Worked Examples of Interaction Dominant Studies on Motivational and SRL

• In Event: Fostering Complexity in the Conceptualization and Study of Motivation, Engagement, and Self-Regulated Learning: A Workshop

Abstract

The workshop participants will examine worked examples that demonstrate insights resulting from shifting conceptualizations of motivation, engagement, and SRL from component-dominant to interaction-dominant representations. The first two worked examples present the strategy of modeling within-person variability to unmask the complexity that is hidden by group-level, nomothetic, component-driven methodologies.

We will begin by reviewing a study by Saqr and López‐Pernas (2024), who investigated the dynamics of SRL among upper-secondary school students in Eastern Finland by utilizing Gaussian Graphical Models to analyze survey responses that were administered twice-daily (at school and at home) to 18 students over 34 days (resulting with 689 responses). The researchers conducted between-person, average within-person, and idiographic analyses, found distinct individual differences in SRL as demonstrated in Figure 2, and derived dynamic and complex theoretical principles for SRL.

(see Figures in uploaded document)

We will follow by examining work by Wolff et al. (in press) who applied Random Intercepts Cross Lagged Panel Models (RICLPM) to investigate the dynamic, longitudinal, and reciprocal relations of self-efficacy, perceived academic burden, and academic performance among 442 undergraduate biology students in a western US university who were surveyed five times, one month apart. The researchers conducted several cross lagged panel models, including a standard group-focused model and a model in which grade was regressed on the random-intercepts as well as on within-person fluctuations. The findings indicated that separating individual differences from within-person effects was not only important for capturing the motivational relations with achievement in the data, but that once within-person effects were included, between-person effects became not-significant. Figure 3 presents the final model that fitted the data best.

Researchers with a complexity-minded approach increasingly employ these models due to their capacity to distinguish between within-person (idiographic) and between-person (group) findings. This allows for an effective examination of whether insights derived from group-level data can be adequately applied to individuals within a sample. This approach is part of a broader trend towards statistical techniques capable of capturing complexity, underscoring the critical need to explore idiographic, within-person dynamics for theory development using these types of analytic techniques (Beymer et al., 2022; Kryshko et al., 2024; Marsh et al., 2023).

The third worked example, Garner and Kaplan (2019), demonstrates mapping a complexity-model of identity and motivation—The Dynamic Systems Model of Role Identity (DSMRI; Kaplan & Garner, 2017)—onto a qualitative protocol and codebook in a study that yields theoretical principles from person-focused data. This study demonstrates how qualitative techniques can be used towards similar aims as quantitative idiographic methods, including how these methods can be integrated in mixed methods approaches that are designed to capture the phenomenon’s complexity (McCrudden & Marchand, 2020). Figure 4 presents a schematic of the DSMRI.

Authors

• Jonathan C. Hilpert, University of Nevada - Las Vegas

• Joanna K. Garner, Old Dominion University

• Gwen C. Marchand, University of Nevada - Las Vegas

• Avi Kaplan, Temple University