Identifying Learning Progression Profiles in a Psychomotor Task Using Sequence Clustering

Résumé fourni par la source

Psychomotor skill learning remains underexplored in educational data mining, which has historically focused on cognitive tasks. Understanding how learners progress toward mastery over time, rather than simply measuring final outcomes, is essential for designing adaptive instructional systems. In this work, we analyze trial-by-trial learning progressions from a simulated drone landing task. Participants completed 20 trials with performance categorized as crash, unsafe landing, or safe landing. We compute transition probabilities between performance categories and apply a k-medians clustering algorithm using Levenshtein distance to identify representative learning sequences. Transition results show that learners rarely make large performance jumps between consecutive trials and tend to maintain safe landings once achieved, consistent with our knowledge of motor skill acquisition. Clustering reveals meaningful learner profiles ranging from quick mastery to persistent struggle, with finer-grained distinctions emerging as the number of clusters increases. These findings provide an initial foundation for developing feedback and task selection policies in intelligent tutoring systems that are sensitive to a learner's trajectory through skill acquisition, not only their current performance state.

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