# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 3 Techniques
### Machine Learning
#### 3.1.1 Supervised Learning  
Supervised Learning (SL) seeks algorithms able to reason from instances externally supplied in order to produce general hypotheses, which then make predictions about future instances [66]. In other  
Figure 1: Basic statistics about the techniques, objectives and algorithms tackled in the literature review. words, the goal of SL is to build a clear model of the distribution of class labels in terms of predictor characteristics. Rule Induction is an efficient SL method to make predictions, which was able to reach an accuracy level of 94% when predicting dropout of new students in nursing courses, from 3978 records on 528 students [4]. When using classification techniques, it is necessary to be careful if there are unbalanced datasets, since they can produce misleading predictive accuracy. For this purpose, several improvements were proposed in [5] when predicting dropout, such as exploring a wide range of learning methods, selecting attributes, evaluating the effectiveness of theory, and studying factors between dropout and non-dropout students. The classifier algorithms explored in this study were One-R, C4.5, ADTrees, NB, BN, and Radial Basis Networks (RBN). In this sense, applying several algorithms and comparing their results will be always very useful, as in [6], where four classification algorithms (Logistic Regression (LR) [67], DT, ANN, and SVM) were compared with three data balancing techniques: Over-Sampling, Under-Sampling, and Synthetic Minority Over-Sampling (SMOTE). In this case, SVM with SMOTE gave the best accuracy (90.24%) for retention prediction. A promising technique was proposed in [7] for predicting the risk of dropout at early stages in online courses, where high dropout rate is a serious problem for this kind of courses at university level. This technique is based on a parallel combination of three ML techniques (K-Nearest Neighbor (KNN), RBN, and SVM), which make use of 28 attributes per student. Considering students' attributes, in [8] a set of ML algorithms (ANN, DT, and BN) took into account the personal characteristics of the students and their academic performance together with input attributes for building prediction models. The effectiveness of the prediction was evaluated using indicators such as the accuracy rate, recovery rate, overall accuracy rate and a particular measure. Moreover, if we take into account the cognitive characteristics of the students, the prediction accuracy improves using DT [9]. An SA framework for early identification of at-risk students was compared to other ML approaches [10], since more than 60% of dropouts occur in the first 2 years, especially in the areas of Science, Technology, Engineering, and Mathematics. Other ML algorithms (DT, NB, KNN, Gradient Boosted Tree (GBT), linear models, and Deep Learning (DL)) were proposed in [11] with similar purposes. Among them, DL and GBT showed the best accuracy. Other studies highlight the quality of SL techniques in predicting dropout: NB and SVM were proposed to predict of individual dropouts [12]; and Sequential Forward Selection (SFS), C4.5, RF, KNN and NB, among other classifiers, were proposed to identify students with difficulties in the third week with 97% accuracy [13]. Along these lines, the use of Random Forests (RF) showed excellent performance in predicting school dropout in terms of various performance metrics for binary classification [14]. Finally, ANN, SVM, LR, NB, and DT were analyzed in [15] for similar purposes by using the data recorded by e-learning tools. In this case, ANN and SVM achieved the highest accuracies. Several ML algorithms were compared in [2] to predict the performance of new students, where NB showed the best behaviour in a web tool. SVM was the best of the four techniques analyzed in [16] for predicting academic performance. Also Bayesian Belief Network (BNN) was used to predict the students' performance (grade point average) early [17]. Also LR and SVM were applied for this purpose [18].