Abstract
Adaptive courseware products implementing mastery learning pedagogy must determine when each student reaches mastery. Such determinations are often made in real time, in order to inform student progress, but the validity of algorithmically determined mastery typically can only be assessed by examination of later student performance. This paper examines the impact of platform-determined mastery on future quiz and assignment preparedness in the context of Knewton alta. With simple controls for overall student initial ability, platform-wide results indicate that students achieving mastery (as calculated by Knewton’s Proficiency Model) outperform students who do not, with largest future performance gains seen by students with lowest initial ability levels.
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Notes
- 1.
Properly setting mastery thresholds through examination performance has been a topic of considerable research [6]. Real-time mastery thresholds present a more significant validation challenge.
- 2.
When students are compared only to class intra-assignment or intra-quiz peers, the outcome distributions over the resulting (much smaller) data set match the general trends shown here. The results below provide a less-controlled but wider-ranging composite picture of student performance across a variety of classroom implementations.
References
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Jones, A., Bomash, I. (2018). Validating Mastery Learning: Assessing the Impact of Adaptive Learning Objective Mastery in Knewton Alta. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_81
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DOI: https://doi.org/10.1007/978-3-319-93846-2_81
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