Factors influencing Omani students' selection of higher education institutions: an emphasis on undergraduate and postgraduate students

Tanas, Islam Nazier; Echchabi, Abdelghani; Hajri, Salim Al

doi:10.1504/ijeed.2019.10022687

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The factors necessary to make informed decisions about further education include location, reputation, employment opportunities, and prospects for graduates [24]. The website's publicity, an essential aspect of promotion, showcases the university's reputation [25]. Students are primarily interested in the infrastructure, reputation, teaching and learning methods, and other aspects of the university's location [26].…”

Section: Literature Reviewmentioning

confidence: 99%

An Information Service Platform for Decision Support in Academic Admissions Using Data Fabrics and Artificial Intelligence

Tasatanattakool,

Nongnuch,

Wannapiroon

et al. 2023

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

This paper presents the architecture of an information service platform for decision support in academic admissions, utilizing data fabrics and artificial intelligence. The factors affecting students’ further education can be classified into four main types: (1) the course, (2) image, (3) personal reasoning of the student, and (4) public relations. The process of providing information for decision support in academic admissions can be divided into six stages: (1) collecting information, (2) matching study guidance, (3) recommending appropriate education, (4) confirming information, (5) assessing student admissions, and (6) providing feedback. Data fabric is an increasingly popular technology application for data management. The data fabric architecture consists of six layers: (1) an augmented data catalog, (2) a knowledge graph enriched with semantics, (3) metadata activation, (4) a recommendation engine for active metadata, (5) data preparation and integration, and (6) orchestration and data operations. A smart decision support system (DSS) technology is used to assist in decision-making. The results showed that this architecture has an excellent level of suitability (mean = 4.56, standard deviation = 0.35). It can be applied to a university to help it become a digital university and align with its mission.

show abstract

Section: Literature Reviewmentioning

confidence: 99%