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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - A time series classification method for behaviour-based dropout prediction
AU - Haiyang, Liu
AU - Wang, Zhihai
AU - Benachour, Phillip
AU - Tubman, Philip
N1 - ©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.
PY - 2018/8/10
Y1 - 2018/8/10
N2 - 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.
AB - 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.
KW - Dropout prediction
KW - MOOCS
KW - Online distance learning
KW - Student interaction and behaviour
KW - Time series
KW - VLEs
U2 - 10.1109/ICALT.2018.00052
DO - 10.1109/ICALT.2018.00052
M3 - Conference contribution/Paper
AN - SCOPUS:85052499240
SN - 9781538660492
SP - 191
EP - 195
BT - Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018
A2 - Chen, Nian-Shing
A2 - Chang, Maiga
A2 - Huang, Ronghuai
A2 - Kinshuk, K.
A2 - Moudgalya, Kannan
A2 - Murthy, Sahana
A2 - Sampson, Demetrios G
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018
Y2 - 9 July 2018 through 13 July 2018
ER -