School Performance Analysis From a Scholastic Learning Process

School Performance Analysis From a Scholastic Learning Process

Judit Lacomba Masmiquel (La Salle Open University, Andorra), August Climent Ferrer (Universitat Ramon Llull, Spain) and David Fonseca (La Salle Universitat Ramon Llull, Spain)
DOI: 10.4018/978-1-5225-2584-4.ch026
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Abstract

In this study, the authors describe the design and use evaluation of a system that allows the academic behavior analysis of high school students using a data model based on business intelligence techniques. Based on the system data analysis information can be extracted and the right decisions taken in order to help and improve the student academic performance. From the design and implementation of the model, the relationship between the study habits of students and their academic performance is studied. More specifically, it aims to validate the initial hypothesis that there is a relationship between study habits of students and their academic performance. Based on the analysis it is concluded that the initial hypothesis is true. The proposed model also allows the extraction of information to other levels, where its primary objective is the application to improve the academic performance of students or to prevent situations of academic failure.
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2. Background And State Of The Art

There are numerous examples from recent years of the incorporation of all types of applications and systems into classrooms at all educational levels to improve teaching, especially to improve student motivation. In preschool and early childhood education, the use of digital chalkboards and very basic web applications are enabling new ways to teach subjects such as math, languages, and science (Beauchamp & Parkinson, 2008; Freeman, Eddy, McDonough, Smith, Okoroafor, Jordt & Wenderoth, 2014). In primary school, the increasingly complex use of computers and applications, which recently has even included the programming of robots, are innovations that directly impact the attention and comprehension levels of students, where the technological and social profiles of the student begin to illuminate the final answer (Volman, Eck, Heemskerk & Kuiper, 2005; Petre & Price, 2004). Beginning in secondary school, there is a challenge to incorporate mobile devices belonging to students, such as smartphones and tablets, into educational use through collaborative practices (and even gaming methods) that complement their social use (Leask and Pachler, 2013).

On the other hand, is easy to find multiple studies that analyze academic performance and the students learning process. These are based on different factors, personal variables and environment variables which are classified as external and internal factors (Mella y Ortiz, 1999). These factors could be personal and family-related which in some cases lead to school dropout in high school students (Díaz, 2003). This fact highlights the direct influence of the student’s parent’s academic level, the gender, the motivation and the social relationships as relevant external agents in the improvement of academic performance. Other studies (León, 2008) evaluate the students’ concentration and attention as an internal factor that demonstrates the positive proportionality between concentration and performance.

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