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Analyzing the Engagement of CAPT Program Users With Data Mining Methods: High Scorers Are Not Always the Best Learners
Abstract
Educators gauge learning with CALL applications via achievement metrics such as point scores or level advancement. Overreliance on such metrics may limit validity of learner engagement measures. This chapter used learning analytic approaches (e.g., cluster and regression analyses) to investigate user engagement with a web-based CAPT program. Cluster analysis identified four types of users with effort-based attributes best distinguishing among types. Regression analysis found that lines recorded has the strongest association with point scores. Follow-up retrospective time-series analysis of cluster members showed distinct trends in learning behavior that indicate possible goal orientations per group. These results imply that one must deconstruct and identify the aspects of engagement that are actually being measured by application metrics. Additionally, significant differences in engagement patterns exist within high and low scoring groups that are opaque to analysis across the whole sample. Finally, activity logs provide data that suggest variability in motivation types.
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