Abstract
MOOC user behavior is generally studied using the data collected within platform interactions in the learning system or via outside social media platforms. It is important to understand the root causes of anomalies in MOOCs, such as the 80% attrition, less interactions within platforms and what causing the reflected behaviors beyond platforms. We study MOOC student behaviors outside the platform using ethnographic methods, mainly focusing on diary study and interviews. Two groups, 11 extreme users who have completed many MOOCs and 10 who never completed MOOC have been used to collect data. The log sheets data and interviews were analyzed using the Epistemic Network Analysis (ENA) method to explore if there is a significance between these 2 groups and other qualitative comparisons to explore behavioral patterns. Our results indicated 4 behavioral patterns with insights into a significant level of learner's habits between extreme and novice users’ behaviors leading to completion or dropping. This reflects the design gaps of MOOC platforms and based on the behavioral patterns, we provide recommendations to meet the learners' needs.
Highlights
Massive Open Online Courses (MOOCs) are a phenomenal education technology consist of short videos, peer graded or self-graded assignments and forum to interact
Epistemic Network Analysis (ENA) networks were visualized using network graphs where nodes correspond to the codes, and edges reflect the relative frequency of co-occurrence, or connection, between two codes
Our model had co-registration correlations of 0.96 (Pearson) and 0.96 (Spearman) for the first dimension and co-registration correlations of 0.99 (Pearson) and 0.99 (Spearman) for the second. These measures indicate that there is a strong goodness of fit between the visualization and the original model which intended to see the difference of MOOC behaviors
Summary
Massive Open Online Courses (MOOCs) are a phenomenal education technology consist of short videos, peer graded or self-graded assignments and forum to interact. Researchers argue number of completions may not be ideal metrics for measuring success but other factors such as percentages from those who watch all the content, take quizzes/assignment and engage in the MOOC [18] Such MOOC behaviors being measured in many platforms to understand and improve learner experience and retention. Apart from the data generated within the MOOC platform itself, few data-driven research works go beyond Such as exploring the learners’ traces on the wider Web, in particular the Social Web, to gain understanding of learner behavior in a distributed learning ecosystem. Data within MOOCs and social platforms may provide certain behavior without knowing causes triggering to the interactions or the reason causing the behaviors Such as, some users may never log into continue assignments and platform logs only provide evidence that the did not log, but would not support “why”. We interested to understand if there is a behavioral difference between those who conveniently complete MOOCs and those who struggle to finish
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