ENA-based three-dimensional model of self-regulated learning linking learning strategies, time management modes, and course topics¶

Theory · All theories
Evidence · 8 claims (8 for) · 1 study (1 associational), q2 · 1 of 1 report an effect size · 8 claims rest on one study

Description¶

A network analytic model in which epistemic network analysis (ENA) represents each student's self-regulated learning as a network whose nodes are codes from three dimensions: learning strategies, time management modes, and course weekly topics. As the article states, "The nodes in the network depict the codes from three dimensions: (1) learning strategies (four different strategies, Table 3), (2) time management modes (four different modes, Table 4), and (3) course weekly topics (10 topics, Table 2)." Edges are created only between nodes from different dimensions based on co-occurrence, and group networks are compared via subtracted networks and mean-rotated t-tests. The article uses this model to examine how strategy use and timing jointly relate to exam performance across the semester.

Design Implications¶

Context¶

Requirements¶

• Trace data recording students' interactions with online learning resources, coded into learning actions, topics, and timestamps
• Specification of the ENA unit of analysis, conversation, and window size

Constraints¶

• Edges can only be created between nodes from different dimensions, not within a dimension
• The article notes SRL is a highly contextual set of learning processes, so the model was examined relative to weekly topics in one course

Target Learners¶

• first-year undergraduate engineering students in flipped classroom courses

Target Learning Objectives¶

• productive self-regulated learning, including learning strategy selection and time management during online preparatory tasks

Claims¶

• Final Exam Subtracted Network Group Differences [+M]
• Weekly Ena Ttests Significant Weeks [+M]
• In examination weeks, students revisited prior topics and used exercises, whose deadlines had passed, as formative practice for the exams [+W]
• High- and low-performing students differed significantly on the final-exam ENA network X dimension but not the Y dimension [+W]
• Students in the flipped classroom most often used the preparing and revisiting time management modes together with the summative assessment learning strategy [+W]
• Low-performing students tended to procrastinate more on graded problem-solving exercises and on video watching with formative MCQs [+W]
• High- and low-performing students did not differ significantly on the midterm-exam ENA network dimensions despite detected network differences [+W]
• Low performers completed activities on time for fewer topics and showed less catching-up only in early topics, while high performers showed no catching-up on later topics [+W]

Related Theories¶

Examples¶

• Use findings on strategy–time-management links to develop early warning systems and interventions promoting effective time management and strategy use in flipped classrooms

Key Sources¶

• Raković, M., Ahmad Uzir, N., Matcha, W., Eagan, B., Jovanović, J., Shaffer, D. W., Pardo, A., & Gašević, D. (2023). Network Analytics to Unveil Links of Learning Strategies, Time Management, and Academic Performance in a Flipped Classroom. Journal of Learning Analytics, 10(3), 64–86. https://doi.org/10.18608/jla.2023.7843