Use the WD statistic during gameplay as a quick screening tool for pairing learners with likely different profiles¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Based on simulation evidence that distinct learner trajectories yield reasonably distinct WD value ranges, the authors recommend computing the WD statistic during gameplay as "a quick screening tool to suggest pairings of learners who have likely different profiles for subsequent activities of the game", for example by pairing learners with lower and higher WD values. They caution that subtler developmental nuances escape the statistic.

Design Implications¶

Context¶

Requirements¶

• WD values must be computed from automatically coded chat utterances during gameplay
• The screening use presumes that learners with distinct learning trajectories have ranges of WD values that are reasonably distinct

Constraints¶

• More subtle nuances in developmental trends will of course not be captured by the statistic

Target Learners¶

• individual learners playing epistemic games such as Land Science
• learners collaborating in stakeholder-group activities

Target Learning Goals¶

• detecting differences in learners' developing profiles on SKIVE competencies
• forming learner pairings for subsequent game activities

Related Strategies¶

Examples¶

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Key Sources¶

• Sweet, S. J., & Rupp, A. A. (2012). Using the ECD Framework to Support Evidentiary Reasoning in the Context of a Simulation Study for Detecting Learner Differences in Epistemic Games. Journal of Educational Data Mining, Special Issue, Article 5, Volume 4. https://jedm.educationaldatamining.org/index.php/JEDM. doi:10.5281/zenodo.3554649