A big data framework for early identification of dropout students in MOOC

Jeff K.T. TANG, Haoren XIE, Tak Lam WONG*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

18 Citations (Scopus)

Abstract

Massive Open Online Courses (MOOC) became popular and they posted great impact to education. Students could enroll and attend any MOOC anytime and anywhere according to their interest, schedule and learning pace. However, the dropout rate of MOOC was known to be very high in practice. It is desirable to discover students who have high chance to dropout in MOOC in early stage, and the course leader could impose intervention immediately in order to reduce the dropout rate. In this paper, we proposed a framework that applies big data methods to identify the students who are likely to dropout in MOOC. Real- world data were collected for the evaluation of our proposed framework. The results demonstrated that our framework is effective and helpful.

Original languageEnglish
Title of host publicationTechnology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers
EditorsTak Lam WONG, Jeanne LAM, Kwan Keung NG, Fu Lee WANG, Simon K.S. CHEUNG, Kam Cheong LI
Place of PublicationBerlin
PublisherSpringer-Verlag GmbH and Co. KG
Pages127-132
Number of pages6
ISBN (Electronic)9783662489789
ISBN (Print)9783662489772
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventThe 2nd International Conference on Technology in Education - Caritas Institute of Higher Education, Hong Kong, China
Duration: 2 Jul 20154 Jul 2015
http://icte2015.cihe.edu.hk/ICT4K.html

Publication series

NameCommunications in Computer and Information Science
Volume559
ISSN (Print)1865-0929

Conference

ConferenceThe 2nd International Conference on Technology in Education
Abbreviated titleICTE 2015
CountryChina
CityHong Kong
Period2/07/154/07/15
Internet address

Bibliographical note

This work is partially supported by the Small Research Grant (MIT/SRG10/14-15) and the Internal Research Grant (IRG 30/2014-2015) of the Hong Kong Institute of Education.

Keywords

  • Big data
  • Decision tree
  • Dropout rate
  • MOOC

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    TANG, J. K. T., XIE, H., & WONG, T. L. (2015). A big data framework for early identification of dropout students in MOOC. In T. L. WONG, J. LAM, K. K. NG, F. L. WANG, S. K. S. CHEUNG, & K. C. LI (Eds.), Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers (pp. 127-132). (Communications in Computer and Information Science; Vol. 559). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-662-48978-9_12