|
| Author name | Techniques | Proposed method | Comparative methods | Problems |
|
| Göppert, A. et al. [20] | Artificial neural network | Dynamic interconnected based assembly systems for student performance prediction | ANN | Need to use a greedy approach for better results |
| Greedy heuristic approaches |
| Chango et al. [21] | Data diffusion-based approach | Multi-mode data fusion based student academic prediction model | JRIP REPTREE | Need to extract semantic level features |
| PART |
| Nage |
| Random tree |
| J48 |
| Mubarak et al. [22] | Combination of CNN and LSTM | A deep learning-based analytic model | CNN-based LSTM | Misclassification issues in complex data |
| DNN (deep neural network) |
| SVM (support vector machine) |
| Logistic regression (LR) |
| Fotso et al. [23] | Deep neural network | Deep learning-based model for learner performance prediction | RNN | For more effective results, need to work on more features |
| LSTM |
| GRU (gated recurrent unit) |
| Al Nagi et al. [24] | SVM | Machine learning-based model for student performance prediction | Decision tree | Poor feature extraction results |
| ANN | SVM (support vector machine) |
| DT | ANN (artificial neural network) |
| KNN | KNN (K-nearest neighbour) |
| Random forest |
| Raga et al. [25] | A deep neural network-based system | Blended learning-based DNN approach for student performance prediction | Dataset-based comparison | Limited information |
| Brahim (2022) [26] | Random-forest | Machine learning-based model for student performance prediction | SVM (support vector machine) | Poor feature extraction results; more advanced machine learning algorithms are needed for feature extraction |
| SVM | Decision tree |
| Naïve Bayes, | ANN (artificial neural networks) |
| Logistic regression | Naïve Bayes |
| MLP | KNN (K-nearest neighbor) |
| Logistic regression |
| Afzaal et al. [27] | Logistic regression | Machine learning-based approach, a dashboard that provides data-driven feedback for assessment of students outcomes | Logistic regression | The dashboard that provides feedback and recommendations does not provide evidence about improved student knowledge about course contents; the sample size is small |
| KNN (k nearest neighbors) | KNN (k nearest neighbors) |
| SVM | SVM |
| Random-forest | Random forest |
| MLP | MLP |
| BayesNet | BayesNet |
|
