Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

Higher education decision making and decision support systems

The authors illustrate several issues in decision support and decision support systems (DSS), state of the art research in these fields, and also their own studies in designing a higher education DSS. The final section contains our contribution in outlining the modules of the DSS, involving the present systems and databases of FSEGA and UBB, results and activities belonging to FSEGA students, teaching and research staff, to assist decisions for all the actors implicated in the processes, in various specific situations.decision support, decision support systems (DSS), higher education institutions, Information and Communication Technologies (ICT)

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5

MODEL DRIVEN DEVELOPMENT OF ONLINE BANKING SYSTEMS

In case of online applications the cycle of software development varies from the routine. The online environment, the variety of users, the treatability of the mass of information created by them, the reusability and the accessibility from different devices are all factors of these systems complexity. The use of model drive approach brings several advantages that ease up the development process. Working prototypes that simplify client relationship and serve as the base of model tests can be easily made from models describing the system. These systems make possible for the banks clients to make their desired actions from anywhere. The user has the possibility of accessing information or making transactions.MDA, UML, Online Banking, Class diagram, Platform Independent Model

Higher education decision making and decision support systems

The authors illustrate several issues in decision support and decision support systems (DSS), state of the art research in these fields, and also their own studies in designing a higher education DSS. The final section contains our contribution in outlining the modules of the DSS, involving the present systems and databases of FSEGA and UBB, results and activities belonging to FSEGA students, teaching and research staff, to assist decisions for all the actors implicated in the processes, in various specific situations

Higher education decision making and decision support systems

The authors illustrate several issues in decision support and decision support systems (DSS), state of the art research in these fields, and also their own studies in designing a higher education DSS. The final section contains our contribution in outlining the modules of the DSS, involving the present systems and databases of FSEGA and UBB, results and activities belonging to FSEGA students, teaching and research staff, to assist decisions for all the actors implicated in the processes, in various specific situations

Determining IT Student Profile Using Data Mining and Social Network Analysis

To become higher competitive a university needs to develop a viable students’ absorption strategy on the labor market. A key to the successful development of such a strategy rests to synchronize jobs descriptions with profiles and behavior of IT students. In order to generate this synchronization, it is essential to identify a way to improve university curricula, learning and teaching process based on the students’ profile and on the labor market needs. In this manner, universities could offer IT companies information about their IT students’ profile and behavior. Our paper proposes a data mining and social network analysis to examine IT students’ skills and behavior in order to generate their actual profile. The results contribute to the development of knowledge concerning the IT graduates’ profile and based on this, a solution that might match the university curricula with the labor market requirements. Finally, the results attempt to provide IT companies with information with the aim of better understanding the IT students’ profile and to create a realistic description of the job in the recruitment software on the digital market

The Role of Emotional Intelligence in Labour Market Orientation and Career Development

Our study is part of an ample research project on the students of a University of the Central West part of Romania. For this particular paper, our aim was to underline the correlation between the level of school training and a series of skills of the young people who took part in the study (such as labour market orientation, interaction with the managers of the employer company and with the company owner, in order to find an advantageous job, to undergo advanced training and to develop their career), meaning the connection between the global intelligence (useful for the school training) and the emotional intelligence. We took into consideration the research in the specialised literature and our previous studies as well as an analysis based on a sociological survey. The survey was conducted on a sample of 518 students, most of them in their second year of Master Degree studies. The questionnaire that contained 38 questions was conceived by the authors, the data was processed with an SPSS software and the results obtained are presented in tables and explained throughout the paper. The conclusions consist of the authors’ considerations regarding the existent connection (in the presented case) between the general, global intelligence of the subjects, manifested through their school results and their emotional intelligence, as well as a portrait of the representative person for the studied community

Integrated Management Solution for a Sustainable SME—Selection Proposal Using AHP

In the conditions of the pandemic crisis, implementing an enterprise resource planning (ERP) system with a sustainability component represents a crucial investment for a small and medium enterprise (SME) but critical for the organization, if it is not the matching solution. A comprehensive framework for selecting the S–ERP system was elaborated, including a set of relevant criteria for an SME to draw and assess the selection of an ERP system, considering that the factors extracted as well as the importance in the proposed model have been debated for years in the available literature. A methodology based on the brainstorming and questionnaire techniques was proposed in establishing the selection criteria, and the AHP decision analysis method was used for evaluating the weight of the criteria, all these in order to provide a model for ERP selection. This model was tested on a set of numerical, hypothetical, and applied data of the Romanian context. The use of the recommended model shows that it can be applied to improve decisions and decrease the time interval required for S–ERP selection. The results also show that AHP can fulfill the S–ERP selection objective for SMEs and the decisive factors that affect decision–making processes in a systematic way