Individualization for Education at Scale: MIIC Design and Preliminary Evaluation

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Online and distance learning programs, including those for higher education and corporate training, are notorious for their issues in maintaining learner engagement. Adaptive learning is a potential solution to this issue, because of its ability to differentiate learning automatically based on a user model that can capture diverse learning styles and backgrounds. This work presents the design and preliminary evaluation of the Mobile Integrated and Individualized Course (MIIC), an Adaptive Educational System (AES) which integrates video, text, assessments, and social learning into a native mobile app for delivery to end users. The inputs collected about users as they interact with each learning mode can be used to update the user model, which is in turn used to drive the adaptation engine. Through two initial trials, it was found, for example, that the mean level of engagement – when quantified as the number of pages viewed – was statistically higher among distance learning students using MIIC than among those using a one-size-fits-all (OSFA) presentation of the same material.

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