Advance CSCL by building Bayesian and collaborative-specific theoretical challenges: knowledge building priors, technology formalisation, and collaborative diagnostic reasoning¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The authors propose concrete research directions: treat learners' exposure to multiple resource-peers as requiring conceptualising information integration and coherence building; add "epistemic norms such as coherence and newness as part of the priors" in Bayesian models of knowledge building; formalise the role of technology in social collaboration; and progress from Marr's computational analysis to algorithmic and implementation levels using Brandl et al.'s collaborative diagnostic reasoning data.

Design Implications¶

Context¶

Requirements¶

• Grounding in the computational analysis level (what is computed and why) before algorithmic and implementation modelling

Constraints¶

Target Learners¶

• CSCL researchers modelling collaborative learning

Target Learning Goals¶

• collaborative knowledge construction and diagnostic reasoning

Related Strategies¶

• Sequence CSCL research on human-AI knowledge construction from exploratory observation to experimental variable manipulation

Examples¶

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Key Sources¶

• Baker, M., Reimann, P. (2024). CSCL: a learning and collaboration science?. International Journal of Computer-Supported Collaborative Learning. https://doi.org/10.1007/s11412-024-09433-4