130,911 research outputs found
Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods
Massive Open Online Courses (MOOCs) offer unprecedented opportunities to
learn at scale. Within a few years, the phenomenon of crowd-based learning has
gained enormous popularity with millions of learners across the globe
participating in courses ranging from Popular Music to Astrophysics. They have
captured the imaginations of many, attracting significant media attention -
with The New York Times naming 2012 "The Year of the MOOC." For those engaged
in learning analytics and educational data mining, MOOCs have provided an
exciting opportunity to develop innovative methodologies that harness big data
in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning
Analytics: Applications in Educational Research
Learning sentiment from students’ feedback for real-time interventions in classrooms
Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded
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