Gauging MOOC Learners' Adherence to the Designed Learning Path

Massive Open Online Course (MOOC) platform designs, such as those of edX and Coursera, afford linear learning sequences by building scaffolded 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.

[1]  Michel C. Desmarais,et al.  A review of recent advances in learner and skill modeling in intelligent learning environments , 2012, User Modeling and User-Adapted Interaction.

[2]  Gautam Biswas,et al.  Mining Student Behavior Models in Learning-by-Teaching Environments , 2008, EDM.

[3]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[4]  Ryan Shaun Joazeiro de Baker,et al.  Adapting to When Students Game an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.

[5]  Niels Pinkwart,et al.  Predicting MOOC Dropout over Weeks Using Machine Learning Methods , 2014, EMNLP 2014.

[6]  Jane Sinclair,et al.  Dropout rates of massive open online courses : behavioural patterns , 2014 .

[7]  Alex Paramythis,et al.  Towards Adaptive Learning Support on the Basis of Behavioural Patterns in Learning Activity Sequences , 2010, 2010 International Conference on Intelligent Networking and Collaborative Systems.

[8]  Alex Paramythis,et al.  Activity sequence modelling and dynamic clustering for personalized e-learning , 2011, User Modeling and User-Adapted Interaction.

[9]  Sally Fincher,et al.  Making sense of card sorting data , 2005, Expert Syst. J. Knowl. Eng..

[10]  Beverly Park Woolf,et al.  Identifying High-Level Student Behavior Using Sequence-based Motif Discovery , 2010, EDM.

[11]  Chris Piech,et al.  Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.

[12]  Carolyn Penstein Rosé,et al.  Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses , 2014, CIKM.

[13]  Carolyn Penstein Rosé,et al.  “ Turn on , Tune in , Drop out ” : Anticipating student dropouts in Massive Open Online Courses , 2013 .

[14]  Wil M. P. van der Aalst,et al.  Conformance checking of processes based on monitoring real behavior , 2008, Inf. Syst..

[15]  Katharina Reinecke,et al.  Demographic differences in how students navigate through MOOCs , 2014, L@S.

[16]  Patrick Jermann,et al.  Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions , 2014, Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs.

[17]  Sherif A. Halawa,et al.  Dropout Prediction in MOOCs using Learner Activity Features , 2014 .

[18]  Carl Gutwin,et al.  The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments , 2014 .

[19]  Rebecca Ferguson,et al.  Consistent Commitment: Patterns of Engagement across Time in Massive Open Online Courses (MOOCs) , 2016 .

[20]  L. Bradford,et al.  Off the Beaten Track: Messages as a Means of Reducing Social Trail Use at St. Lawrence Islands National Park , 2007 .

[21]  KöckMirjam,et al.  Activity sequence modelling and dynamic clustering for personalized e-learning , 2011 .

[22]  Jens Liegle,et al.  The effect of learning styles on the navigation needs of Web-based learners , 2006, Comput. Hum. Behav..