Foster and, if necessary, enforce socio-cognitive conflicts that lead to productive solutions as a strategy to optimize learning outcomes¶

Strategy · All strategies
Evidence · 4 claims (3 for, 1 against) · 3 studies (2 review, 1 qualitative), q2–q3 · 0 of 3 report an effect size · 4 claims rest on one study

Description¶

Arguing that socio-cognitive conflicts and productive friction are indispensable for learning and knowledge construction, the article recommends that fostering, and if necessary enforcing, such conflicts "that lead to a productive solution can be a fruitful strategy to optimize learning outcomes." It links this to productive failure in mathematics teaching (Kapur 2008), where learners first try out different, often false, solutions collaboratively before the correct solution is discovered. The article notes future research must show whether similar approaches work at large scale.

Design Implications¶

Context¶

Requirements¶

• Conflicts must be guided toward productive resolution rather than being ignored or escalating

Constraints¶

• The article states future research will have to show how far productive-failure-like approaches are useful for large-scale learning and knowledge construction environments, beyond the investigated mathematics-teaching context

Target Learners¶

• learners in CSCL environments, mathematics classrooms, and mass collaboration platforms

Target Learning Goals¶

• learning and knowledge construction through resolving socio-cognitive conflicts and experiencing productive failure

Affordances¶

• Productive Friction Concept
• Co Evolution Individual Collective Knowledge Model

Claims¶

• Articles edited by more heterogeneous contributors are more balanced, indicating productive friction that counters polarization [+W]
• Wikipedia contributors show confirmation bias by preferentially editing articles consistent with their own views [-W]
• PS-I fosters learning only if specific design features (contrasting cases or building instruction on student solutions) are implemented [+W]
• Computer-supported collaborative learning with simulation programs fostered conceptual change for students who were cognitively engaged and prepared to reflect on and reconstruct their conceptions [+W]

Related Strategies¶

• Prevent closely knit homogeneous-attitude communities to reduce bias by increasing productive friction

Examples¶

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Key Sources¶

• Peter Holtz & Joachim Kimmerle & Ulrike Cress. (2018). Using big data techniques for measuring productive friction in mass collaboration online environments. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-018-9285-y