Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation.

ABSTRACT: The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners' behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that devi

SUBMITTER: Peach RL 

PROVIDER: S-EPMC7854683 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature