# Predicting Student Academic Performance by Means of Associative Classification
## 2 Literature Review  
In the context of student performance prediction, recent findings [3] have shown the high variability of classifier performance according to the learning context, the analyzed features, and the considered algorithms. The main research works conducted in this field have mainly addressed the following research questions:  
1. What are the most discriminating features to forecast the exam success rates? 2. From which time point can classifier predictions be deemed as reliable? 3. What are the most effective classification techniques? 4. Can we make Learning Analytics solutions interpretable and transparent to the end-users? A thorough analysis of each branch of research is given below. **(A) What are the most discriminating features? ** Learner- and context-related features describe the interactions between students, teachers, and Learning Management Systems under multiple aspects. Based on the type of interaction, in [16] the features are classified as student-student, student-teacher, and student-content categories. The results achieved on the Moodle LMS data and presented in [17; 18] show that student-student and student-teacher interactions are relevant to predict the success rate of fully online courses, while student-content interactions are deemed as relevant to in-class lectures. The results reported in [19] confirm that learners' habits, social activities, and teamwork styles are relevant to identify the key factors influencing students performance. To deepen the analysis of the interactions between students and LMSs, in [20; 21] the authors analyze the data acquired from the Moodle LMS to discover which features (e.g., total time online, number of downloads, amount of communications with peers) are significantly correlated with the final grade. The results show that the total time online and the number of files viewed are the most discriminating features. In [22; 23], the authors identify a set of key performance factors influencing student performance in both K-12 and higher education environments. The goal of this work is different to those of all the aforesaid approaches. It proposes a methodology, based on associative classification, to accurately perform predictions and to interpret the results. By exploring the mined association rules, the most significant features can be automatically identified. Notice that since the presented methodology is general, it can be applied to an arbitrary feature set. **(B) From which time point can classifier predictions be deemed as reliable? ** To allow timely interventions, performance predictions should be performed as early as possible. The works presented in [7; 24; 25] address this specific issue under different viewpoints. As expected, the prediction accuracy increases when the examination session is approaching. However, for a relevant percentage of students the final grade appears to be strongly correlated with the result of the entry test [25]. Therefore, fruitful information about at-risk students is available very soon. The number of clicks to the online materials made in the week immediately before the course turns out to be another significant predictor [7]. Results of ongoing assessments or previous examination sessions are, as expected, strongly correlated with the grade as well [26]. Hence, they should be considered as soon as they become available. Even the presence of unlabeled data could contribute to the improvement of predictors' reliability [27]. In [3], the authors analyze 17 blended learning courses using the Moodle LMS. The prediction models achieve high recall values (i.e., they identify most of the at-risk students) but low precision values (i.e., the number of false positives is fairly high). The main conclusion drawn by the authors is that it is very hard to find a comprehensive set of variables that can be used to consistently predict student performance across multiple courses.