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Business Intelligence System Flowchart
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Submission received: 28 May 2020 / Revised: 12 July 2020 / Accepted: 13 July 2020 / Published: 17 July 2020
Currently, universities are forced to change educational paradigms, where knowledge is mainly based on the teacher’s experience. These changes include the development of quality education that focuses on student learning. These factors have led universities to look for a solution that allows them to obtain data from various information systems and turn it into knowledge needed to make decisions that improve learning outcomes. The information systems administered by universities store a large amount of data on the socio-economic and academic variables of students. In a university, this data is not usually used to create knowledge about its students, unlike in the business field, where data is intensively analyzed in business intelligence to gain competitive advantage. These entrepreneurial success stories can be replicated by universities by analyzing education data. This paper presents a method that combines data mining models and methods in 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 different university information systems.
Currently, the use of information and communication technologies (ICT) is included in all activities of society. Universities are not far behind and incorporate ICT in most of their processes. These processes integrate administrative management, on which the existence of universities depends, or use them as support for academic management . The most common use of ICT for academic management is the Learning Management System (LMS) , which supports online interaction between teachers and students. However, there are scenarios where specific ICT support is needed to solve common learning-oriented problems. These scenarios enable ICT to use 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 executives, managers and analysts to discover trends and improve the services and products they offer their customers.
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Personalized service can be implemented in educational environments, where the process is similar to that used at the business level, but the goal of education is to improve methods or activities that create learning in students . The learning environment is mainly based on various 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, the results of which help make business decisions. These architectures are called business intelligence (BI); their ability to obtain data from various sources, process and transform it into knowledge is a solution that can also be included in the management of higher education .
As a precedent, it is important to note that several universities use a BI platform with an administrative or operational focus, which helps them make decisions in the financial management of the institution . In the same way, previous works [8, 9] have analyzed dropout rates using models and statistical tools using economic and academic variables, segmenting the analysis on whether students enrolled in the next semester. This formula is perfectly valid; however, it leaves aside the reasons behind why students drop out. In contrast, our proposal differs in its ability to analyze the data of students’ academic activities and focus on the learning problems they raise. This analysis helps to make decisions in the management of education and in the improvement of teaching methods determined by teachers .
This paper proposes three research questions that help align concepts and processes in their development; in addition, they try to find out the current environmental situation in which this work is carried out:
To answer each of these questions, this work includes a description of a BI framework, the design of which is based on a detailed review of previous work, a Unified Modeling Language (UML) diagram, and a complete method for applying academic data mining. This work takes data from various academic sources, processes it and allows us to identify each student’s strengths and weaknesses using 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 paper is organized as follows: Section 2 reviews the existing work related to the objective of this study; Section 3 describes the components and processes of the proposed system; Section 4 applies the method to a case study to test the feasibility of the method; and Section 5 provides conclusions.
The presented literature review follows the guidelines published by Kitchenham et al.  and Petersen et al. . Kitchenham et al. describe how to plan, execute and present the results of a software engineering literature review; Petersen et al. provide guidance on how to conduct a rigorous literature review and follow a systematic procedure. For our literature review, papers were grouped by the type of tool, model, paradigm, or discussion they use in their analysis of educational data. This type of classification required knowing the status of scholarly work in learning environments involving the use of BI techniques that enhance education. The purpose of this literature review is to try to find out how they do it and what methods and techniques they use. The search string “business intelligence AND education” was selected, and only papers published within the last 5 years were considered.
Searches were conducted based on the information provided in the title, abstract, and keywords of the papers. From the selected works, a detailed reading of the introduction and conclusion was carried out to filter out unrelated publications.
Figure 1 shows a flowchart of the bibliography selection process; the first stage collects articles from 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. During the selection process, each of the articles was analyzed according to the guidelines for developing BI. In the next phase, we looked at jobs that included data mining applications. This filter was applied because the BI platform has integrated data mining algorithms that generate knowledge about the analyzed data. These articles then entered the classification stage and were finally integrated as valid research literature. Jobs that did not meet the selection criteria were automatically excluded from the process.
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Papers were classified by research type, contribution and scope. Articles were classified by research type based on the processes proposed by  and , prioritizing articles in which the proposed solution to the problem is an innovative or substantial extension.
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