Early Predictor for Student Success Based on Behavioural and Demographical Indicators
نویسندگان
چکیده
As the largest distance learning university in UK, Open University has more than 250,000 students enrolled, making it also academic institute UK. However, many end up failing or withdrawing from online courses, which makes extremely crucial to identify those “at risk” and inject necessary interventions prevent them dropping out. This study thus aims at exploring an efficient predictive model, using both behavioural demographical data extracted anonymised Learning Analytics Dataset (OULAD). The model was implemented through machine methods that included BART. analytics indicates proposed could predict final result of course a finer granularity, i.e., classifying into Withdrawn, Fail, Pass, Distinction, rather only Completers Non-completers (two categories) as existing studies. Our model’s prediction accuracy 80% above for predicting would withdraw, fail get distinction. information be used provide accurate personalised interventions. Importantly, unlike similar studies, our predicts very beginning course, first assignment mark, among others, help reduce dropout rate before too late.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2021
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-030-80421-3_19