Effects of Multimedia Self-Regulated Learning Training on Course Engagement Across Motivational Profiles

• In Event: Designing Effective Digital Programs to Help Students Develop and Deploy Self-Regulated Learning Skills

Abstract

Objectives/Purpose. Struggles with retention in postsecondary STEM have led researchers to employ a variety of approaches to support students’ self-regulated learning (SRL; Greene, 2018; Greene, et al. 2019; Theobald, 2021). However, students’ personal motivations and goals for a course may impact the efficacy of such interventions (Robinson et al., 2022). In this study, we examine the consistency of the relationship between motivational profiles and observed behaviors, and the ways those behaviors and academic performance differ as a result of SRL training.

Methods and Data Sources. 349 consenting students at a large United States university were enrolled in an online introductory, high-structure biology course. Participants completed a motivation survey (i.e., achievement goals, Elliot & Murayama, 2008; self-efficacy, Midgley et al., 2000; values, Perez et al., 2014; cost, Eccles & Wigfield, 2020) after three course meetings. Participants were randomly assigned to complete a series of multimedia modules on either biology content (control) or the science of learning to learn (SoL2L; treatment). Participants’ interactions across multiple technology-enhanced learning environments were collected.

Results.
Motivation. Confirmatory factor analyses (CFA) and exploratory structural equation modeling (ESEM), with and without bifactors (see Figure 1; Part et al., 2020) showed that achievement goal factors were best modeled using an ESEM model, self-efficacy with a CFA model, and costs and values with a bifactor-ESEM model. Latent profile analysis was conducted on the resulting factor scores, generating a four-profile solution (see Tables 1 & 2, Figure 2).
Moderation Analysis. Analyses involving the full sample showed students in treatment and control conditions did not statistically significantly differ from one another. Next, we split the sample into the four motivation profiles and conducted ANOVAs within each subsample, but no treatment effect on performance or behavior was detected (see Table 3).
Cross profile comparisons revealed statistically significant performance differences (i.e., final exam and final grade) between adaptively motivated (Class 4) and maladaptively motivated (Class 1) students (see Table 4). Class 4 was more likely to be first-year students and biology majors, but behavioral differences were minimal. Compared to moderately motivated (Class 3) students, Class 4 students submitted fewer incorrect homework items and were more likely to engage with exam preparation (i.e., previous semester exams, study guides), lecture materials (i.e., slides, videos), and metacognitive exam reflection assignments, often multiple lessons ahead of schedule, yet interestingly no performance differences were found. Goal striving (Class 2) students were less likely to be first-year students than adaptively motivated (Class 4) students, but otherwise no performance or minimal behavioral differences were found.

Significance. In environments where exams are online and access to outside materials is not restricted, exam preparation behaviors may be less predictive of performance than in other exam environments. Despite being more experienced college students and having accessed all the same materials, maladaptively motivated students performed significantly worse than their adaptively motivated counterparts. These findings show that motivational differences are not always clearly exhibited via course engagement behaviors. Further analysis of the emergence of these behavioral differences across exam periods will also be presented.

Authors

• Robert D Plumley, University of North Carolina - Chapel Hill
  Presenting Author

• Jeff A. Greene, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Matthew L. Bernacki, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Shelbi Laura Kuhlmann, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Michael Berro, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Mara Evans, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Kelly Hogan, University of North Carolina - Chapel Hill
  Non-Presenting Author