Enhancing student success by developing hybrid NNRW-LSTM predictive multi-task performance prediction in higher education institutions
Student success in college is influenced by both academic and behavioral wellbeing. In this paper, a novel hybrid architecture called NNRW-LSTM (Neural Network Random Weights. Long Short-Term Memory) is proposed for multi-task predicting academic and behavioral risk among co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Academic Publications Ltd.
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46792/1/Enhancing%20student%20success%20by%20developing%20hybrid%20NNRW-LSTM.pdf https://doi.org/10.12732/ijam.v38i11s.1265 https://umpir.ump.edu.my/id/eprint/46792/ |
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| Summary: | Student success in college is influenced by both academic and behavioral wellbeing. In this paper, a novel hybrid architecture called NNRW-LSTM (Neural Network Random Weights. Long Short-Term Memory) is proposed for multi-task predicting academic and behavioral risk among college students. The model capitalizes on a large-scale data set including academic data, demographic variables, and behavioral indicators and combines static variables and semester-based time-series data into its framework. With a hybrid feature selection pipeline based on Mutual Information, Correlation Analysis, Recursive Feature Elimination, and PCA achieving high-relevance input variables and input dimensionality reduction, the proposed model predicts Cumulative GPA and behavioral warning percentages using a dual-branch neural network. Results show better performance relative to single-task approaches with Mean Absolute Errors 0.7415 and 0.3163 for GPA and behavioral warnings, respectively. The proposed method offers a scalable and interpretable solution for early risk detection and student-focused intervention in college settings. |
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