Clustering n-gram flip sequences per task yields three distinct problem-solving strategy groups across the binary challenges¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2017 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? EM clustering of n-gram features of tile-flip sequences produced three coherent strategy clusters: quick solvers with few trial-and-error attempts, moderate solvers with thoughtful trial-and-error, and slow solvers with many flips and seemingly random trial-and-error. → Akram 2017

Evidence¶

Akram 2017¶

Akram, B., Mott, B., Min, W., Boyer, K. E., Wiebe, E., & Lester, J. (2017). Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics. https://scholar.google.com/scholar?q=Improving+Stealth+Assessment+in+Game-based+Learning+with+LSTM-based+Analytics

q2 · i? · design · r2

Unsupervised EM clustering (MClust in R) of n-gram features from flip sequences, performed separately for each of the 11 tasks on the training set. Decimal and binary error-decay plots (Figures 3 and 4) showed distinct decay patterns per cluster; three clusters were chosen for all tasks.

"The clustering grouped students into those who solved the problem in a few flips and a few attempts, those who solved the problem with a moderate number of flips and with thoughtful trial-and-error, and those who solved the problem with a long sequence of flips and with seemingly random trial-and-error."

Discussion¶

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