# Transfer Learning from Deep Neural Networks for Predicting Student Performance
## 1 Introduction  
Transferring knowledge from one domain to another has gained a lot of attention among scientists in the past few years. Consider the task of predicting student performance (pass/fail) in higher education courses. According to the traditional supervised learning approach, a sufficient amount of training data, regarding a specific course \(C_{A}\), is required for building an accurate predictive model which is subsequently used for making predictions on testing data derived from the same course. If the testing dataset is derived from a different course, \(C_{B}\), sharing some common characteristics with course \(C_{A}\) (hereinafter referred to as related or similar courses), then transfer learning is the appropriate machine learning methodology for building accurate learning models in a more efficient manner, since it could contribute to the improvement of the predictive performance of the target domain model (course \(C_{B}\)) exploiting the knowledge of the source domain (course \(C_{A}\)) [1]. In a nutshell, a learning model is trained for a specific task using data derived from a source domain and, subsequently, it is reused for another similar task in the same domain or the same task in a different domain (target domain) [2,3]. More generally, when we lack information about a problem, we could train a learning model for a related problem, for which there is plenty of information, and apply it to the existing one. Transfer learning is currently gaining popularity in deep learning [4]. Not long ago, it was claimed as the second \"driver of machine learning commercial success\", whereas supervised learning was the first one [5]. Pre-trained deep networks, usually trained on large datasets and thus requiring significant computation time and resources, are employed as the starting point for other machine learning problems due to their ability to be repurposed either for a new or for a similar task. Therefore,these networks could support complex problems in a more efficient way, since they can decrease the training time for building a new learning model and finally improve its generalization performance [6]. In recent years, several types of Learning Management Systems (LMSs) have been successfully adopted by universities and higher education institutions, recording a variety of student learning features and gathering huge amounts of educational data. Educational Data Mining (EDM) is a fast-growing scientific field offering the potential to analyze these data and harness valuable knowledge from them. To this end, a plethora of predictive algorithms have been effectively applied in educational contexts for solving a wide range of problems [7]. However, building predictive models in the EDM field through transfer learning methods has been poorly studied so far. Therefore, the main question in the present study is whether a predictive model trained on a past course would perform well on a new one. [PERSON] and [PERSON] observe that courses (a) might evolve over time in a dissimilar way, even if they are not much different in terms of context and structure, (b) are populated with different students and instructors, and (c) might have features that cannot be transferred (e.g., a feature defined on a specific learning resource which is not available on another course) [8]. In addition, the complexity of LMSs as well as the course design have a significant impact on the course progress during the semester [9]. Therefore, there may be problems where transfer learning might not reflect the anticipating results, showing some uncertainty about the predictive accuracy of the newly created learning model [10]. In this context, the present study aims to propose a transfer learning methodology for predicting student performance in higher education, a task that has been extensively studied in the field of EDM through traditional supervised methods. To this purpose, we exploit a set of five datasets corresponding to five undergraduate courses, each one lasting one semester, all supported by a Moodle platform.