Abstract
Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the use of automated sentiment analysis in assessing student experience in a beginner computer programming MOOC. A dataset of more than 25,000 online posts made by participants during the course was analysed and compared to student feedback. The results were further analysed by grouping participants according to their prior knowledge of the subject: beginner, experienced, and unknown. In this study, the average sentiment expressed through online posts reflected the feedback statements. Beginners, the target group for the MOOC, were more positive about the course than experienced participants, largely due to the extra assistance they received. Many experienced participants had expected to learn about topics that were beyond the scope of the MOOC. The results suggest that MOOC designers should consider using sentiment analysis to evaluate student feedback and inform MOOC design.
Highlights
Since 2011, technological development has enabled the growth of online learning with free courses known as Massive Open Online Courses (MOOCs) attracting thousands of learners
Of the 3,531 participants who made at least one post in sessions five to seven, 264 (7.5%) individuals wrote something in week four’s feedback step
Using sentiment analysis on text data from a MOOC has helped the teaching team make evidence-based observations and conclusions that otherwise might have been overshadowed by anecdotal evidence from teaching experiences
Summary
Since 2011, technological development has enabled the growth of online learning with free courses known as Massive Open Online Courses (MOOCs) attracting thousands of learners. The success of MOOCs depends on the active involvement of large numbers of learners who, through dynamic engagement, self-organise into learning communities where they share skills, objectives, knowledge, and interests, by commenting within the learning system and using other social networking tools (McAuley, Stewart, Siemens, & Cormier, 2010). A challenge when running a MOOC is gaining an accurate understanding of learner experience because the number of participants makes it impossible to follow all posts and interactions. Participant comments and actions can provide an impression of the sentiments and concerns of learners within a course. Analysis of individual learner experience is an important aspect evaluation but difficult to undertake when there are thousands of participants. Without analytical tools to understand overall sentiments and how they may vary across different groups of learners, it is easy to disproportionately focus on negative posts
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