Exploring Navigation Styles in a FutureLearn MOOC
This provides insights into online learners' engagement and tools for identifying vulnerable learners in MOOCs, potentially aiding personalized interventions in Intelligent Tutoring Systems, though it is incremental as it builds on existing navigation style concepts.
The paper tackled the problem of identifying fine-grained navigation styles in MOOCs, analyzing data from many active learners to reveal that sequential styles are common but global styles are less so, most learners don't fit these categories, and navigation styles are unstable with detrimental effects from swapping.
This paper presents for the first time a detailed analysis of fine-grained navigation style identification in MOOCs backed by a large number of active learners. The result shows 1) whilst the sequential style is clearly in evidence, the global style is less prominent; 2) the majority of the learners do not belong to either category; 3) navigation styles are not as stable as believed in the literature; and 4) learners can, and do, swap between navigation styles with detrimental effects. The approach is promising, as it provides insight into online learners' temporal engagement, as well as a tool to identify vulnerable learners, which potentially benefit personalised interventions (from teachers or automatic help) in Intelligent Tutoring Systems (ITS).