Understanding the Roles of Personalization and Social Learning in a Language MOOC Through Learning Analytics (74772)

Session Information: Online and Mobile Learning
Session Chair: Napat Jitpaisarnwattana

Saturday, 11 November 2023 11:00
Session: Session 1
Room: Sri Sachanalai
Presentation Type: Paper Presentation

All presentation times are UTC + 7 (Asia/Bangkok)

In the last decade, there has been a great deal of interest in language MOOCs (LMOOCs) and their potential to offer learning opportunities for large audiences, including those in disadvantaged communities. However, experiences and research have shown MOOCs to suffer from several challenges. Chief among these have been low participation and completion rates, which are often attributed to limitations in how opportunities for personalisation and social interaction are implemented. For the current study, a dedicated LMOOC was designed and implemented, called the “Social and Personal Online Language Course (SPOLC).” This language learning environment incorporates a recommendation system and emphasizes personalisation and social interaction. The study identified the types of learning behaviour that were related to course completion and observed how 270 learners in the LMOOC used the various course features. The data were collected using learning analytical methods and analysed using binary logistic regression and feature extraction prediction model. The results demonstrated that working in groups and creating a learning plan were important factors associated with course completion, while interacting with other learners online was not. We conclude with several suggestions and implications for future LMOOC design, implementation, and research.


Abstract Summary
In the last decade, there has been a great deal of interest in language MOOCs (LMOOCs) and their potential to offer learning opportunities for large audiences. For the current study, a dedicated LMOOC was designed and implemented, called the “Social and Personal Online Language Course (SPOLC).” This language learning environment incorporates a recommendation system and emphasizes personalisation and social interaction. The study identified the types of learning behaviour that were related to course completion and observed how 270 learners in the LMOOC used the various course features. The data were collected using learning analytical methods and analysed using binary logistic regression and feature extraction prediction model. The results demonstrated that working in groups and creating a learning plan were important factors associated with course completion, while interacting with other learners online was not. I conclude with several suggestions and implications for future LMOOC design, implementation, and reserch

Authors:
Napat Jitpaisarnwattana, University of Cambridge and Silpakorn University, Thailand


About the Presenter(s)
Dr Napat Jitpaisarnwattana is a University Assistant Professor/Lecturer at University of Cambridge in Thailand

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