacy tutorial modules, took the end-of-module quiz repeatedly until earning 100%, and answered three reflective questions about each module. The modules were used for C because completing them was not expected to impact the variables of interest but could potentially help students with their library and internet research. Modules were accessed online via the university's website. Module 1 (Selecting) was designed to help students learn to select sources appropriate for academic research. Module 2 (Searching) was designed to help students learn to effectively search library databases and the Web. Module Three (Evaluating) was designed to help students learn how to locate and evaluate print and online sources.</p> <hd id="AN0161518417-16">Measures</hd> <p>Self-report measures used a 7-point Likert-type response scale ranging from 1 = <emph>strongly disagree</emph> to 7 = <emph>strongly agree</emph>. They were administered online via SurveyMonkey. Measures included perceived competence (Kaplan & Midgley, [<reflink idref="bib20" id="ref63">20</reflink>]), task value (Pintrich et al., [<reflink idref="bib26" id="ref64">26</reflink>]), endogenous instrumentality (Husman et al., [<reflink idref="bib17" id="ref65">17</reflink>]), and intentions to continue learning statistics.</p> <hd id="AN0161518417-17">Perceived competence</hd> <p>Students' perceived competence for course tasks was measured with the Perceived Academic Competence Scale (PACS; Kaplan & Midgley, [<reflink idref="bib20" id="ref66">20</reflink>]), which has been shown to have strong internal consistency reliability and construct validity. Items were modified to refer to students' statistics course. Also, because one item was double-barreled and referred to "assignments and tests", the item was divided into two separate items - one referring to assignments and the other tests – bringing the total scale items to eight.[<reflink idref="bib4" id="ref67">4</reflink>] Internal consistency reliability of the scale for the current study was α = 0.91.</p> <hd id="AN0161518417-18">Task value</hd> <p>Students' subjective task value was assessed with the six-item Task Value subscale of the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., [<reflink idref="bib26" id="ref68">26</reflink>]), which includes items that address the general importance, usefulness, and enjoyment of course tasks. This scale has shown strong reliability and validity evidence (see Duncan & McKeachie, [<reflink idref="bib11" id="ref69">11</reflink>]). Reliability of the scale for the current study was α = 0.92.</p> <hd id="AN0161518417-19">Endogenous instrumentality</hd> <p>The 4-item endogenous instrumentality scale (Husman et al., [<reflink idref="bib17" id="ref70">17</reflink>]) was used to measure a facet of utility value regarding the perceived usefulness of developing course knowledge and skills for attaining future goals. Husman et al. ([<reflink idref="bib17" id="ref71">17</reflink>]) provided theoretical reasoning and factor analytic results suggesting that endogenous instrumentality had strong reliability and was related yet distinct from MSLQ measures of task value and intrinsic motivation.[<reflink idref="bib5" id="ref72">5</reflink>] For the current study, the reliability of the scale was α = 0.91.</p> <hd id="AN0161518417-20">Intrinsic value</hd> <p>Intrinsic value (interest or enjoyment in a task) was measured with the 7-item Interest/Enjoyment scale of the Intrinsic Motivation Inventory (Ryan, [<reflink idref="bib28" id="ref73">28</reflink>]). The Interest/Enjoyment scale was found to have adequate reliability (McAuley et al., [<reflink idref="bib24" id="ref74">24</reflink>]). For this study, items were modified to refer to students' statistics course. The reliability of the scale for this study was α = 0.95.</p> <hd id="AN0161518417-21">Intentions to continue learning statistics</hd> <p>Eccles et al. ([<reflink idref="bib12" id="ref75">12</reflink>]) and Meece et al. ([<reflink idref="bib25" id="ref76">25</reflink>]) used a single item to measure students' intentions to take more math in the future if it was not required. Aligned with this intention construct, I developed a six-item scale to measure students' intentions to continue learning statistics if it was not required. I conducted a pilot study with 103 university students enrolled in an introductory statistics course and the items were found to be unidimensional based on inspection of the scree plot and eigenvalues derived from exploratory factor analysis; the scale also showed strong reliability (<emph>α</emph> =.92). In the current study, the scale was modified by making three of the items negatively worded and rewording two of the items so that the contextual element of the items preceded the item stem (see Appendix). Internal consistency reliability of the scale for the current study was α = 0.88.</p> <hd id="AN0161518417-22">Exam performance</hd> <p>The first exam given after the administration of the interventions and control condition was used as a dependent variable. For Section A, the third course exam was used (given approximately 4–5 weeks after the administration of the intervention). Section B's second exam, administered about 6–7 weeks following the intervention, was used. The content and format of exams varied across section. Exam topics similar across courses were as follows: related and independent samples t tests; correlation; and simple linear regression. Differences in exam topics were that Section A included Chi-square test of association and Section B included probability, sampling distributions, inference about means, and analysis of variance. In addition, Section B used an open-book, online, take-home exam, whereas Section A used a timed, in-person, paper-and-pencil exam that was not open-book. Z-score transformation was used to standardize exam scores within each course section, yielding a mean of 0 and standard deviation of 1 within each course section.</p> <hd id="AN0161518417-23">Goal progress</hd> <p>Students in GS were asked to choose two learning objectives for their upcoming statistics exam. For each learning objective, they were asked to set four proximal sub-goals that, if reached, could enable the attainment of the learning objective. On four occasions (i.e., 3, 7, 10, and 14 days after the intervention), using a Likert-type response scale ranging from 1 = <emph>no progress</emph>, 2 = <emph>a little progress</emph>, 3 = <emph>some progress</emph>, 4 = <emph>a fair amount of progress</emph>, and 5 = <emph>very much progress</emph>, students self-evaluated their goal progress toward reaching the two learning objectives and eight proximal sub-goals. As an example, a participant in the study chose the following learning objective,</p> <p>Identify and/or be able to provide definitions and interpretations of: (a) probability, random process, simulation, and random variable (continuous, discrete, dichotomous, binomial) (Unit 7); and, (b) parameter, statistic, population, sample bias, sampling distributions and the central limit theorem (Unit 8).</p> <p>and set four proximal subgoals to reach it, of which two examples are provided here as follows, "On Wednesday, October 15, 10am-10:40, I will read over the definition of parameter and statistic and come up with two examples of each." "On Thursday, October 16, 5 pm–6:40pm, I will read unit 7 and unit 8." For more examples of participants' goals see Online Supplement A. Students' ratings of goal progress were highly consistent across the 10 goals they evaluated at each timepoint. Cronbach alpha reliability coefficient values computed for the four timepoints were 0.94, 0.93, 0.94, and 0.98, respectively.</p> <hd id="AN0161518417-24">Data analyses</hd> <p>To examine intervention effects on self-report measures over time, repeated measures analysis of variance (ANOVA) tests, 3 Group (VR, GS, C) x 3 Time (pretest, immediate posttest, 2-week delayed posttest), were conducted for perceived competence, task value, endogenous instrumentality, intrinsic motivation, and intentions to continue learning statistics. The Greenhouse-Geisser degrees of freedom adjustments were employed to help correct for potential violations of the sphericity assumption, and Bonferroni adjustments were used to help control inflation of Type I Error when delineating main effects and interactions. For standardized postintervention exam, multiple regression analysis was used to analyze intervention effects controlling for standardized preintervention exam. Dummy coded variables were created for VR (VR = 1, GS and C = 0) and GS (GS = 1, VR and C = 0), with C as the reference group, and entered as predictors of standardized postintervention exam. Interactions with course section and preintervention exam were examined and only retained if statistically significant. For students in GS, goal progress was examined as a predictor of changes in perceived competence. Using multiple regression, goal progress and pretest perceived competence were entered as predictors of perceived competence at two-week delayed posttest.</p> <hd id="AN0161518417-25">Results</hd> <p>In this section, I summarize the results of preliminary analyses used to examine the reliability of the study measures and their intercorrelations, preexisting differences between groups at pretest, treatment fidelity, statistical assumptions, and if course section moderated intervention effects and needed to be included in the primary analyses. I also present the results of the primary analyses used to test the study hypotheses.</p> <hd id="AN0161518417-26">Preliminary analyses</hd> <p>Internal-consistency reliability analyses of the pretest self-report measures (reported in the methods section for each scale) yielded strong Cronbach's alpha coefficients ranging between.88 and.95. Pearson product-moment correlation coefficients showed statistically significant positive intercorrelations among the self-report pretest measures; all were moderate in size, ranging from.37 to.54, apart from the strong correlations between task value and endogenous instrumentality (<emph>r</emph> =.78) and task value and intrinsic value (<emph>r</emph> =.80). Although these strong correlations with the task value scale raised concerns about redundancy, the task value scale was retained as a dependent measure for the purposes of examining the reproducibility of intervention effects on this measure, and for the reasons provided in Acee and Weinstein ([<reflink idref="bib3" id="ref77">3</reflink>], p. 500).</p> <p>Table 2 provides means and standard deviations by group for the self-report measures at each time point. There were no statistically significant differences between intervention groups on any of the pretest self-report measures, pretest standardized exam scores, age, course section, race/ethnicity, and year in university. These null findings supported that groups were comparable at pretest. No violations of statistical assumptions were found.[<reflink idref="bib6" id="ref78">6</reflink>]</p> <p>Table 2. Change in self-report dependent variables by group.</p> <p> <ephtml> <table><thead><tr><td /><td>Pretest</td><td>Immediate posttest</td><td>2-Week posttest</td></tr><tr><td /><td><italic>M</italic></td><td><italic>SD</italic></td><td><italic>M</italic></td><td><italic>SD</italic></td><td><italic>M</italic></td><td><italic>SD</italic></td></tr></thead><tbody valign="top"><tr><td><italic>Perceived competence</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Control</td><td>5.48</td><td>1.11</td><td char=".">5.51</td><td char=".">.89</td><td char=".">5.39</td><td char=".">.90</td></tr><tr><td>GS</td><td>5.72</td><td>1.01</td><td char=".">5.83</td><td char=".">.90</td><td char=".">5.80</td><td char=".">.94</td></tr><tr><td>VR</td><td>5.73</td><td>.97</td><td char=".">5.92</td><td char=".">.79</td><td char=".">5.69</td><td char=".">.92</td></tr><tr><td><italic>Task value</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Control</td><td>3.78</td><td>1.39</td><td char=".">3.54</td><td char=".">1.23</td><td char=".">3.72</td><td char=".">1.33</td></tr><tr><td>GS</td><td>4.12</td><td>1.31</td><td char=".">3.89</td><td char=".">1.33</td><td char=".">4.05</td><td char=".">1.52</td></tr><tr><td>VR</td><td>4.39<sub>a</sub></td><td>1.37</td><td char=".">4.97<sub>a</sub></td><td char=".">1.17</td><td char=".">4.72</td><td char=".">1.19</td></tr><tr><td><italic>Endogenous instrumentality</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Control</td><td>4.09</td><td>1.30</td><td char=".">4.07</td><td char=".">1.27</td><td char=".">4.03</td><td char=".">1.41</td></tr><tr><td>GS</td><td>4.22</td><td>1.15</td><td char=".">4.20</td><td char=".">1.22</td><td char=".">4.24</td><td char=".">1.45</td></tr><tr><td>VR</td><td>4.38<sub>ab</sub></td><td>1.52</td><td char=".">5.47<sub>a</sub></td><td char=".">1.15</td><td char=".">5.15<sub>b</sub></td><td char=".">1.23</td></tr><tr><td><italic>Intrinsic value</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Control</td><td>3.00</td><td>1.25</td><td char=".">2.89</td><td char=".">1.13</td><td char=".">2.80</td><td char=".">1.20</td></tr><tr><td>GS</td><td>2.99</td><td>1.49</td><td char=".">3.07</td><td char=".">1.52</td><td char=".">2.89</td><td char=".">1.61</td></tr><tr><td>VR</td><td>3.10<sub>a</sub></td><td>1.31</td><td char=".">3.62<sub>ab</sub></td><td char=".">1.36</td><td char=".">3.34<sub>b</sub></td><td char=".">1.38</td></tr><tr><td><italic>Intentions to continue learning statistics</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Control</td><td>2.54</td><td>1.09</td><td char=".">2.56</td><td char=".">1.00</td><td char=".">2.52</td><td char=".">1.13</td></tr><tr><td>GS</td><td>2.79</td><td>1.24</td><td char=".">2.94</td><td char=".">1.25</td><td char=".">2.96</td><td char=".">1.36</td></tr><tr><td>VR</td><td>2.42<sub>ab</sub></td><td>1.07</td><td char=".">3.23<sub>a</sub></td><td char=".">1.26</td><td char=".">3.21<sub>b</sub></td><td char=".">1.23</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note.</emph> Means in the same row sharing the same subscript are significantly different at <emph>p</emph> <.05. Control (<emph>n</emph> = 30), GS (<emph>n</emph> = 27), and VR (<emph>n</emph> = 31). Bonferroni adjustments were used in calculating the statistical significance of pairwise comparisons. Scale ranged from 1 to 7.</p> <p>To assess treatment fidelity, the researcher examined student responses to VR, GS, and C for completion. All students in each condition completed all activities and their responses were germane to the activity prompt. For students' final goal revisions, the researcher determined that all goals included (a) a standard to reach that was relevant to the learning objective and (b) a timeframe for achieving their goal that was within two weeks (for example goals, see Online Supplement A). When asked at two-week delayed posttest about how much of the intervention they read (and told their response would not affect their research participation credit), on average, students reported reading 90.42% (<emph>SD</emph> = 1.99) of the material and this did not vary statistically significantly by intervention group.</p> <p>To examine if course section moderated intervention effects and needed to be included in the primarily analyses, preliminary analyses were conducted for each dependent variable. For self-report dependent variables, there were not statistically significant main or interaction effects involving course section; therefore, the course section variable was not included in the primary analyses when testing these outcomes. For exam performance, a three-way interaction between course section, intervention group, and preintervention exam was observed, therefore, course section was retained in the primary analyses involving exam performance.</p> <p>Table 3 provides means and standard deviations for exam scores in each section, prior to standardizing the scores. In both course sections, students' preintervention exam scores were statistically significantly higher than their postintervention course exam scores (Section A: <emph>M<subs>d</subs></emph> = 6.92, <emph>SD</emph> = 12.21, <emph>t</emph> = 4.01, <emph>p</emph> < 0.000; Section B: <emph>M<subs>d</subs></emph> = 5.47, <emph>SD</emph> = 9.92, <emph>t</emph> = 3.40, <emph>p</emph> = 0.002). This suggests that the postintervention exams were more difficult than the preintervention exams. This could be due to the course content and exams growing in complexity over time in both sections of the course. Overall students' exam scores were high. Although very few students (1.1 − 2.3%) earned the highest possible score on an exam, the stronger clustering of exam scores at the higher end of the scale and the relatively lower variation in exam scores in Section B are notable concerns indicative of possible ceiling effects (Uttl, [<reflink idref="bib35" id="ref79">35</reflink>]). Restricted variation in postintervention exam scores in Section B could reduce power to detect intervention effects. Giver both empirical and pedagogical differences in exams between course sections, primary analyses involving exam scores were run separately for each course section when probing the statistically significant three-way interaction between course section, preintervention exam, and intervention group.</p> <p>Table 3. Descriptive statistics for exam performance by group by section.</p> <p> <ephtml> <table><thead><tr><td>Group</td><td>Preintervention exam</td><td>Postintervention exam</td></tr></thead><tbody valign="top"><tr><td /><td><italic>M</italic></td><td><italic>SD</italic></td><td><italic>M</italic></td><td><italic>SD</italic></td></tr><tr><td /><td>Section A (<italic>n</italic> = 50)</td></tr><tr><td>Control</td><td>88.38</td><td>7.97</td><td char=".">82.26</td><td char=".">16.47</td></tr><tr><td>GS</td><td>90.54</td><td>7.58</td><td char=".">76.93</td><td char=".">17.21</td></tr><tr><td>VR</td><td>88.34</td><td>10.14</td><td char=".">85.63</td><td char=".">11.64</td></tr><tr><td>Section A</td><td>88.97</td><td>8.64</td><td char=".">82.05</td><td char=".">15.15</td></tr><tr><td /><td>Section B (<italic>n</italic> = 38)</td></tr><tr><td>Control</td><td>94.46</td><td>3.38</td><td char=".">88.31</td><td char=".">7.20</td></tr><tr><td>GS</td><td>93.23</td><td>3.42</td><td char=".">84.62</td><td char=".">12.66</td></tr><tr><td>VR</td><td>93.33</td><td>4.29</td><td char=".">92.00</td><td char=".">5.33</td></tr><tr><td>Section B</td><td>93.68</td><td>3.65</td><td char=".">88.21</td><td char=".">9.30</td></tr></tbody></table> </ephtml> </p> <hd id="AN0161518417-27">Primary analyses</hd> <p>This section contains results of the primary analyses used to test the study hypotheses. The effects of the interventions were tested on self-report motivational variables and postintervention exam performance. For GS, goal progress was examined as a predictor of changes in perceived competence.</p> <hd id="AN0161518417-28">Perceived competence</hd> <p>Repeated measures ANOVA results revealed no statistically significant intervention effects on perceived competence over time.</p> <hd id="AN0161518417-29">Task value</hd> <p>A statistically significant Group x Time interaction (<emph>F</emph> (3.62, 153.83) = 5.22, <emph>p</emph> =.001, <emph>η<subs>p</subs><sups>2</sups></emph> =.11) was found for task value. Post hoc tests suggested no statistically significant change over time on task value for C and GS, whereas VR made statistically significant gains on task value from pretest to immediate posttest (<emph>M</emph>-difference =.59, <emph>SE</emph> =.13, <emph>CI</emph> =.26 to.91, <emph>p</emph> <.001, <emph>d</emph> =.46). Although VR effects on task value were not found to attenuate significantly from immediate posttest to two-week delayed posttest, VR did not evidence statistically significant gains on task value from pretest to 2-week delayed posttest.</p> <hd id="AN0161518417-30">Endogenous instrumentality</hd> <p>A statistically significant Group x Time interaction (<emph>F</emph> (3.79, 161.09) = 7.54, <emph>p</emph> <.001, <emph>η<subs>p</subs><sups>2</sups></emph> =.15) was detected for endogenous instrumentality. Post-hoc tests showed that neither GS nor C made statistically significant gains or losses on endogenous instrumentality over time. Conversely, VR made statistically significant gains from pretest to immediate posttest (<emph>M-</emph>difference = 1.09, <emph>SE</emph> =.16, <emph>CI</emph> =.69 to 1.48, <emph>p</emph> <.001, <emph>d</emph> =.81) and from pretest to 2-week delayed posttest (<emph>M-</emph>difference =.77, <emph>SE</emph> =.19, <emph>CI</emph> =.32 to 1.23, <emph>p</emph> <.001, <emph>d</emph> =.56). Attenuation was not statistically significant from immediate posttest to two-week delayed posttest.</p> <hd id="AN0161518417-31">Intrinsic value</hd> <p>A Group x Time interaction (<emph>F</emph> (3.91, 166.30) = 4.49, <emph>p</emph> =.002, <emph>η<subs>p</subs><sups>2</sups></emph> =.10) was detected for intrinsic value. Post-hoc tests suggested that neither GS nor C made statistically significant gains or losses on intrinsic value over time. On the other hand, VR made statistically significant gains from pretest to immediate posttest (<emph>M-</emph>difference =.52, <emph>SE</emph> =.10, <emph>CI</emph> =.27 to.77, <emph>p</emph> <.001, <emph>d</emph> =.39). These intervention effects were found to attenuate significantly from immediate posttest to two-week delayed posttest (<emph>M-</emph>difference =.29, <emph>SE</emph> =.12, <emph>CI</emph> =.01 to.57, <emph>p</emph> =.045, <emph>d</emph> =.29), and VR did not make statistically significant changes from pretest to 2-week delayed posttest.</p> <hd id="AN0161518417-32">Intentions to continue learning statistics</hd> <p>A statistically significant Group x Time interaction (<emph>F</emph> (3.92, 166.39) = 6.89, <emph>p</emph> <.001, <emph>η<subs>p</subs><sups>2</sups></emph> =.14) was detected on intentions to continue learning statistics. Post-hoc tests showed that neither GS nor C changed significantly over time on this variable. Conversely, VR made statistically significant gains from pretest to immediate posttest (<emph>M-</emph>difference =.81, <emph>SE</emph> =.13, <emph>CI</emph> =.49 to 1.13, <emph>p</emph> <.001, <emph>d</emph> =.70) and from pretest to 2-week delayed posttest (<emph>M-</emph>difference =.79, <emph>SE</emph> =.14, <emph>CI</emph> =.45 to 1.13, <emph>p</emph> <.001, <emph>d</emph> =.68). These intervention effects did not attenuate significantly from immediate posttest to two-week delayed posttest.</p> <hd id="AN0161518417-33">Exam performance</hd> <p>Multiple regression was used to examine intervention effects on standardized postintervention exam scores, controlling for standardized preintervention exam scores. For the joint purpose of testing homogeneity of regression slopes assumptions and testing the second research question regarding moderation, preintervention exam and course section were examined as moderators of intervention effects on postintervention exam. Intervention group (dummy coded with control as the reference category), course section (dummy coded as 1 = Section A, 0 = Section B), preintervention exam, and all two-way and three-way interactions were regressed on standardized postintervention exam (11 predictors). Findings suggested a statistically significant three-way interaction (VR x Preintervention Exam x Course Section: <emph>b</emph> = −.93, <emph>SE</emph> =.45, <emph>β</emph> = −.50, <emph>p</emph> =.041) and a statistically significant two-way interaction (Preintervention Exam x Course Section: <emph>b</emph> = 1.18, <emph>SE</emph> =.35, <emph>β</emph> =.89, <emph>p</emph> =.001),[<reflink idref="bib7" id="ref80">7</reflink>] none of the other interactions were statistically significant.</p> <p>To probe the three-way interaction, we ran separate regression analyses for Section A and Section B[<reflink idref="bib8" id="ref81">8</reflink>] and tested the main and interactive effects of intervention group and preintervention exam (5 predictors). Given low variation in exam scores observed in Section B, there was concern about limited power to detect intervention effects in Section B. Following Aiken and West ([<reflink idref="bib5" id="ref82">5</reflink>]), main effects were entered into the first block; in the second block interaction terms were added. For Section B, adding the interaction terms did not result in a statistically significant <emph>R</emph><sups>2</sups> change, also the model of main effects was not statistically significant. For Section A, adding the interaction terms to the second block yielded a statistically significant <emph>R</emph><sups>2</sups> change in models (<emph>F</emph> (<reflink idref="bib2" id="ref83">2</reflink>, 44) = 4.91, <emph>p</emph> =.012, Δ<emph>R</emph><sups>2</sups> =.10) and the model including the interaction terms was statistically significant (<emph>F</emph> (<reflink idref="bib5" id="ref84">5</reflink>, 44) = 10.40, <emph>p</emph> <.001, <emph>R</emph><sups>2</sups> =.54).[<reflink idref="bib9" id="ref85">9</reflink>] Examination of regression coefficients (see Table 4) showed that the interaction between VR and preintervention exam was statistically significant and the interaction between GS and preintervention exam was not. Probing of the VR x preintervention exam interaction in Section A showed, at one standard deviation lower on preintervention exam, VR had statistically significantly higher postintervention exam scores than C (<emph>b</emph> =.84, <emph>SE</emph> =.33, β =.41, <emph>p</emph> =.014) and GS (<emph>b</emph> = 1.39, <emph>SE</emph> =.39, β =.68. <emph>p</emph> =.001), but GS was not significantly different from C (see Figure 2). At one standard deviation above the mean, no differences between groups were found.</p> <p>Graph: Figure 2. This figure depicts the VR x pre-intervention exam interaction found in Section A. For preintervention exam scores one standard deviation below the mean (-1SD), VR had statistically significantly higher postintervention exam scores than the other two groups. For preintervention exam scores one standard deviation above the mean, no statistically significant group differences were observed.</p> <p>Table 4. Interaction effects for Section A and null effects for Section B on exam performance.</p> <p> <ephtml> <table><thead><tr><td>Predictors</td><td>Block 1 (main effects)</td><td>Block 2 (interactions)</td></tr></thead><tbody valign="top"><tr><td /><td><italic>b</italic></td><td><italic>SE</italic></td><td><italic>β</italic></td><td><italic>b</italic></td><td><italic>SE</italic></td><td><italic>β</italic></td></tr><tr><td /><td>Section A (<italic>n</italic> = 50)</td></tr><tr><td>Preintervention exam</td><td>0.63</td><td>0.11</td><td char=".">0.63***</td><td char=".">0.97</td><td char=".">0.19</td><td char=".">0.97***</td></tr><tr><td>GS</td><td>–0.51</td><td>0.28</td><td>–0.23</td><td>–0.59</td><td char=".">0.26</td><td>–0.27*</td></tr><tr><td>VR</td><td>0.23</td><td>0.26</td><td char=".">0.11</td><td char=".">0.18</td><td char=".">0.24</td><td char=".">0.09</td></tr><tr><td>GS x preintervention exam</td><td /><td /><td /><td>–0.04</td><td char=".">0.30</td><td>–0.02</td></tr><tr><td>VR x preintervention exam</td><td /><td /><td /><td>–0.66</td><td char=".">0.24</td><td>–0.47**</td></tr><tr><td /><td>Section B (<italic>n</italic> = 38)</td></tr><tr><td>Preintervention exam</td><td>0.02</td><td>0.16</td><td char=".">0.02</td><td>–0.21</td><td char=".">0.31</td><td>–0.21</td></tr><tr><td>GS</td><td>–0.39</td><td>0.39</td><td>–0.19</td><td>–0.42</td><td char=".">0.40</td><td>–0.20</td></tr><tr><td>VR</td><td>0.40</td><td>0.40</td><td char=".">0.19</td><td char=".">0.36</td><td char=".">0.41</td><td char=".">0.17</td></tr><tr><td>GS x preintervention exam</td><td /><td /><td /><td char=".">0.40</td><td char=".">0.44</td><td char=".">0.21</td></tr><tr><td>VR x preintervention exam</td><td /><td /><td /><td char=".">0.27</td><td char=".">0.40</td><td char=".">0.17</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note.</emph> Section A: VR (<emph>n</emph> = 19), GS (<emph>n</emph> = 14), Control (<emph>n</emph> = 17). Section B: VR (<emph>n</emph> = 12), GS (<emph>n</emph> = 13), Control (<emph>n</emph> = 13).</item> <item>4 <emph>p</emph> <.05,</item> <item>5 <emph>p</emph> <.01,</item> <item>6 <emph>p</emph> <.001.</item> </ulist> <hd id="AN0161518417-34">Goal progress</hd> <p>Students in GS evaluated their progress toward reaching two exam learning objectives and eight proximal sub-goals on four occasions over two weeks. Table 5 reports students' ratings of goal progress at each timepoint averaged across their 10 goals. To examine changes in goal progress within GS, I ran a repeated measures ANOVA. Results suggested that students' goal progress ratings increased significantly over time (<emph>F</emph> (2.01, 52.16) = 26.63, <emph>p</emph> <.001, <emph>η<subs>p</subs><sups>2</sups></emph> =.51). Post hoc analyses showed that students' goal progress ratings increased significantly at each timepoint (see Table 5). However, on average, students' self-evaluations of goal progress were low to moderate; students reported making approximately little goal progress 3 days after setting their goals and this rose to approximately some goal progress after two weeks.</p> <p>Table 5. Goal progress for GS (<emph>n =</emph> 27).</p> <p> <ephtml> <table><thead><tr><td>Self-evaluation time points (days after setting goals)</td><td><italic>M</italic></td><td><italic>SD</italic></td></tr></thead><tbody valign="top"><tr><td>3 days</td><td>2.08<sub>a</sub></td><td>1.0</td></tr><tr><td>7 days</td><td>2.81<sub>a</sub></td><td>1.06</td></tr><tr><td>10 days</td><td>3.18<sub>a</sub></td><td>.99</td></tr><tr><td>14 days</td><td>3.50<sub>a</sub></td><td>1.19</td></tr></tbody></table> </ephtml> </p> <p>7 <emph>Note.</emph> Statistics reported are for goal progress ratings across the 10 goals students set as part of GS. Means sharing the same subscript are significantly different at <emph>p</emph> <.05. Scale ranged from 1 to 5.</p> <p>To examine if perceived goal progress predicted changes in perceived competence from before to two weeks after the intervention, I ran a multiple regression analysis with pretest perceived competence scores and students' final goal progress ratings as predictors of perceived competence at two-week delayed posttest. The model was statistically significant (<emph>F</emph> (<reflink idref="bib2" id="ref86">2</reflink>, 24) = 126.87, <emph>p</emph> <.001, <emph>R</emph><sups>2</sups> =.91). Goal progress (<emph>b</emph> =.15, <emph>SE</emph> =.05, β =.19, <emph>p</emph> =.008) was found to be a statistically significant positive predictor of perceived competence two weeks after the intervention, controlling for pretest perceived competence scores (<emph>b</emph> =.82, <emph>SE</emph> =.06, β =.88, <emph>p</emph> <.001). This showed that students with lower evaluations of goal progress were more likely to report negative changes in perceived competence and students with higher evaluations of goal progress were more likely to report positive changes in perceived competence.[<reflink idref="bib10" id="ref87">10</reflink>]</p> <hd id="AN0161518417-35">Discussion</hd> <p>With the proliferation of studies on brief, targeted motivational interventions in high school and college settings (Harackiewicz & Priniski, [<reflink idref="bib14" id="ref88">14</reflink>]), the current study shows that a relatively brief (∼80 min.) value-reappraisal intervention, given once mid-semester, has the capacity to improve students' attitudes toward learning statistics and, for students with lower prior exam scores, their exam performance. These findings corroborate and extend research on task-value interventions (Acee et al., [<reflink idref="bib4" id="ref89">4</reflink>]; Harackiewicz & Priniski, [<reflink idref="bib14" id="ref90">14</reflink>]) that address subjective task value constructs within expectancy-value theory (Eccles et al., [<reflink idref="bib12" id="ref91">12</reflink>]).</p> <p>Findings from the current study show some directional alignment between VR effects and the larger body of research on utility-value interventions. More specifically, VR had positive effects on a measure of endogenous instrumentality which overlaps conceptually with utility value (Husman et al., [<reflink idref="bib17" id="ref92">17</reflink>]). In addition, the finding from the current study that VR effects were positive for students with lower prior performance and null for students with higher prior performance converges with findings from utility-value-intervention research that suggest academic performance benefits for students with lower prior performance and other at-risk characteristics (Harackiewicz & Priniski, [<reflink idref="bib14" id="ref93">14</reflink>]).</p> <p>The current study also corroborates and extends Acee and Weinstein ([<reflink idref="bib3" id="ref94">3</reflink>]) who examined VR effects on the same measures of task value and endogenous instrumentality used in the current study and reported similar results and effect sizes. Also similar across both studies, VR did not affect students' perceived competence. This pattern of null findings provides discriminate evidence that VR functions on values rather than expectancies, as hypothesized. Acee and Weinstein ([<reflink idref="bib3" id="ref95">3</reflink>]) did not examine intrinsic value and intentions to continue learning statistics, therefore, the current study offers novel findings that VR positively affects these two constructs. Furthermore, the current study shows stronger and longer lasting VR effects on endogenous instrumentality and intentions to continue learning compared to intrinsic value and task value.</p> <p>The current study is rooted in the tenet that engaging students in active processing about issues relevant to their subjective task values can induce positive attitude change about course learning and improve academic performance. The value-reappraisal model (Acee et al., [<reflink idref="bib4" id="ref96">4</reflink>]) has guided the intervention approaches used in this study which involve task-value messages, task-value activities, and strategy instruction. The findings from this study corroborate that the combination of these approaches produces positive effects on students' subjective task values, intentions, and exam performance. The task-value messages used in VR addressed attainment, utility, and intrinsic value (Eccles et al., [<reflink idref="bib12" id="ref97">12</reflink>]). Task-value messages could have assisted students in generating personal relevance connections and made their engagement in subsequent task-value activities more fruitful (Acee et al., [<reflink idref="bib4" id="ref98">4</reflink>]; Gaspard et al. [<reflink idref="bib13" id="ref99">13</reflink>]). However, providing students with task-value messages alone does not ensure they will reflect effortfully and individualize relevance connections. Therefore, VR incorporated task-value activities that guided students in effortfully scrutinizing the personal significance of learning statistics using value-reappraisal strategies (i.e., generating rationales, imagining future benefits of learning, and contrasting pros and cons of learning task engagement). Models of persuasion suggest that effortful elaboration of message content is more likely to lead to lasting attitude change, whereas peripheral, surface-level, processing is related to fleeting attitude change (see Vogel & Wanke, [<reflink idref="bib37" id="ref100">37</reflink>]). The lasting effects on endogenous instrumentality and intentions in the current study could have been due to the effortful reflection and individualization (Albrecht & Karabenick, [<reflink idref="bib6" id="ref101">6</reflink>]) prompted by VR's task-value activities.</p> <p>Theory and research on self-regulated learning (Zimmerman, [<reflink idref="bib41" id="ref102">41</reflink>]) suggest learning benefits to goal setting and self-evaluation (e.g., Schunk & Ertmer, [<reflink idref="bib30" id="ref103">30</reflink>] and Kitsantas et al. [<reflink idref="bib21" id="ref104">21</reflink>]). However, GS did not yield the hypothesized benefits to students on perceived competence and exam performance. One possible explanation for why GS did not affect the study outcomes is because students reported making little to moderate goal progress on average over the two weeks after they set their goals. Bandura ([<reflink idref="bib8" id="ref105">8</reflink>]) and Schunk and Ertmer ([<reflink idref="bib30" id="ref106">30</reflink>]) have suggested that observing positive goal progress substantiates students' self-efficacy and sustains their motivated behaviors, however, negative evaluations could detract from self-efficacy and motivation. Results from the current study supported these ideas because goal progress was positively correlated with changes in perceived competence over two weeks.</p> <p>Practically, the positive effects of VR coupled with the relatively low time costs to implement the intervention make VR a feasible cost-effective approach to supporting students. Given that VR asks students to generate written responses, time grading could be one concern of instructors. Because VR asks for students' opinions, instructors would be limited to grading on features such as the extent to which students provided an in-depth thoughtful response with relevant examples. To save time, instructors could potentially grade on completion alone. The intervention could also serve as a basis for constructing in-class activities.</p> <hd id="AN0161518417-36">Limitations</hd> <p>Differences in exam content, format, and timing between course sections is a major limitation of this study. Although these differences reflect authentic classroom-level differences in how and when course performance was measured, it presents challenges in comparing exam results across course sections. Also, the observed exam score distributions in Section B presents complications with interpreting null intervention effects for that course section. Because, in Section B, the observed high scores and relatively low levels of variation in exam scores could have limited power to detect intervention effects on postintervention exam. These distributive properties were even more pronounced in the preintervention exam for Section B, which could have limited its capacity to moderate intervention effects. Acquiescence bias is a possible reason for students' reported changes in attitudes toward learning. Measurement of goal progress was limited to self-report ratings of self-set goals with variation in goals between students unaccounted. The generalizability of the study results is limited by the characteristics of the study sample—undergraduate female students at a 4-year public university who were for the most part traditionally aged and Caucasian. Sample size is small for this study, which limits the generalizability and precision of point estimates.</p> <hd id="AN0161518417-37">Future research</hd> <p>The value reappraisal model (Acee et al., [<reflink idref="bib4" id="ref107">4</reflink>]) depicts causal paths among model components, some of which were not examined in the current study (see Figure 1). One such unexplored path is students' immediate cognitive-affective responses to the intervention as a mediator of intervention effects on attitude change. Properties of cognitive-affective responses (e.g., the degree to which students elaborate during the intervention, the valence of their elaborations, and the types of relevance connections they make) should be examined in future research. In the current study, intervention components differentiated in the value-reappraisal model were examined in combination. Therefore, future research is needed to tease apart the main and interactive effects of task-value messages, task-value activities, strategy instruction, and the incorporation of different types of value-reappraisal strategies within the intervention. Future research should also examine classroom-level factors to determine environments in which VR interventions have the greatest potential to help students. Research should also examine the effectiveness of the approaches used in this intervention in other subject areas and for more general purposes such as addressing the personal significance of college. Because research suggests that subjective task values influence effort, choice, and persistence (Eccles et al., [<reflink idref="bib12" id="ref108">12</reflink>]), it stands to reason that VR influenced students through motivational routes. But it is also possible that VR influenced students through cognitive routes, because making personal connections between course tasks and one's existing knowledge and goals is a type of elaboration learning strategy that has the potential to aid memory and deep-level information processing (Weinstein & Acee, [<reflink idref="bib38" id="ref109">38</reflink>]). Accordingly, future research should investigate the extent to which VR influences academic performance through cognitive and motivational routes.</p> <p>Future research on goal setting should examine how the directionality of goal progress evaluations influence intervention effects and ways to mitigate negative effects of negative evaluations. GS should be examined for ways to better support students' goal progress and self-efficacy, for example, through interactions with instructors or tutors that provide feedback and encouragement about students' goals and strategic approaches. Adopting a flexible electronic goal-setting template that allows for continuous goal modification over time such as the one used in Marzouk et al. ([<reflink idref="bib23" id="ref110">23</reflink>]) is another direction for strengthening GS. Future research should systematically examine the level of support and implementation timing of goal-setting interventions used to support exam studying in college courses.</p> <hd id="AN0161518417-38">Conclusion</hd> <p>Given the pervasive use of statistics in everyday life and across diverse occupations coupled with limited appreciation for learning statistics among college student populations, college educators are continually faced with the challenge of motivating students to learn statistics. This study showed that a brief value-reappraisal intervention that incorporated task-value messages, activities, and strategy instruction led students to positively reappraise the value they assign to learning statistics and helped students with lower exam performance improve on subsequent exams. Practical implications support creating pedagogy aimed at communicating reasons for why learning course material could be relevant to students and asking students to actively explore the personal relevance of course learning for themselves.</p> <hd id="AN0161518417-39">Acknowledgments</hd> <p>I would like to acknowledge the late Dr. Claire Ellen Weinstein, Professor Emeritus, University of Texas, for her sage advice and mentorship with this research project. I also acknowledge Giovanna Lorenzi Pinto for her editorial assistance.</p> <ref id="AN0161518417-40"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref56" type="bt">1</bibl> <bibtext> All ethical approvals were obtained for this study through the university's IRB (#2008080096).</bibtext> </blist> <blist> <bibl id="bib2" idref="ref43" type="bt">2</bibl> <bibtext> Acee ([1]) reported data on this same sample in an unpublished dissertation.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref25" type="bt">3</bibl> <bibtext> Marzouk et al. ([23]) also directed students to focus on what and how to study in their goal-setting intervention.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref7" type="bt">4</bibl> <bibtext> In addition, two single-item scales were included, one to measure self-efficacy for exam performance and another to measure self-efficacy for reaching exam learning objectives. Each scale asked about students' next exam in the course. Because these scales were highly correlated with the perceived competence scale and showed the same pattern of results, and for parsimony, we did not include this data in the results.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref72" type="bt">5</bibl> <bibtext> Four items designed to measure exogenous instrumentality were found to have psychometric problems; thus, data for this scale are not included.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref29" type="bt">6</bibl> <bibtext> Although one potential outlier was detected on standardized postintervention exam score, this case was retained in analyses because removing it did not alter the study findings.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref42" type="bt">7</bibl> <bibtext> Probing of this two-way interaction showed that in Section A preintervention exam was a statistically significant positive predictor of postintervention exam (see Table 4), whereas in Section B the relationship was not statistically significant.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref54" type="bt">8</bibl> <bibtext> Another justification for separating results by section is because exam content, format, and timing were different between courses.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref45" type="bt">9</bibl> <bibtext> The main effects model was also statistically significant (<emph>F</emph> (3, 46) = 12.02, <emph>p</emph> <.001, <emph>R</emph><sups>2</sups> =.44) and showed that VR scored statistically significantly higher than GS on standardized postintervention exam (<emph>b</emph> =.73, <emph>SE</emph> =.27, β =.36, <emph>p</emph> =.01), controlling for standardized preintervention exam. 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  Label: Title
  Group: Ti
  Data: Value-Reappraisal and Goal-Setting Intervention Effects on Attitudes and Performance in College Statistics
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Acee%2C+Taylor+W%2E%22">Acee, Taylor W.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Experimental+Education%22"><i>Journal of Experimental Education</i></searchLink>. 2023 91(2):298-316.
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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
– Name: PeerReviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 19
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Goal+Orientation%22">Goal Orientation</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics+Education%22">Statistics Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Introductory+Courses%22">Introductory Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Tests%22">Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Competence%22">Competence</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Likert+Scales%22">Likert Scales</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink>
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  Label: Assessment and Survey Identifiers
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  Data: <searchLink fieldCode="SU" term="%22Motivated+Strategies+for+Learning+Questionnaire%22">Motivated Strategies for Learning Questionnaire</searchLink>
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  Label: DOI
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  Data: 10.1080/00220973.2021.1993773
– Name: ISSN
  Label: ISSN
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  Data: 0022-0973<br />1940-0683
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The purpose of this study was to test the effects of a value-reappraisal intervention (VR) on students' motivation and performance compared to a goal-setting intervention (GS) and information-literacy control condition (C). Eighty-eight female students in an undergraduate introductory statistics course were randomly assigned to one of the three conditions. VR yielded statistically significant increases in students' intrinsic value, endogenous instrumentality, task value, and intentions to continue learning statistics, but not perceived competence. GS and C had no effects on these outcomes. For exam performance, in one course section VR benefited students with lower preintervention exam scores; there were no intervention effects on exam performance in the other section. Examined only for GS, self-reported goal progress predicted changes in perceived competence over two weeks. Theoretical and practical implications are discussed.
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  Data: 2023
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  Data: EJ1377422
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        Value: 10.1080/00220973.2021.1993773
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      – Text: English
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      Pagination:
        PageCount: 19
        StartPage: 298
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      – SubjectFull: Goal Orientation
        Type: general
      – SubjectFull: Undergraduate Students
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      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Statistics Education
        Type: general
      – SubjectFull: Student Motivation
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      – SubjectFull: Motivated Strategies for Learning Questionnaire
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      – TitleFull: Value-Reappraisal and Goal-Setting Intervention Effects on Attitudes and Performance in College Statistics
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