# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 2 Related Work
### Related Work on Predicting Student Grade  
Personalized multiple regression-based methods and matrix factorization approaches based on recommender systems were used by [PERSON] et al. (2016) to forecast students' grades in future courses and in-class assessments [29]. Briefly, the first method was the course-specific regression, which predicted the grade that a student will achieve in a specific course as a sparse linear combination of the grades that the student obtained in past courses. The second method was the personalized linear multiregression, which employed a linear combination of k regression models, weighted on a per-student basis. The third method was a standard matrix factorization approach that approximated the observed entries of the student-course grade matrix. The fourth method was matrix factorization based on factorization machines. The evaluations showed that the factorization machines produced lower error rates for the next-term grade prediction. Predicting student performance was also the main focus of the study conducted by [PERSON] et al. (2017) [30]. Their goal was to predict the final cumulative Grade Point Average (GPA) of a student, given his/her background and performance states of the known grades and the predictions for the courses that have not been taken. For enabling such progressive predictions, the authors proposed a two-layer architecture. The first layer implements the base predictors for each course, given the performance state of graduate students on courses relevant to the targeted course. For discovering the relevant courses, a course clustering method was developed. In the second layer, ensemble-based predictors were developed, able to keep improving themselves by accumulating new student data over time. The authors' architecture was compared with four classic machine learning algorithms, named Linear Regression (LR), Logistic Regression (LogR), RF and kNN. The proposed method yielded the best prediction performance. Predicting students' final grade was one of the two research goals also by [PERSON] et al. (2015) [31]. The authors evaluated various popular regression algorithms, i.e., Ordinary Least Squares, SVM, CART, kNN, RF and AdaBoost R2. The experiments were carried out using administrate data from the university's Student Information System (SIS) of Porto, concerning approximately 700 courses. The algorithms with best results overall were SVM, RF and AdaBoost R2. The proposed method by [PERSON] et al. (2015) made personalized and timely predictions of the grade of each student in a class [32]. Using data obtained from a pilot course, the authors' methodology suggested that it was effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student. The study compared their proposed algorithm against four different prediction methods: two simple benchmarks; Single performance assessment and past assessments and weights and two well-known data mining algorithms; Linear Regression (LR) employing the Ordinary Least Squares (OLS) method and kNN algorithm (k = 7). The error of the proposed method decreased approximately linearly as more homework and in-class exam results were added to the model. [PERSON] et al. (2016) also presented the problem of student performance prediction as a regression task [33]. They explored three classes of methods for predicting the next-term grade of students. These were (1) simple baselines, (2) Matrix Factorization (MF)-based methods, and (3) common regression models. For the third category of methods, four different regression models were tested: RF, Stochastic Gradient Descent (SGD), kNN and personalized LR. The obtained results revealed that a hybrid of the RF model and the MF-based Factorization Machine (FM) was the best performer. The first study that applied Semi-Supervised Regression (SSR) methods for regression tasks in educational settings was carried out by [PERSON] et al. (2019) [34].