Transfer Learning from Deep Neural Networks for Predicting Student Performance
Source abstract
Abstract
Transferring knowledge from one domain to another has gained a lot of attention among scientists in recent years. Transfer learning is a machine learning approach aiming to exploit the knowledge retrieved from one problem for improving the predictive performance of a learning model for a different but related problem. This is particularly the case when there is a lack of data regarding a problem, but there is plenty of data about another related one. To this end, the present study intends to investigate the effectiveness of transfer learning from deep neural networks for the task of students’ performance prediction in higher education. Since building predictive models in the Educational Data Mining field through transfer learning methods has been poorly studied so far, we consider this study as an important step in this direction. Therefore, a plethora of experiments were conducted based on data originating from five compulsory courses of two undergraduate programs. The experimental results demonstrate that the prognosis of students at risk of failure can be achieved with satisfactory accuracy in most cases, provided that datasets of students who have attended other related courses are available.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Domain Adaptation and Few-Shot Learning · Imbalanced Data Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mathematics, University of Patras, 26504, Rio Patras, Greece
References (33)
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