Business Intelligence Advancement Workshop 2008 R2

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Business Intelligence Advancement Workshop 2008 R2 – From NEDC to WLTP: The Effect on Energy Consumption, NEV Credits and PHEV Subsidy Policies in the Chinese Market

Measuring perceived service quality and its impact on golf course performance by facility types and user profile

Business Intelligence Advancement Workshop 2008 R2

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Editor’s Choice articles are based on recommendations from scientific journal editors around the world. The editors select a small number of recently published articles in the journal that they believe will be of particular interest to readers or important to the respective research area. The aim is to provide a snapshot of some of the most interesting work published in the various research areas of the journal.

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By William Villegas-Ch William Villegas-Ch Scilit Google Scholar 1, * , Xavier Palacios-Pacheco Xavier Palacios-Pacheco Scilit Google Scholar 2 and Sergio Luján-Mora Sergio Luján-Mora Scilit Google Scholar 3

Received: May 28, 2020 / Revised: July 12, 2020 / Accepted: July 13, 2020 / Published: July 17, 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 different information systems and transform it into the knowledge needed to make decisions that improve learning outcomes. Information systems managed by universities store a large volume of data on socioeconomic and academic variables of students. In the academic field, 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 business success stories can be replicated by universities through an analysis of educational data. This paper presents a method that combines models and data mining techniques within a business intelligence architecture to make decisions about variables that can influence learning development. 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 different information systems of a university.

Currently, the use of information and communication technologies (ICT) is included in all company activities. Universities are not far behind and include ICT in most of their processes. These processes integrate the administrative management on which the existence of universities depends or use them as support for academic management [1]. The most extensive 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 ICT support is needed to solve common learning-centered problems. These scenarios allow ICT to apply new educational models and methods to student learning. A guide to this can be the personalization that companies have achieved with their customers through data analysis models that allow managers, executives and analysts to discover trends and improve the services and products they provide to their customers.

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Personalized 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 mainly based on a series of interactive and delivery services. Personalized learning recommendation systems can provide students with learning recommendations based on their needs [4, 5]. Companies 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 different sources, process them and transform them 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 make decisions in the financial management of the institution [7]. In the same way, previous works [8, 9] carried out an analysis of dropout rates considering models and statistical tools with the use of economic and academic variables, segmenting the analysis according to whether students enrolled or not in the following semester. This formula is perfectly valid; however, it leaves out the causes of why students drop out. Instead, our proposal differentiates itself 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 to improve the learning methods established by teachers [10].

Three research questions are proposed in this paper that help align concepts and processes in their design; in addition, they aim to establish the current situation of the environment in which this activity is carried out:

To answer each of these questions, this paper includes the description of a BI framework that bases its design on a detailed review of previous works, the Unified Modeling Language (UML) diagram, and a comprehensive application method of academic data mining. This work extracts data from various academic sources, processes it and allows us to identify, through data mining algorithms, the strengths and weaknesses of each student. Once the results are obtained, knowledge is generated about each student’s learning process, allowing appropriate decisions to be made to improve how the student learns.

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This article 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.

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 software engineering literature review should be planned, executed, and presented; 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 own analysis of educational data. For this type of classification it was necessary to know the state of scientific work in learning environments that include the use of BI techniques that improve education. The objective 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 papers published in the last 5 years were considered.

Searches were made based on the information provided in the title, abstract and keywords of the papers. A detailed reading of the introduction and conclusions was performed among the selected papers to filter out unrelated publications.

Figure 1 represents the organizational chart of the bibliography selection process; the first phase 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 are found in Table 1. In the selection process, each of the articles a have been analyzed according to the guidelines that must be followed for the design of a BI. In the next step, we reviewed papers that included data mining applications. This filter was applied because a BI platform integrates data mining algorithms that generate knowledge about the analyzed data. These articles then went through 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.

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Papers were classified according to type, contribution and scope of research. Articles were classified according to research type 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. Getting results

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