Massive Open Online Course (MOOC) platform designs, such as those of edX and Coursera, aord linear learning sequences by building scaolded knowledge from activity to activity and from week to week. We consider those sequences to be the courses' designed learning paths. But do learners actually adhere to these designed paths, or do they forge their own ways through the MOOCs? What are the implications of either following or not following the designed paths? Existing research has greatly emphasized, and succeeded in, automatically predicting MOOC learner success and learner dropout based on behavior patterns derived from MOOC learners' data traces. However, those predictions do not directly translate into practicable information for course designers & instructors aiming to improve engagement and retention | the two major issues plaguing today's MOOCs. In this work, we present a three-pronged approach to exploring MOOC data for novel learning path insights, thus enabling course instructors & designers to adapt a course's design based on empirical evidence.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Educational Data Mining, EDM 2016
EditorsTiffany Barnes, Min Chi, Mingyu Feng
PublisherInternational Educational Data Mining Society (IEDMS)
Pages54-61
Number of pages8
StatePublished - 1 Jun 2016
Event9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, NC, United States

Conference

Conference9th International Conference on Educational Data Mining, EDM 2016
CountryUnited States
CityRaleigh, NC
Period29/06/162/07/16

    Research areas

  • MOOCs, learning path analysis, visualization

ID: 11401769