# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 2 Related Work
### Related Work on Predicting Student Academic Performance  
[PERSON] et al. (2016) employed Naive Bayes (NB), Neural Networks (NNs), and Decision Trees (DTS) classification algorithms to predict the performance of undergraduate students [21]. The dataset was collected from two courses, both supported by a Learning Management System (LMS). The information retrieved included the access to teaching material, the performance on course assignments and the participation in discussion fora. The experimental results revealed that NB classifier outperformed the other two methods. Student performance prediction models were also constructed and compared in a similar study [22]. Demographic features, academic background and behavioral metrics for student and parent participation in the learning process were exploited for this purpose. Apart from Artificial Neural Network (ANN), NB and DTS algorithms, the authors also compared several ensemble methods such as Bagging, Boosting and Random Forest (RF). The ANN model outperformed the other data mining techniques, while Boosting was the best ensemble method. The prediction of whether a student should be considered as qualified or not was the research objective of [PERSON] et al. (2015) [23]. The authors experimented with four attribute evaluation methods and five classification algorithms, using a sample dataset of 152 regular high school students. The Multilayer Perceptron (MLP) was the best performing classifier among all other methods. [PERSON] et al. (2015) developed a prewarning system for students at risk of failing on the basis of a deep learning multiclass classification model [24]. The deep neural system developed in this work was a six-layer, fully connected feed-forward neural network with Rectified Linear Units (ReLU). The output layer was composed of five neurons with Softmax as a classifier. Each node on the output layer represented the students' final score {O, A, B, C, D}. The results showed that the proposed architecture acquired the highest accuracy values compared to three familiar classification algorithms: NB, MLP and Support Vector Machine (SVM). The objective of [PERSON]'s study (2016) was to discover relations between students' personal and social factors, as well as their educational performance using data mining tasks [25]. The authors collected 270 records through online questionnaires and tested four classification algorithms, while the values of the class attribute were: excellent, very good, good, and pass. The best performing method was the Classification and Regression Tree (CART) classifier, scoring an accuracy measure of 40%. In another comparative study regarding the effectiveness of EDM techniques [26], the authors compared four methods to predict students that are likely to fail in introductory programming courses at early stages. In addition, they demonstrated the importance of data preprocessing and algorithms fine-tuning tasks in the effectiveness of these techniques. Finally, the fine-tuned SVM was the best performer. Predicting students' graduation performance at the end of a degree program was one of the three research goals examined by [PERSON] et al. (2017) [27]. The data used comprised students' marks for all the courses in the four years of the program and variables related to students' pre-admission marks. The graduation mark (the class attribute) was divided into five possible values: A, B, C, D, E. The authors used several classification algorithms (DTS, k-Nearest Neighbor (kNN), NB, NNs and RF), while the maximum accuracy score (83.65%) was obtained by NB. Finally, a recent study indicated the effectiveness of semisupervised learning (SSL) methods in students' performance prediction [28]. The authors evaluated the performance of SSL algorithms, namely Self-Training, Democratic, De-Tri-Training, Tri-Training, Co-Training, RAndom Subspace CO-training (RASCO) and Rel-RASCO. The best overall averaged accuracy score was obtained by Tri-Training algorithm with a C4.5 Decision Tree as the base classifier.