Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

Abstract

This article proposes a context-aware course-planning model for pervasive learning environments. The model considers learners’ preferences, including their learning styles, locations, activities, and devices. The model generates four-dimensional contexts based on these preferences, each dimension being a weighted vector of visual, aural, read/write, and kinesthetic (VARK) features. Using a content-based similarity algorithm, the model adapts the course content to each learner’s context. The courses are created in a sequence of rings, each containing all possible learning materials (LMs) that have the same content in different formats. Each LM is represented by a vector with weights like those of the learners’ context dimensions. The model generates all possible plans based on the predefined contexts of the learner, detects the learner’s actual context, and adapts the content accordingly. The goal of the model is to enable learners to learn what they want, in the way they prefer, and to complete courses efficiently, at any time, place, and during any activity, in the appropriate format.