# Analysis of Student Academic Performance Using Human-in-the-Loop Cyber-Physical Systems
## 1 Introduction  
The development stage of a country is directly related to the level of education of its habitants and how this contributes to its socioeconomic and technological progress. Therefore, one of the main objectives that should concern governments is the availability of high-quality educational institutions, something directly related to students achieving a high academic performance. Within this context, UNESCO presents goal 4.3 as follows [1]: \"By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university\", emphasizing the need to achieve a high quality of education and technical assistance to their member states. In fact, technology can help in processes related to the improvement of education quality. As a result, the interest in data mining techniques has increased in the educational field and has led to the creation of a new research area called Educational Data Mining (EDM). The objective of this area is the analysis of educational data, through which the data are turned into useful information, allowing researchers to identify possible solutions to different challenges that arise in the educational field [2]. In addition, we can take advantage of technological advances such as the Internet of Things (IoT) by using them in the process of improving students' academic performance. This can be achieved by developing and deploying student-centric applications based on those technologies. In the last years, mobile human-centric applications have been considered as a good tool to gather patterns of student behavior [3, 4, 5]. Some studies have focused on obtaining information about student behavior by using a mobile phone, such as the StudentLife study [6], that was developed by researchers at Dartmouth College. This system allows information to be collected in order to analyze the impact of a student's daily workload on their life and on their academic performance. The data were obtained from the GPS, accelerometer, microphone, light sensor, and Bluetooth and WiFi signals. The authors determined that student behavior is dependent on the period of the academic year (i.e., the behavior at the beginning differs from the behavior at the end of the school period). To obtain the students' location, the GPS was used outdoors, and WiFi signals were used indoors. This work was complemented with a new study presented in [7], where, based on the information already collected in [6], they obtained patterns of behavior related to students' activity and sociability. Later on, these patterns were correlated with the Grade Point Average (GPA) of the students. On the basis of the results obtained, the authors proposed a prediction model for the students' academic performance. However, these studies do not offer an IoT platform that includes feedback to each student to encourage them to modify their behavior. This is one of the aspects where the present work differs, because we propose a mechanism to close the loop, making the human being an active part of the process. On the other hand, the work SmartGPA [8], based on data collected with StudentLife, offers methods to automatically infer study and social behaviors. Additionally, they propose a simple model to predict the cumulative GPA, opening up a new way to improve academic performance. In this approach, some studies are related to prediction techniques of student performance, which use the historical data of students as demographic information [9, 10], i.e., socioeconomic and academic data [11], extracted from university database. The attributes for the prediction of academic performance were academic and social integration, emotional skills [12], average of grades, evaluations, final exam grades, class attendance, task delivery, and participation rate [13]. In the same direction, [2] identifies the following factors associated with the students' performance: availability of computer equipment and Internet facilities at home, gender, attendance, and marks.