# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 2 Related Work
### Related Work on Predicting Student Dropout  
One interested study used data gathered from 419 high schools students in Mexico [36]. The authors carried out experiments to predict dropout at different steps of the course, to select the best indicators of dropout. Results showed that their classifier (named ICRM2) could predict student dropout within the first 4-6 weeks of the course. Student retention and the identification of potential problems as early as possible was the main aim of [PERSON] et al. (2010) [37]. The authors used data from the Thames Valley University systems that were related to the background and the academic activities of the students. Three algorithms, namely NB, SVM and DTS, were chosen and different configurations for each algorithm were tested in order to find the optimum result. Finally, NB was reported to have achieved the highest prediction accuracy. Moreover, [PERSON] (2010) used five years of institutional data along with several popular data mining techniques (four individual and three ensemble techniques), in order to build models to predict and explain the reasons behind students dropping out [38]. The data contained variables related to students' academic, financial, and demographic characteristics. The SVM produced the best results when compared to ANN, DTS and LogR. The information fusion-type ensemble model produced the best results when compared with the Bagging and Boosting ensembles. [PERSON] et al. (2009) presented a dropout prediction method for e-learning courses, based on three machine learning techniques: NNs, SVM and the probabilistic ensemble simplified fuzzy ARTMAP [39]. The results of these techniques were combined using three decision schemes. The dataset consisted of demographic attributes, prior academic performances, time-varying characteristics depicting the students' progress during the courses, as well as their level of engagement with the e-learning procedure. The decision scheme where a student was considered to be a dropout if at least one technique has classified this student as such, was reported to be the most appropriate solution for achieving and maintaining high accuracy, sensitivity and precision results in predicting at-risk students. [PERSON] et al. (2006) applied NNs, discriminant analysis, DTS and RF on survey data from three universities, to classify new students in low-risk, medium-risk, and high-risk categories [40]. The authors found that the scholastic history and socio-family background were the most significant predictors of students at risk. The least bad result of the four methods was reported by the NNs method, reaching the total rate of correctly classified students up to 57.35%. [PERSON]'s study (2006) examined the predictive accuracy of the DTS and NNs over the problem of predicting college freshmen retention [41]. The author used three sources to produce the data set: the institutional student information system for student demographic; the American College Test (ACT)'s Student Profile Section for parent income data; and the National Student Clearinghouse for identifying transfer-out students. Overall prediction results showed that the DTS and NNS performed substantially better to the LR baseline. Also, the different results from the three NNs variations confirmed the importance of exploring available setup options. Finally, a recent study explored the usage of semi-supervised techniques for the task of drop out prediction [42]. The dataset consisted of 2 classes of 344 instances characterized by 12 attributes. The authors compared familiar SSL techniques, Self-Training, Co-Training, Democratic Co-Training, Tri-Training, RASCO and Rel-RASCO. In their two separate experiments, C4.5 and NB were the base classifiers, while C4.5 was the dominant supervised algorithm. The results revealed that Tri-Training (C4.5) algorithm outperformed the rest SSL algorithms as well as the supervised C4.5 Decision Tree.