Abstract
Is it possible to analyze student academic performance using Human-in-the-Loop Cyber-Physical Systems (HiLCPS) and offering personalized learning methodologies? Taking advantage of the Internet of Things (IoT) and mobile phone sensors, this article presents a system that can be used to adapt pedagogical methodologies and to improve academic performance. Thus, in this domain, the present work shows a system capable of analyzing student behavior and the correlation with their academic performance. Our system is composed of an IoT application named ISABELA and a set of open-source technologies provided by the FIWARE Project. The analysis of student performance was done through the collection of data, during 30 days, from a group of Ecuadorian university students at “Escuela Politécnica Nacional” in Quito, Ecuador. Data gathering was carried out during the first period of classes using the students’ smartphones. In this analysis, we found a significant correlation between the students’ lifestyle and their academic performance according to certain parameters, such as the time spent on the university campus, the students’ sociability, and physical activity, etc.
Highlights
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
This work presents a study on the use of ISABELA, a developed student-centric application, which analyzes student behavior and its relationship with their academic performance
Application, which analyzes student behavior and its relationship with their academic performance. This sensing system is a nonintrusive platform to obtain data, as it passively captures the data without. This sensing system is a nonintrusive platform to obtain data, as it passively captures the data requiring any input from the user
Summary
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. The work SmartGPA [8], based on data collected with StudentLife, offers methods to automatically infer study and social behaviors They propose a simple model to predict the cumulative GPA, opening up a new way to improve 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 Telecom 2020, 1, FOR PEER REVIEW “Escuela Politécnica Nacional” of Quito, Ecuador This was done during the first period of classes, In addition to the work presented in this paper, two additional papers have been published, using our application on their Android mobile phones.
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