Transfer Learning from Deep Neural Networks for Predicting Student Performance
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 b...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/6/2145 |
| id |
doaj-8d17ac72c64e47eea9240b6c746d42f1 |
|---|---|
| record_format |
Article |
| spelling |
doaj-8d17ac72c64e47eea9240b6c746d42f12020-11-25T01:54:54ZengMDPI AGApplied Sciences2076-34172020-03-01106214510.3390/app10062145app10062145Transfer Learning from Deep Neural Networks for Predicting Student PerformanceMaria Tsiakmaki0Georgios Kostopoulos1Sotiris Kotsiantis2Omiros Ragos3Department of Mathematics, University of Patras, 26504, Rio Patras, GreeceDepartment of Mathematics, University of Patras, 26504, Rio Patras, GreeceDepartment of Mathematics, University of Patras, 26504, Rio Patras, GreeceDepartment of Mathematics, University of Patras, 26504, Rio Patras, GreeceTransferring 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.https://www.mdpi.com/2076-3417/10/6/2145transfer learningdeep learningeducational data miningstudent performance prediction |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Maria Tsiakmaki Georgios Kostopoulos Sotiris Kotsiantis Omiros Ragos |
| spellingShingle |
Maria Tsiakmaki Georgios Kostopoulos Sotiris Kotsiantis Omiros Ragos Transfer Learning from Deep Neural Networks for Predicting Student Performance Applied Sciences transfer learning deep learning educational data mining student performance prediction |
| author_facet |
Maria Tsiakmaki Georgios Kostopoulos Sotiris Kotsiantis Omiros Ragos |
| author_sort |
Maria Tsiakmaki |
| title |
Transfer Learning from Deep Neural Networks for Predicting Student Performance |
| title_short |
Transfer Learning from Deep Neural Networks for Predicting Student Performance |
| title_full |
Transfer Learning from Deep Neural Networks for Predicting Student Performance |
| title_fullStr |
Transfer Learning from Deep Neural Networks for Predicting Student Performance |
| title_full_unstemmed |
Transfer Learning from Deep Neural Networks for Predicting Student Performance |
| title_sort |
transfer learning from deep neural networks for predicting student performance |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2020-03-01 |
| description |
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. |
| topic |
transfer learning deep learning educational data mining student performance prediction |
| url |
https://www.mdpi.com/2076-3417/10/6/2145 |
| work_keys_str_mv |
AT mariatsiakmaki transferlearningfromdeepneuralnetworksforpredictingstudentperformance AT georgioskostopoulos transferlearningfromdeepneuralnetworksforpredictingstudentperformance AT sotiriskotsiantis transferlearningfromdeepneuralnetworksforpredictingstudentperformance AT omirosragos transferlearningfromdeepneuralnetworksforpredictingstudentperformance |
| _version_ |
1724986264841617408 |