Different Machine Learning Models to Predict Dropouts in MOOCs

Avinash Kashyap, Ashalatha Nayak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Massive Open Online Courses have emerged as an alternative to the traditional educational system because of the flexibility in timings and also it overcomes the economic and geographical barriers for the users. MOOCs also help learners from diverse background to communicate and exchange knowledge in MOOCs forums. The number of learners enrolling in such courses is very high. Despite the unrestricted accessibility, the completion rate is very low. Various factors affect the completion of the course by the students such as interest in the subject, the purpose of enrolling in the subject, whether the lecturer is able to convey his knowledge to the students or not. EDM (Educational Data Mining) and LA (Learning Analytics) are the fields in which data of students learning activity is analyzed to obtain certain vital information or can be used in prediction using EDM tools and techniques. Data analysis shows that there is a strong relationship between the number of events such as click event, video watched, forum post and the successful learner's outcome. Machine Learning algorithms are applied on the dataset from HarvardX and the result shows that Random Forest gives an optimum result with the highest performance.

Original languageEnglish
Title of host publication2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages80-85
Number of pages6
ISBN (Electronic)9781538653142
DOIs
Publication statusPublished - 30-11-2018
Event7th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018 - Bangalore, India
Duration: 19-09-201822-09-2018

Conference

Conference7th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018
CountryIndia
CityBangalore
Period19-09-1822-09-18

Fingerprint

Learning systems
Students
Data mining
Learning algorithms
Economics

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Kashyap, A., & Nayak, A. (2018). Different Machine Learning Models to Predict Dropouts in MOOCs. In 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018 (pp. 80-85). [8554547] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2018.8554547
Kashyap, Avinash ; Nayak, Ashalatha. / Different Machine Learning Models to Predict Dropouts in MOOCs. 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 80-85
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Kashyap, A & Nayak, A 2018, Different Machine Learning Models to Predict Dropouts in MOOCs. in 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018., 8554547, Institute of Electrical and Electronics Engineers Inc., pp. 80-85, 7th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018, Bangalore, India, 19-09-18. https://doi.org/10.1109/ICACCI.2018.8554547

Different Machine Learning Models to Predict Dropouts in MOOCs. / Kashyap, Avinash; Nayak, Ashalatha.

2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 80-85 8554547.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kashyap A, Nayak A. Different Machine Learning Models to Predict Dropouts in MOOCs. In 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 80-85. 8554547 https://doi.org/10.1109/ICACCI.2018.8554547