# Analysis of Student Academic Performance Using Human-in-the-Loop Cyber-Physical Systems
## 1 Introduction
In the same way, [14] offers a review of data mining techniques employed to infer the students' academic performance and to improve the effectiveness and efficiency of their performance. They used diverse attributes, such as Cumulative Grade Point Average (CGPA), internal evaluation related (tests, attendance, laboratory works, and quizzes), demographic aspects (gender, ages, family, and disabilities), and external evaluation related (final grade of a particular subject). According to this work, the most used external attributes are extracurricular activities, school background, and social network interaction. These works use different classifiers such as decision tree (J-48), random tree, neuronal networks, native Bayes, Nearest Neighbor (K-NN), and Support Vector Machine (SVM). On the basis of the literature related to academic performance prediction, we obtained a good idea about the attributes that could be used in our work as the GPA and CGPA. However, in all the works analyzed we identified the lack of a feedback mechanism, i.e., the authors do not propose any mechanism to actively influence the human to change behaviors, thus closing the loop. In this article, we present a new approach to collect data using a mobile phone as a sensing system as well as information from social networks. This allows us to infer the student behavior using a student-centric mobile application with the goal of obtaining the correlation between the student behavior and their academic performance. To analyze the correlation between the student behavior and the academic performance, we collected, in a continuous way over 30 days, sensing data from 30 Ecuadorian students at \"Escuela Politecnica Nacional\" of Quito, Ecuador. This was done during the first period of classes, using our application on their Android mobile phones. In addition to the work presented in this paper, two additional papers have been published, namely, \"A Human-in-the-Loop Cyber-Physical Approach for Students Performance Assessment\" [15] and \"ISABELA - A Socially-Aware Human-in-the-Loop Advisor System\" [16]. While the first paper presents implementation details of the physical system and different trial results from the ones presented in both this paper and the second, the second presents a more detailed exploration of the online social network data, as well as the implementation of three different machine learning mechanisms for sleep detection, sleep quality, and sociability detection. In addition, the previous papers used only a mid-term exam as the validation of student's performance. In contrast, in this paper, we present a more in-depth study of the student's performance by leveraging the student's historical academic data. The structure of the paper is as follows: In Section 2, we describe the applications that were developed (the Android app and the backend application) and used to collect the data. Then, the methodology followed is detailed in Section 3. Section 4 presents the results obtained and discusses them. Finally, the conclusions and future work are presented in Section 5.