# Transfer Learning from Deep Neural Networks for Predicting Student Performance
## 4 Research Methodology
### The Proposed Transfer Learning Approach
Particularly, this is observed in the cases where \(C_{1}\) starts with \(C_{2}\) weights, \(C_{2}\) with \(C_{1}\) weights and \(C_{4}\) with \(C_{1}\) weights. For instance, \(C_{1}\) outperforms the baseline with an accuracy measure of 0.7768 after 100 epochs of retuning the preloaded weights of \(C_{2}\). However, after 150 epochs the accuracy is decreased to 0.7552. ## 6 Discussion

An important finding to emerge in this study is that even a small amount of prior knowledge from a past course dataset could result in a fair measure of accuracy for predicting student performance in a related current course. This was verified by a plethora of experiments that have been carried out regarding twenty different pairs of five distinct one-semester courses, investigating the effectiveness of transfer learning in deep neural networks for the task of predicting at-risk students in higher education. In most cases, the transfer model obtained better accuracy than the baseline one. An improvement was noticed in half of the datasets (10 out of 20) using the pretrained weights from the source course (i.e., zero epochs). There was also a considerable accuracy improvement in most cases (16 out of 20) when the pre-trained model was further tuned for 10 to 40 epochs. Therefore, fine-tuning provides a substantial benefit over training with random initialization of the weights, thus leading to higher accuracy with fewer passes over the data. Overall, there was only one case where the transfer learning did not achieve better results (\(C_{5,4}\)). Hence, it is evident that it is not always feasible to transfer knowledge from one course to another one. In addition, it is worth noting that the type of course, laboratory or theoretical, does not seem to directly affect the predictive accuracy of the transfer learning model. This indicates that there is a slight uncertainty about the transferability level of a predictive model. The definition of what is a \"transferable\" model is where this ambiguity lies. A model trained on a set of courses is considered to be \"transferable\" if it achieves respectively fair results on a new, related course [10]. We believe this is yet another important attempt towards transfer knowledge in the educational field. Further, there are key issues to be considered such as measuring the degree of similarity between two courses (i.e., the number and form of learning activities), the type of attributes and the duration of the course. Finally, it is similarly important to build both simple and interpretable transferable models that could be easily applied by educators from one course to another [29]. Therefore, more studies are required on the current topic for establishing these results. ## 7 Conclusions

In the present study, an effort was made to propose a transfer learning method for the task of predicting student performance in undergraduate courses. The identification of failure-prone students could lead the academic staff developing learning strategies that aim to improve students' academic performance [32]. Transfer learning enables us to train a deep network using the dataset of a past course (source course) and reuse it as the starting point for a dataset of a new related course (target course). Moreover, it is possible to further tune the repurposed model. Our findings proved that a fair performance was achieved in most cases, while the proposed method handily outperforms the baseline model. Transfer learning offers many future research directions. Our results are encouraging and should be validated by larger samples of courses from different departments and programs. An interesting task is to apply a model for a specific task, such as the prediction of student's performance, for another related task, such as the prediction of student's dropout or for regression tasks (e.g., for predicting students' grades). In a future work we will also investigate the efficiency of transfer learning in imbalanced datasets obtained from several educational settings.