A Novel Methodology Using RNN + LSTM + ML for Predicting Student's Academic Performance
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| Title: | A Novel Methodology Using RNN + LSTM + ML for Predicting Student's Academic Performance |
|---|---|
| Language: | English |
| Authors: | Ashima Kukkar, Rajni Mohana, Aman Sharma, Anand Nayyar (ORCID |
| Source: | Education and Information Technologies. 2024 29(11):14365-14401. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 37 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Predictor Variables, Academic Achievement, Pass Fail Grading, Long Term Memory, Short Term Memory, Data Use, Influences, Evaluation Methods, Individual Characteristics, Neurology, Models, Correlation |
| DOI: | 10.1007/s10639-023-12394-0 |
| ISSN: | 1360-2357 1573-7608 |
| Abstract: | In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often struggle to capture the temporal dynamics inherent in student data, such as learning trajectories, study habits, and evolving performance patterns. In response, this research leverages Recurrent Neural Network (RNNs) and Long Short Term Memory (LSTM) network (LSTMs), which are specifically designed to model sequences and long-term dependencies from OULAD and self-generated Emotional dataset. By incorporating these architectures, the proposed methodology excels in capturing the intricate relationships between various factors over time. Further, various ML models such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) are integrated with RNN + LSTM to enhance the predictive power of model. The proposed system with RNN + LSTM + RF techniques gained approximately 97% accuracy that is comparatively higher than RNN + LSTM + SVM, RNN + LSTM + NB and RNN + LSTM + DT i.e., 90.67%, 86.45% & 84.42% respectively. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1437401 |
| Database: | ERIC |
| Abstract: | In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often struggle to capture the temporal dynamics inherent in student data, such as learning trajectories, study habits, and evolving performance patterns. In response, this research leverages Recurrent Neural Network (RNNs) and Long Short Term Memory (LSTM) network (LSTMs), which are specifically designed to model sequences and long-term dependencies from OULAD and self-generated Emotional dataset. By incorporating these architectures, the proposed methodology excels in capturing the intricate relationships between various factors over time. Further, various ML models such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) are integrated with RNN + LSTM to enhance the predictive power of model. The proposed system with RNN + LSTM + RF techniques gained approximately 97% accuracy that is comparatively higher than RNN + LSTM + SVM, RNN + LSTM + NB and RNN + LSTM + DT i.e., 90.67%, 86.45% & 84.42% respectively. |
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| ISSN: | 1360-2357 1573-7608 |
| DOI: | 10.1007/s10639-023-12394-0 |
