Behavioral Analytics of a Freshmen Small Private Online English Course
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SPOC (Small Private Online Course) is an online learning platform combining classroom and online lessons. For the development of the SPOC, it is worth looking at learning behaviors in-depth throughout the course. As Higher Education courses in Thailand focus on English language benchmarking, this research decided to choose English Level 1 as a content course for the reading and grammar skills from August 2020 to June 2021. The descriptive analytics study conducted by Visual Analytics and K-Means Clustering, both student-level and lesson-level learning behavior. The result found four types of learners in Student-level learning behavior: 1) Intelligent, 2) Weak cognitive, 3) Inattentive 4) Unenthusiastic. Moreover, Predictive Analytics, for predicting learning quality through learning behaviors of each cluster by four machine learning models, were comparatively experimented with: Generalized Linear Model, Decision Tree, Random Forest, and Gradient Boosted Trees. The Optimization method is used for tuning the optimum parameter of each method. For student-level behavior prediction, the Unenthusiastic Decision Tree was 0.0449, and Lesson-level Weak-cognitive Gradient Boosted Trees was 0.0371 relative error. Additionally, the factor of importance in quality prediction was found that amount of quizzes was the essential variable among all clusters. The result of this research is that the instructors can further develop content and teaching methods in the course to truly meet the learners' needs.
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