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By William Villegas-Ch William Villegas-Ch Scilit Preprints.org Google Scholar 1, * , Xavier Palacios-Pacheco Xavier Palacios-Pacheco Scilit Preprints.org Google Scholar 2 and Sergio Luján-Mora Sergio Luján-Mora Scilit Preprints.org Google Scholar 3
Received: 28 May 2020 / Revised: 12 July 2020 / Accepted: 13 July 2020 / Published: 17 July 2020
Currently, universities are forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on student learning. These factors have forced universities to look for a solution that allows them to extract data from various information systems and convert them into the knowledge needed to make decisions that improve learning outcomes. The information systems managed by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, this data is generally not used to generate knowledge about its students, unlike in the business field, where the data is intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within a business intelligence architecture to make decisions about variables that can influence the development of learning. To test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the various information systems of a university.
Currently, the use of information and communication technologies (ICTs) is included in all activities of society. Universities are not far behind, incorporating ICTs in most of their processes. These processes integrate the administrative management on which the existence of the universities depends or use them as support for academic management [1]. The most extensive use of ICTs for academic management is the learning management system (LMS) [2] which supports online interaction between teachers and students. However, there are scenarios in which specific support by ICTs is needed to solve common problems aimed at learning. These scenarios allow ICTs to apply new models and educational methods in student learning. A guide to this can be the personalization that companies have achieved with their customers through data analysis models that allow managers, directors and analysts to discover trends and improve the services and products they offer their customers.
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Personal service can be introduced in educational environments where the process is similar to that applied at the business level, but the goal in education is to improve the methods or activities that generate learning in students [3]. Learning environments are primarily based on a range of interactive and delivery services. Personalized learning recommendation systems can provide learning recommendations to students based on their needs [4, 5]. Companies use data analysis architectures whose results help them make decisions about their business. These architectures are called business intelligence (BI); their ability to retrieve data from various sources, process and convert it into knowledge is a solution that can also be included in the educational management of a university [6].
As a precedent, it is important to consider that several universities use a BI platform with an administrative or operational focus, which helps them to make decisions in the financial management of the institution [7]. In the same way, previous works [8, 9] have carried out an analysis of desertion rates considering models and statistical tools with the use of economic and academic variables, segmenting the analysis in whether students have enrolled or not in the next semester. This formula is completely valid; however, it leaves aside the causes that determine why students leave their studies. In contrast, our proposal is differentiated by its ability to analyze the data of students’ academic activities and focus on the learning problems they present. This analysis helps to make decisions in educational management and the improvement of the teaching methods established by teachers [10].
In this work, three research questions are proposed that help to establish the concepts and processes in its design; in addition, they try to determine the current situation of the environment where this work is carried out:
To answer each of these questions, this work includes the description of a BI framework that bases its design on a detailed review of the previous works, the Unified Modeling Language (UML) diagram and a complete method for applying academic data mining This work extracts data from various academic sources, processes them and allows us to identify each student’s strengths and weaknesses through data mining algorithms. Once the results are obtained, knowledge is generated about each student’s learning process, allowing appropriate decisions to be made to improve the way the student learns.
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This article is organized as follows: Section 2 reviews the existing work regarding the purpose of this study; Section 3 describes the components and processes of the proposed framework; Article 4 applies the method to a case study, to test the feasibility of the method; and section 5 presents the conclusions.
The presented literature review follows the guidelines published in the systematic literature review methodology proposed by Kitchenham et al. [11] and Petersen et al. [12]. Kitchenham et al. describe how the results of a literature review in software engineering should be planned, implemented and presented; Peterson et al. provide a guide on how to conduct a rigorous review of the literature and follow a systematic procedure. For our literature review, the works were grouped according to the type of tool, model, paradigm or discourse they use in their own analysis of educational data. For this type of classification, it was necessary to know the status of scientific work in learning environments that include the use of BI techniques that improve education. The purpose of this literature review is to try to learn how they do it, and what methods and techniques they use. The search string “business intelligence AND education” was selected, and only documents published in the last 5 years were considered.
The searches were made based on the information given in the title, abstract and keywords of the works. From the selected works, a detailed reading of the introduction and conclusions was carried out, in order to filter out the unrelated publications.
Figure 1 represents the flow chart of the bibliography selection process; the first phase collects the articles from the online databases. The string terms used to search for articles in online databases, such as Springer Link, Web of Science, ACM Digital Library, IEEE Digital Library (Xplore) and Scopus, can be found in Table 1. In the selection process, each of the articles analyzed according to the guidelines that must be met for the design of a BI. In the next stage, we reviewed the works that included data mining applications. This filter was applied because a BI platform integrates data mining algorithms that generate knowledge from the analyzed data. These articles then proceeded to the classification stage and were finally integrated as valid literature for the study. Works that do not meet the conditions defined in the selection were automatically excluded from the process.
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The works were classified according to the type, contribution and extent of the research. The articles were classified according to the type of research based on the processes proposed in [11] and [13], prioritizing articles in which the proposed solution to a problem is innovative or a significant extension of an existing technique. Getting the results
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