Easy, highly specific simulated tasks compress the range of the WD statistic across learner types, while well-designed tasks widen it¶

Claim · All claims
Evidence · 1 study (2 entries) · design r2 · q2
Currency · newest study 2012 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In design matrix 1 games, the smallest distributional range of WD values across learner types is observed when tasks are easy as well as highly specific (condition A2). → Sweet 2012 q2 i? In the same games, the widest range of WD values is observed for well-designed tasks (conditions A1, B1, and C1). → Sweet 2012 (2)

Evidence¶

Sweet 2012¶

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. doi:10.5281/zenodo.3554649

q2 · i? · design · r2

Distributional analysis of WD values across learner types for design matrix 1 games, broken down by task difficulty (1-7) and specificity (A, B, C), shows "the smallest distributional range is observed when the tasks are "easy" as well as "highly specific" (condition A2)".

"The smallest distributional range is observed when the tasks are "easy" as well as "highly specific" (condition A2)."

Sweet 2012 (2)¶

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. doi:10.5281/zenodo.3554649

q2 · i? · design · r2

The same design matrix 1 distributional analysis shows "the widest range of WD values is observed for "well-designed" tasks (conditions A1, B1, and C1)", with remaining conditions falling between these extremes.

"In contrast, the widest range of WD values is observed for "well-designed" tasks (conditions A1, B1, and C1); remaining conditions represent middle grounds between these."

Discussion¶

Related Claims¶

• Defining and quantifying reliability and measurement error is particularly challenging in epistemic games for three principal reasons — the general claim this finding bears on, with the studies behind it
• In simulated epistemic games, the weighted density statistic distinguishes simulated learner types with distinct mastery trajectories, with the expert trajectory showing the largest WD values — related
• The WD statistic is more useful for differentiating between simulated learner types when games are played longer — related
• In pairwise WD analyses, learner trajectory similarity dominates variation in percentage-overlap values (57.90% of variation) while remaining design factors are essentially zero — related
• In the simulation, task complexity and task difficulty explain the majority of variance in the individual-learner WD statistic, with some effect of task specificity — related
• Segmentation boundary choices differentially affect statistics computed on epistemic-game process data — related