The emergence of Massive Open Online Courses (MOOC) posted great impact to education. Students could enroll and attend any MOOC anytime and anywhere, depending on their interest, schedule and learning pace. However, the dropout rate of MOOC was known to be high in practice. Earlier identification of students who have high chance to dropout in MOOC is useful for immediate intervention and reduction in dropout rate. In this paper, we aim at applying data mining methods to discover the students who are likely to dropout in MOOC. Real-world data were collected for the evaluation of our proposed method.
|Publication status||Published - May 2015|
|Event||The 19th Global Chinese Conference on Computers in Education (GCCCE 2015): "21st Century Core Competencies" - National Central University, Taipei, Taiwan, Province of China|
Duration: 25 May 2015 → 29 May 2015
|Conference||The 19th Global Chinese Conference on Computers in Education (GCCCE 2015): "21st Century Core Competencies"|
|Abbreviated title||GCCCE 2015|
|Country/Territory||Taiwan, Province of China|
|Period||25/05/15 → 29/05/15|
CitationWong, T.-L., Kong, S. C., Wang, F. L., & Kwan, R. (2015, May). Predicting potential dropout students in MOOC via data mining. Paper presented at The 19th Global Chinese Conference on Computers in Education (GCCCE 2015), Howard Civil Service International House, Taiwan.
- Data mining
- Dropout rate
- Decision tree