Reconsider CSCL research designs and methodologies for mass collaboration and learning analytics¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 2 studies (2 review), q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

The editorial directs the CSCL community to respond to emerging social configurations. As printed: "These new social configurations and environments challenge mainstream assumptions about collaborative learning and ask us to reconsider the types of research design and methodologies that will become most productive and influential in the coming years." The strategy calls for examining how phenomena like mass collaboration and learning analytics will change the field, alongside maintaining a mix of experimental, quasi-experimental, naturalistic studies and randomized controlled trials with a wide range of analytic techniques.

Design Implications¶

Context¶

Requirements¶

• Research programs must span experimental, quasi-experimental, naturalistic studies and randomized controlled trials with a wide range of analytic techniques

Constraints¶

• The editorial frames this as an open question for the coming years, not a settled methodological prescription

Target Learners¶

• CSCL researchers and designers studying collaborative learners

Target Learning Goals¶

• advancing research methodology for computer-supported collaborative learning

Claims¶

• Three big data methods—text classification, cluster analysis, and social network analysis—can each yield insights into learning in mass collaboration [+W]
• CSCL needs to promote the inter-animation of complementary quantitative and qualitative perspectives rather than converge on a single ideal method. [+W]

Related Strategies¶

• Editorial vision: embrace diverse technologies and methodologies, rigor appropriate to each paper's methods, and CSCL in new contexts

Examples¶

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Key Sources¶

• Sten Ludvigsen. (2016). CSCL towards the future: The second decade of ijCSCL. International Journal of Computer-Supported Collaborative Learning. https://doi.org/10.1007/s11412-016-9230-x