Design of a Personalized Massive Open Online Course Platform

Focusing on the massive open online course (MOOC) platform, the purpose of this study is to realize personalized adaptive learning according to the needs and abilities of each learner. To this end, the author created a personalized adaptive learning behaviour analysis model, and designed a personalized MOOC platform based on the model. Through the analysis of learning behaviours on the MOOC platform, the model digs deep into the pattern of learning behaviours, and lays the basis for personalized intervention in the learning process. The comparison ex-periments show that our prediction method is more accurate than the other predic-tion algorithms. This research sheds new light on the design of learner-specific MOOC platform.

[1]  Rita Kop,et al.  The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course , 2011 .

[2]  George Siemens,et al.  Courses : Innovation in Education ? , 2013 .

[3]  George Karypis,et al.  Predicting Student Performance Using Personalized Analytics , 2016, Computer.

[4]  Hélène Fournier,et al.  The value of learning analytics to networked learning on a personal learning environment , 2011, LAK.

[5]  George Karypis,et al.  Grade prediction with models specific to students and courses , 2016, International Journal of Data Science and Analytics.

[6]  Robert Zemsky.  With a MOOC MOOC Here and a MOOC MOOC There, Here a MOOC, There a MOOC, Everywhere a MOOC MOOC , 2014 .

[7]  A. Kaplan,et al.  Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster , 2016 .

[8]  William D. Milheim,et al.  Massive Open Online Courses (MOOCs): Current Applications and Future Potential. , 2013 .

[9]  Katy Jordan,et al.  Initial trends in enrolment and completion of massive open online courses , 2014 .

[10]  Jeremy Roschelle,et al.  Designing Formative Assessment Software with Teachers: an Analysis of the Co-Design Process , 2007, Res. Pract. Technol. Enhanc. Learn..

[11]  David E. Pritchard,et al.  Studying Learning in the Worldwide Classroom Research into edX's First MOOC. , 2013 .

[12]  Cathy Sandeen.  Integrating MOOCS into Traditional Higher Education: The Emerging “MOOC 3.0” Era , 2013 .

[13]  Tom Adawi,et al.  “Time is the bottleneck”: a qualitative study exploring why learners drop out of MOOCs , 2016, Journal of Computing in Higher Education.

[14]  Elana Zeide,et al.  The Structural Consequences of Big Data-Driven Education , 2017, Big Data.

[15]  Sebastián Ventura,et al.  Predicting students' final performance from participation in on-line discussion forums , 2013, Comput. Educ..

[16]  Chelsea Dobbins,et al.  The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs) , 2015, Res. Pract. Technol. Enhanc. Learn..

[17]  Candace Thille,et al.  The Future of Data-Enriched Assessment. , 2014 .

[18]  Araz Taeihagh,et al.  MOOCs and Crowdsourcing: Massive Courses and Massive Resources , 2015, First Monday.

[19]  Carlos R. Rivero,et al.  Automated Personalized Feedback in Introductory Java Programming MOOCs , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).