Broaden co-regulation methodology using trace data, server logs, episodic coding and multilevel modelling¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The review recommends methodological expansion for studying co-regulation as a group phenomenon. It suggests "wider use of log server and trace data to illustrate how students are working in the computer-based environment as it relates to other activities", coding co-regulation across connected discourse and episodic tracts rather than single utterances, and multilevel modelling to explain relationships between individuals nested under groups. Multiple measures are needed for convergence and construct validity.

Design Implications¶

Context¶

Requirements¶

• Multiple measures for convergence and construct validity

Constraints¶

• Chat logs and transcripts may make high-level co-regulated learning difficult because there is limited time for reflection during such discussions

Target Learners¶

• students in computer-based collaborative learning

Target Learning Goals¶

• co-regulatory processes in situated group contexts

Claims¶

• Trace data can capture the temporal progress of regulation as learners engage in learning tasks over time [+W]

Related Strategies¶

• Use multimodal data channels combining subjective and objective measures to overcome single-channel constraints in SSRL research
• Adopt an integrative methodological toolkit using machine learning and computational models to capture SR's multi-level, temporal, and social dynamics

Examples¶

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Key Sources¶

• Carol K. K. Chan. (2012). Co-regulation of learning in computer-supported collaborative learning environments: a discussion. Metacognition and Learning, 7, 63–73. https://doi.org/10.1007/s11409-012-9086-z