Business Intelligence Tools In Information Mining

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Business Intelligence Tools In Information Mining

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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 teacher’s experience. This change includes the development of quality education with a focus 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 it into the knowledge necessary to make decisions that improve learning outcomes. The information systems managed by the universities store large amounts of data on the socio-economic and academic variables of the students. In the university setting, this data is generally not used to generate knowledge about their 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 data mining techniques 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, where 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 technology (ICT) is included in all community activities. The universities are not far behind, and include ICT 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 widespread use of ICT for academic management is the learning management system (LMS) [2] which supports online interaction between teachers and students. However, there are scenarios where specific support from ICT is needed to solve common problems with a focus on learning. These scenarios allow ICTs to adopt new models and pedagogical 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 to their customers.

Personal service can be introduced to educational environments where the process is similar to that used at business level, but the goal in education is to improve the methods or activities that generate learning in students [3]. Learning environments are mainly based on a variety of interactive services and delivery services. Personalized learning recommendation systems can provide learning recommendations to students based on their needs [4, 5]. Businesses use data analytics architectures whose results help them make decisions about their business. These architectures are called business intelligence (BI); their ability to extract data from various sources, process them and turn them into knowledge is a solution that can also be included in the educational management of a university [6].

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As a precedent, it is important to consider 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 [7]. In the same way, previous works [8, 9] have carried out an analysis of desertion rates in terms of models and statistical tools using financial and academic variables, and segmented the analysis into whether students have enrolled or not in the next semester. This formula is perfectly valid; but it leaves aside the reasons that determined why students left their studies. In contrast, our proposal is differentiated by its ability to analyze the data from students’ academic activities and focus on the learning problems they present. This analysis helps to make decisions in educational management and improvement of the learning methods established by teachers [10].

In this work, three research questions are proposed that help align the concepts and processes in their design; in addition, they seek to determine the current situation of the environment in which 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 methodology for using academic data mining. This work extracts data from various academic sources, processes them and allows us to identify, through data mining algorithms, the strengths and weaknesses of each individual student. Once the results are obtained, knowledge is generated about the learning process of each student so that appropriate decisions can be made to improve the way the student learns.

This paper is organized as follows: Section 2 reviews the existing work related to the purpose of this study; Section 3 describes the components and processes of the proposed framework; Section 4 applies the method to a case study, to test the feasibility of the method; and Section 5 presents the conclusions.

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The literature review presented follows the guidelines published in the systematic literature review methodology proposed by Kitchenham et al. [11] and by Petersen et al. [12]. Kitchenham et al. describe how the results of a literature review in software engineering should be planned, carried out and presented; Petersen et al. provide guidance on how to carry out a thorough 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 discussion 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 aim of this literature review is to try to learn how they do it, and which methods and techniques they use. The search string “business intelligence AND education” was selected, and only documents published within the last 5 years were considered.

The searches were made based on the information provided 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 search 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 was analyzed according to the guidelines that must are fulfilled for the design of a BI. In the next step, we reviewed the works that included data mining applications. This filter was used because a BI platform integrates data mining algorithms that generate knowledge about the analyzed data. These articles then went to the classification stage and were finally integrated as valid literature for the study. Works that did not meet the conditions defined in the selection were automatically excluded from the process.

The works were classified according to type, contribution and scope of the research. The articles were classified by type of research based on the processes proposed in [11] and [13], prioritizing articles where the proposed solution to a problem is innovative or a significant extension of an existing technique. Obtaining the results of the review began with the location of the primary studies, then progressed to the extraction of the data and, finally, the categorization and resulting scheme.

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