Predicting dropout-prone students in e-learning education system

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

15 Citations (Scopus)

Abstract

High rate of students dropout in courses has been a major problem for many universities or educational institutions that offer online education. If the dropout-prone students can be identified in their early stages, the dropout rate can be reduced by providing individualized care to the students at-risk. Due to the electronic nature of the e-learning courses, various attributes of the student progress can be monitored and analyzed automatically over time. In this paper, a technique for predicting students who are prone to dropout from the online courses has been proposed that progressively analyzes a set of per-learner attributes of the students' activities overtime. Since a single machine learning technique may fail to accurately identify some dropout-prone students whereas others may succeed, this technique uses a combination of multiple classifiers (ensemble of classifiers) for this analysis. The results of the validation found the technique to be promising in predicting dropout-prone students.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
EditorsJianhua Ma, Ali Li, Huansheng Ning, Laurence T. Yang
Pages1735-1740
Number of pages6
ISBN (Electronic)9781467372114
DOIs
Publication statusPublished - 20 Jul. 2016
EventProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015 - Beijing, China
Duration: 10 Aug. 201514 Aug. 2015

Publication series

NameProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015

Conference

ConferenceProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
Country/TerritoryChina
CityBeijing
Period10/08/1514/08/15

Keywords

  • Dropout prediction
  • E-learning
  • Enseble of classifiers
  • Machine learning
  • Per-learner attributes

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