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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 0000-0002-9821-6146)
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
FullText Text:
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  Data: A Novel Methodology Using RNN + LSTM + ML for Predicting Student's Academic Performance
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  Data: <searchLink fieldCode="AR" term="%22Ashima+Kukkar%22">Ashima Kukkar</searchLink><br /><searchLink fieldCode="AR" term="%22Rajni+Mohana%22">Rajni Mohana</searchLink><br /><searchLink fieldCode="AR" term="%22Aman+Sharma%22">Aman Sharma</searchLink><br /><searchLink fieldCode="AR" term="%22Anand+Nayyar%22">Anand Nayyar</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9821-6146">0000-0002-9821-6146</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Education+and+Information+Technologies%22"><i>Education and Information Technologies</i></searchLink>. 2024 29(11):14365-14401.
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  Data: 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/
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  Data: 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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    Subjects:
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Pass Fail Grading
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      – SubjectFull: Long Term Memory
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      – SubjectFull: Evaluation Methods
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      – SubjectFull: Individual Characteristics
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      – SubjectFull: Neurology
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      – TitleFull: A Novel Methodology Using RNN + LSTM + ML for Predicting Student's Academic Performance
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