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

Jeff K.T. TANG; Haoren XIE; Tak Lam WONG

doi:10.1007/978-3-662-48978-9_12

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 Proceedings › Conference paper (refereed) › Research › peer-review

32 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 language	English
Title of host publication	Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers
Editors	Tak Lam WONG, Jeanne LAM, Kwan Keung NG, Fu Lee WANG, Simon K.S. CHEUNG, Kam Cheong LI
Place of Publication	Berlin
Publisher	Springer-Verlag GmbH and Co. KG
Pages	127-132
Number of pages	6
ISBN (Electronic)	9783662489789
ISBN (Print)	9783662489772
DOIs	https://doi.org/10.1007/978-3-662-48978-9_12
Publication status	Published - 2015
Externally published	Yes
Event	The 2nd International Conference on Technology in Education - Caritas Institute of Higher Education, Hong Kong, China Duration: 2 Jul 2015 → 4 Jul 2015 http://icte2015.cihe.edu.hk/ICT4K.html

Publication series

Name	Communications in Computer and Information Science
Volume	559
ISSN (Print)	1865-0929

Conference

Conference	The 2nd International Conference on Technology in Education
Abbreviated title	ICTE 2015
Country/Territory	China
City	Hong Kong
Period	2/07/15 → 4/07/15
Internet address	http://icte2015.cihe.edu.hk/ICT4K.html

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

Access to Document

10.1007/978-3-662-48978-9_12

Cite this

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

TANG, Jeff K.T. ; XIE, Haoren ; WONG, Tak Lam. / A big data framework for early identification of dropout students in MOOC. Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers. editor / Tak Lam WONG ; Jeanne LAM ; Kwan Keung NG ; Fu Lee WANG ; Simon K.S. CHEUNG ; Kam Cheong LI. Berlin : Springer-Verlag GmbH and Co. KG, 2015. pp. 127-132 (Communications in Computer and Information Science).

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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.",

keywords = "Big data, Decision tree, Dropout rate, MOOC",

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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.; The 2nd International Conference on Technology in Education, ICTE 2015 ; Conference date: 02-07-2015 Through 04-07-2015",

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TANG, JKT, XIE, H & WONG, TL 2015, A big data framework for early identification of dropout students in MOOC. in TL WONG, J LAM, KK NG, FL WANG, SKS CHEUNG & KC LI (eds), Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers. Communications in Computer and Information Science, vol. 559, Springer-Verlag GmbH and Co. KG, Berlin, pp. 127-132, The 2nd International Conference on Technology in Education, Hong Kong, China, 2/07/15. https://doi.org/10.1007/978-3-662-48978-9_12

A big data framework for early identification of dropout students in MOOC. / TANG, Jeff K.T.; XIE, Haoren; WONG, Tak Lam.
Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers. ed. / Tak Lam WONG; Jeanne LAM; Kwan Keung NG; Fu Lee WANG; Simon K.S. CHEUNG; Kam Cheong LI. Berlin: Springer-Verlag GmbH and Co. KG, 2015. p. 127-132 (Communications in Computer and Information Science; Vol. 559).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - A big data framework for early identification of dropout students in MOOC

AU - TANG, Jeff K.T.

AU - XIE, Haoren

AU - WONG, Tak Lam

N1 - 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.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

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KW - Decision tree

KW - Dropout rate

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DO - 10.1007/978-3-662-48978-9_12

M3 - Conference paper (refereed)

AN - SCOPUS:84952783417

SN - 9783662489772

T3 - Communications in Computer and Information Science

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EP - 132

BT - Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers

A2 - WONG, Tak Lam

A2 - LAM, Jeanne

A2 - NG, Kwan Keung

A2 - WANG, Fu Lee

A2 - CHEUNG, Simon K.S.

A2 - LI, Kam Cheong

PB - Springer-Verlag GmbH and Co. KG

CY - Berlin

T2 - The 2nd International Conference on Technology in Education

Y2 - 2 July 2015 through 4 July 2015

ER -

TANG JKT, XIE H, WONG TL. A big data framework for early identification of dropout students in MOOC. In WONG TL, LAM J, NG KK, WANG FL, CHEUNG SKS, LI KC, editors, Technology in Education : Technology-Mediated Proactive Learning - 2nd International Conference, ICTE 2015, Revised Selected Papers. Berlin: Springer-Verlag GmbH and Co. KG. 2015. p. 127-132. (Communications in Computer and Information Science). Epub 2015 Dec 8. doi: 10.1007/978-3-662-48978-9_12

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

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Keywords

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