Home > Research > Publications & Outputs > A time series classification method for behavio...

Electronic data

  • A Time Series Classification Method for Behaviour-Based Dropout Prediction camera ready

    Rights statement: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 342 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

A time series classification method for behaviour-based dropout prediction

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date10/08/2018
Host publicationProceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018
EditorsNian-Shing Chen, Maiga Chang, Ronghuai Huang, K. Kinshuk, Kannan Moudgalya, Sahana Murthy, Demetrios G Sampson
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages191-195
Number of pages5
ISBN (Print)9781538660492
<mark>Original language</mark>English
Event18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018 - Bombay, India
Duration: 9/07/201813/07/2018

Conference

Conference18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018
Country/TerritoryIndia
CityBombay
Period9/07/1813/07/18

Conference

Conference18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018
Country/TerritoryIndia
CityBombay
Period9/07/1813/07/18

Abstract

Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

Bibliographic note

©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.