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A CNN–LSTM-based deep learning model for early prediction of student’s performance

Monika Arya

Monika Arya

Department of Computer Science and Engineering, CMR Engineering CollegeHyderabad, India
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Arya, Monika
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Anand Motwani

Anand Motwani

Department of Computer Science, School of Computing Science and Engineering, VIT Bhopal UniversitySehore, India
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Motwani, Anand
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Kauleshwar Prasad

Kauleshwar Prasad

Department of Computer Science and Engineering, BITIndia
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Prasad, Kauleshwar
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Bhupesh Kumar Dewangan

Bhupesh Kumar Dewangan

Department of Computer Science and Engineering, OP Jindal UniversityRaigarh, India
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Dewangan, Bhupesh Kumar
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Tanupriya Choudhury

Tanupriya Choudhury

School of Computer Science, University of Petroleum and Energy Studies (UPES)Dehradun, India
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Choudhury, Tanupriya
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Piyush Chauhan

Piyush Chauhan

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University)Pune, India
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Chauhan, Piyush
  
02 dic 2024
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Categoría del artículo: Research Article
Publicado en línea: 02 dic 2024
Recibido: 10 sept 2024
DOI: https://doi.org/10.2478/ijssis-2024-0036
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© 2024 Monika Arya et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Class distribution of dataset.
Class distribution of dataset.

Figure 2:

Flowchart. CNN, convolutional neural network; LSTM, long short-term memory.
Flowchart. CNN, convolutional neural network; LSTM, long short-term memory.

Figure 3:

Comparison of average accuracy. CNN, convolutional neural network; LSTM, long short-term memory.
Comparison of average accuracy. CNN, convolutional neural network; LSTM, long short-term memory.

Figure 4:

Comparison of average loss. CNN, convolutional neural network; LSTM, long short-term memory.
Comparison of average loss. CNN, convolutional neural network; LSTM, long short-term memory.

Summary of related work

Reference Dataset used Evaluation metrics Limitations/future scope
[1] Datasets of two Udacity ND programs: ND-A and ND-B ROC In future, the indirect data can be incorporated into the GritNet.
[2] WOU, XAPI, UCI, and AV student performance datasets Precision, recall, F-score, and accuracy Further validation on other large size imbalanced datasets is required.
[3] Real data were collected from a multidisciplinary university MAE and RMSE Reliability of the system can be improved further by updating layers of NN
[4] Dataset acquired from the “Khyber Pakh tunkhwa Board of Intermediate & Secondary Education” Peshawar Accuracy and RMS In big data environment, the DL models need to be integrated with traditional ML techniques. RNN model that updates learning rate is required to maximize precision of the prediction framework
[5] Three different datasets Accuracy Educational contexts such as course subjects must be taken into account. Sample size aspect that verifies the empirical research results and implications is overlooked
[6] Open University dataset Precision, recall, accuracy, F1, and FM This study basically focused on online mode of study, and in future, other modes can be analyzed. Hybrid models of TLBO optimization, ANN, and SVM were evaluated. Evaluation and comparison with other hybrid models is required to achieve more reliable predictions of academic performance
[7] House, WOU, XAPI, UCI, and AV dataset Accuracy In future, the CNN model can be squeezed to reduce CNN structures
[8] OULAD Precision, recall, F1-score, and accuracy As a limitation of this study, researchers should consider that MOOC students generate large clickstream records, and DL techniques require significant training time, which can delay data processing and evaluation of results.
[9] OULAD Precision, recall, F1-score, and accuracy With fewer data streams, the system achieves lower accuracy, while more data streams improve prediction performance. In the future, data from various institutions and study areas will be collected to assess performance variations. Additionally, other DL and ML algorithms will be integrated to better understand relationships among student academic attributes and enhance prediction accuracy
[10] OULA dataset Accuracy, sensitivity, specificity, and precision Limitation: It uses only a single dataset and limited performance metrics for predicting student performance, which affects its overall effectiveness. To achieve better results, more data should be considered. Future research can expand this work by using multiple datasets for student performance prediction

Descriptive analysis

School Sex Age Address Family size … Absences G1 G2 G3 Performance
Count 649 649 649 649 649 … 649 649 649 649 649
Unique 2 2 8 2 2 … 24 27 16 17 2
Top GP F 17 U GT3 … 0 10 11 11 Good
Frequency 423 383 179 452 457 … 244 95 103 104 550

Comparison of LSTM-based student performance prediction models

S.No Model Average accuracy Average training time Average loss
1 Simple LSTM 85.83 333 ms/step 0.2516
2 LSTM with dropouts 86.68 659 ms/step 0.2334
3 Bidirectional LSTM 88.69 2 s/step 0.2561
4 Proposed CNN + LSTM 98.45 183 ms/step 0.1989
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