A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance
Abstract
:1. Introduction
- We developed a hybrid deep learning model that combines CNN and LSTM to enhance predictive accuracy in student performance.
- We improved data preprocessing by implementing strategies to address key challenges such as data imbalance and missing values.
- We incorporated advanced optimization and regularization techniques for improved prediction accuracy.
- We compared our model to others, including some of the highest-rated models in the literature.
2. Related Works
3. Materials and Methods
3.1. Description of the Datasets
3.1.1. Open University Learning Analytics Dataset (OULAD)
3.1.2. Western Ontario University (WOU) Dataset
3.2. Data Preprocessing
3.3. Proposed CNN–LSTM Model
3.3.1. Convolutional Neural Network (CNN)
3.3.2. Long Short-Term Memory (LSTM) Network
3.4. Model Tuning
3.5. Baseline Methods
Algorithm 1: CNN–LSTM Model |
Split data into training and testing sets: Input: x_train.shape [1] Output: Metrics Initialization: Define sequential model: model = Sequential () Add Conv1D, (filters, kernel size, activation, MaxPooling1D, LSTM, dropout, Dense) Model Compile optimizer, learning rate, epochs, batch size For epochs = 1 to n do while train model validate model monitor = ‘loss’ adjust loss function using categorical cross-entropy end while end for Evaluate Model Perform prediction using the model Calculate metrics |
3.6. Performance Metrics
3.6.1. Accuracy
3.6.2. Precision
3.6.3. Recall (Sensitivity)
3.6.4. F-Score
4. Results and Discussion
4.1. Proposed CNN–LSTM Model Performance Using the OULAD Dataset
4.2. Proposed CNN–LSTM Model’s Performance Using the WOU Dataset
4.3. Proposed Model Training and Prediction Time
4.4. Significant Test Results Interpretation
4.5. Proposed Model Performance Comparison with Results from Similar Studies
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CNN | Convolutional neural network |
EDM | Educational data mining |
DL | Deep learning |
DNN | Deep neural network |
LSTM | Long short-term memory |
OULAD | Open University Learning Analytics Dataset |
WOU | Western Ontario University |
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Article | Model | Performance (Acc) |
---|---|---|
Hai-tao et al. [10] | Graph CNN | 81.5% |
Poudyal et al. [11] | CNN | 88% |
Wang et al. [12] | SVM and RNN | 86.9% |
Mengash et al. [13] | ANN, DT, SVM, NB | 79% |
Asselman et al. [14] | RF, Adaboost, XGBoost | 78.25%, 78.30%, and 78.75% |
Turabieh et al. [15] | Harris Hawks Optimization (HHO) Algorithm and Layered RNN | 92% |
Yousafzai et al. [16] | Attention based-BILSTM | 90.16% |
Mahareek et al. [17] | SVM | 67.77% |
Yağcı et al. [18] | SVM, NB, KNN, LR, RF | 70% |
Keser et al. [19] | GB, XGB, LGB | 96.6% and 91.2% |
Alarape et al. [20] | SVM and NB | 92.73% and 89.09% |
Module | Domain | Presentations | Students |
---|---|---|---|
AAA | Social Sciences | 2 | 748 |
BBB | Social Sciences | 4 | 7909 |
CCC | STEM | 2 | 4434 |
DDD | STEM | 4 | 6272 |
Parameters | Configuration/Value |
Learning rate | 0.001 |
Number of epochs | 10 |
Batch sizes | 32 |
Activation function | ReLU and Softmax |
Loss function | Categorical cross-entropy |
Optimization algorithm | Adam optimizer |
Hyperparameter optimization | Random search |
Dropout rate | 0.5 |
Regularization techniques | Dropout technique |
Model | Accuracy | Precision | Recall | F-Score |
---|---|---|---|---|
DNN | 92.20 | 91.70 | 92.00 | 91.90 |
CNN | 96.10 | 96.13 | 96.01 | 95.19 |
LSTM | 97.62 | 97.61 | 97.62 | 97.61 |
CNN–LSTM | 98.93 | 98.93 | 98.93 | 98.93 |
Model | Accuracy | Precision | Recall | F-Score |
---|---|---|---|---|
DNN | 86.52 | 88.84 | 86.38 | 87.59 |
CNN | 92.20 | 91.80 | 92.00 | 91.90 |
LSTM | 96.00 | 97.45 | 96.00 | 92.60 |
CNN–LSTM | 98.82 | 97.53 | 97.61 | 97.56 |
Model | Training Time (s) | Prediction Time (s) |
---|---|---|
DNN | 0.10 | 0.09 |
CNN | 0.12 | 0.08 |
LSTM | 0.14 | 0.08 |
CNN–LSTM | 0.14 | 0.06 |
Model | Accuracy | 95% Confidence Interval |
---|---|---|
DNN | 92.20 | [90.9%, 93.5%] |
CNN | 96.10 | [95.1%, 97.0%] |
LSTM | 97.62 | [96.9%, 98.3%] |
CNN–LSTM | 98.93 | [98.5%, 99.3%] |
Articles | Model | Accuracy (%) | Precision (%) | Recall (%) | F-Score (%) |
---|---|---|---|---|---|
[10] | GCNN | 81.5 | - | - | - |
[13] | ANN | 79.22 | 81.44 | 78.03 | 79.70 |
[16] | BiLSTM-AM | 90.16 | 90 | 90 | 90 |
[19] | GB, XGBoost, and LightGBM | 92.40, 94.13, 89.07 | - | - | 92.32, 94.00, 88.91 |
[11] | CNN | 88 | - | - | - |
[12] | SVM-RNN | 86.90 | - | 81.57 | - |
[18] | NN and RF | 74.6 | 74.8 | 74.6 | 72.3 |
Our Work | DNN | 92.20 | 91.70 | 92.00 | 91.90 |
CNN | 96.10 | 96.13 | 96.01 | 95.19 | |
LSTM | 97.62 | 97.61 | 97.62 | 97.61 | |
CNN–LSTM | 98.93 | 98.93 | 98.93 | 98.93 |
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Adefemi, K.O.; Mutanga, M.B. A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance. Digital 2025, 5, 16. https://doi.org/10.3390/digital5020016
Adefemi KO, Mutanga MB. A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance. Digital. 2025; 5(2):16. https://doi.org/10.3390/digital5020016
Chicago/Turabian StyleAdefemi, Kuburat Oyeranti, and Murimo Bethel Mutanga. 2025. "A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance" Digital 5, no. 2: 16. https://doi.org/10.3390/digital5020016
APA StyleAdefemi, K. O., & Mutanga, M. B. (2025). A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance. Digital, 5(2), 16. https://doi.org/10.3390/digital5020016