# Transfer Learning from Deep Neural Networks for Predicting Student Performance
## 3 Related Work  
Predicting students' learning outcomes is considered one of the major tasks of the EDM field [22]. This is demonstrated by a great number of significant studies which put emphasis on the development and implementation of data mining methods and machine algorithms for resolving a plethora of predictive problems [23]. These problems are mainly intended to predict the future value of an attribute (e.g., students' grades, academic performance, dropout, etc.) based on a set of input attributes that describe a student. One typical problem is to detect whether a student is going to successfully pass or fail a course by the end of a semester based on his/her activity on the LMS, as in this study. The successful and accurate detection of students at risk of failure is of vital importance for educational institutions, since remedial measures and intervention strategies could be applied to support low performers and enhance their overall learning performance [24]. It is therefore necessary to build very accurate and robust learning models. Transfer learning could contribute to improving these models, since prior knowledge regarding a specific task could be useful to another similar task. Transfer learning is an approach which has still not been sufficiently examined in the field of EDM, as evidenced by the study of the current literature. To the best of our knowledge, there are few studies focusing on resolving prediction problems through transferring learning models from one domain to another, although this prospect is appealing. These studies indicate that building models based on a particular course and them applying to a new one (different but somehow related) is a rather complex task, which, unfortunately, does not always reflect the anticipating outcomes [10]. A list of some notable works regarding transfer learning in the EDM field are presented in the following paragraphs. [PERSON] et al. investigated the transferability of dropout prediction across Massive Online Open Courses (MOOCs) [9]. Therefore, they presented two variations of transfer learning based on autoencoders: (a) using the transductive principal component analysis, and (b) adding a correlation alignment loss term. The input data were click-stream log events of mixtures of similar and dissimilar courses. The proposed transfer learning methods proved to be quite effective for improving the dropout prediction, in terms of Area Under Curve (AUC) scores, compared to the baseline method. In a similar study, [PERSON] et al. [25] examined how models trained on a MOOC system could be transferred to another. Therefore, they built a unified model allowing the early prediction of dropout students across two different systems. At first, the authors confirmed significant differences between the two systems, such as the number of active students and the structure of courses. After that, they defined a set of features based on the event logs of the two systems. Overall, three dropout prediction experiments were conducted: one for each separate system, one where each system applied a learning model built on the other system and one where the dataset contained data from both systems. The accuracy measure was above the baseline threshold (0.5) in most cases. The method put forward by [PERSON] et al. [26] examined the effectiveness of TrAdaBoost, an extended AdaBoost version in the transfer learning framework, for predicting students' graduation rates in undergraduate programs. The dataset was based on a set of academic and demographic features (152 features in total) regarding 7637 students of different departments. Two separate experiments were conducted, each time using specific data for the training set. In the first experiment, the training set comprised all students apart from those studying engineering, while in the second one, the training set comprised all students that were suspended on academic warnings. The experimental results showed that the TrAdaBoost method recorded the smallest error in both cases.