A Time Series Classification Method for Behaviour-Based Dropout Prediction
| Autor: | Liu Haiyang, Phillip Benachour, Zhihai Wang, Philip Tubman |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Series (mathematics)
Computer science business.industry 05 social sciences Distance education 050301 education 02 engineering and technology Construct (python library) Machine learning computer.software_genre Statistical classification Metric (mathematics) ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence Time series business 0503 education computer Dropout (neural networks) |
| Zdroj: | ICALT |
| DOI: | 10.1109/icalt.2018.00052 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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