A time series classification method for behaviour-based dropout prediction

Haiyang, Liu and Wang, Zhihai and Benachour, Phillip and Tubman, Philip (2018) A time series classification method for behaviour-based dropout prediction. In: Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018. Institute of Electrical and Electronics Engineers Inc., IND, pp. 191-195. ISBN 9781538660492

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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.

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Contribution in Book/Report/Proceedings
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
136324
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Deposited On:
10 Dec 2019 14:45
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 11:54