A "keywords cloud" learning interest/difficult reminding system based on learners' video watching logs and subtitles is proposed for promoting self-paced MOOC learning. By identifying the hot video segments (via video seek event counts) and weighting the keywords of hot video segments, we are able to establish the "keywords cloud" of each learning topic. This feature is valuable for learners to quick identify the most important or difficult concepts of each topic. This is also useful for the teacher to more understand which parts of the contents of each topic are most difficult for the learners which can be further improved.
['Hsuan-Ming Chang', 'Tonny Meng-Lun Kuo', 'So‐Chen Chen', 'Chia-An Li', 'Yi-Wei Huang', 'Yu-Cheng Cheng', 'Hao-Hsuan Hsu', 'Nen-Fu Huang', 'Jian-Wei Tzeng']
Developing a Data-Driven Learning Interest Recommendation System to Promoting Self-Paced Learning on MOOCs