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To measure the perceived quality of service and its effect on the performance of golf courses according to the types of facilities and user profile
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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 education 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 search for a solution that allows them to extract data from various information systems and transform them into knowledge needed to make decisions that improve learning outcomes. Information systems managed by universities store large amounts of data on students’ socio-economic and academic variables. In the university sector, this data is usually not used to create knowledge about its students, unlike the business sector, which is intensively analyzed in business intelligence to gain a competitive advantage. Universities can replicate these successes in business by analyzing educational data. This paper presents an approach that combines data mining models and techniques within a business intelligence architecture to make decisions about variables that influence learning development. In order to test the proposed method, a case study is presented, which is identified and classified according to the data created by students in various information systems of the university.
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 many of their processes. These processes integrate administrative management, on which the existence of universities depends, or use them as support for academic management . A 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 required to solve common learning-oriented problems. These scenarios allow ICT to use new models and educational methods in student learning. Leading this may be the personalization that companies achieve through data analysis models that allow companies and their customers, managers, executives and analysts to find trends and improve the services and products they offer their customers.
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Personalized services can be incorporated into the educational environment, where the process is similar to that used at the business level, but the goal in education is to improve the methods or activities that produce knowledge in students . Learning environments are primarily interactive and based on a range of delivery services. Personalized learning recommendation systems can provide learning recommendations to students based on their needs [4, 5]. Companies use a data analysis architecture whose results help them make decisions about their business. These architectures are called business intelligence (BI); their ability to receive information from various sources, process it and turn it into knowledge is a solution that can be included in the educational management of the university .
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 . Similarly, previous works [8, 9] analyzed whether students attended the next semester, considering models and statistical tools using economic and academic variables. This formula is perfectly valid; but ignores the reasons that determine why students drop out. Rather, our proposal differs in its ability to analyze students’ academic performance data and focus on the learning problems they present. This analysis helps to make decisions in the management of education and to improve the teaching methods prescribed by teachers .
In this work, three research questions are proposed that help to generate concepts and processes; In addition, they seek to determine the current state of the environment in which this work is carried out:
To answer each of these questions, this work includes a description of a BI framework that bases its design on a detailed review of previous work, a Unified Modeling Language (UML) diagram, and a comprehensive approach to academic data mining. This work takes data from a variety of academic sources, processes it, and uses data mining algorithms to identify each student’s strengths and weaknesses. Once the results are obtained, knowledge is created about each student’s learning process, which allows appropriate decisions to be made to improve the student’s learning path.
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This article is organized as follows: Section 2 reviews the work relevant to the purpose of this study; Section 3 describes the components and processes of the proposed framework; Chapter 4 applies the method to practical research, to verify the feasibility of the method; and Section 5 presents the conclusions.
A review of the literature provided by Kitchenham et al.  and Petersen et al. . Kitchenham et al. describe how the results of a literature review in software engineering should be planned, executed, and presented; Petersen et al. provide a guide to follow a rigorous literature review and systematic procedure. For our literature review, papers were grouped according to the type of tool, model, paradigm, or discussion they used to independently analyze the learning data. For this type of classification, it was necessary to know the state of research in educational areas involving the use of BI methods to improve education. The purpose of this literature review is to try to learn how they did it and what methods and techniques they used. The search string “Business intelligence AND education” is selected, and only documents published in the last 5 years are considered.
The search was conducted on the basis of information provided in the title, abstract and keywords. A detailed reading of the introduction and conclusions was performed to filter out irrelevant publications from the selected papers.
Figure 1 shows an outline 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 are listed in Table 1 . During the selection process, each of the articles is analyzed according to the guidelines that must be followed in BI design. In the next phase, we looked at cases involving data processing applications. This filter was used because the BI platform integrates data mining algorithms that generate knowledge from the analyzed data. These articles went through the classification stage and were finally combined as valid literature for the study. Cases that did not meet the conditions defined in the selection were automatically excluded from the process.
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The works are classified according to the type of research, contribution and scope. Articles  and  were classified according to the type of research based on the proposed processes, where priority was given to articles where the proposed solution to the problem was innovative or a significant extension of an existing approach. Get results
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