A conceptual framework of web-based academic advisory information system

Ishak, Isma; Lehat, Mohd Lezam

doi:10.1109/shuser.2012.6269012

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…Many HEI offer AAS designed to help students and their academic advisors recommend learning resources and review degree requirements and the student's progress towards the intended degree. However, existing advising support software tools can augment the student-advisor relationship, but cannot and should not replace in-person advising [6,8,16,18,58]. This is the point at which learning science, psychology, pedagogy, and computer science intersect.…”

Section: Discussionmentioning

confidence: 99%

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

2017

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This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Academic Advising Systems (AAS) and its impact on learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of AAS in generic educational strategic planning. The researchers examined the literature on experimental case studies conducted in the domain during the past ten years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Search terms identified 98 mature pieces of research work, but inclusion criteria limited the key studies to 43. The authors analyzed the research questions, methodology, and findings of these published papers and categorized them accordingly. The results have highlighted three distinct major directions of the AAS empirical research. This paper discusses the emerged added value of AAS research and highlights the significance of further implications. Finally, the authors set their thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.

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Section: Discussionmentioning

confidence: 99%

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

2017

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“…But, one of the most critical gaps of existing systems is the consideration of all available courses without considering students' interests or success in related courses, particularly in elective courses. For this reason, several studies were performed in electronic advising systems [3][4][5][6][7][8] Dash and Vaidhehi [9] developed an advising system for the students of the eighth standard of Indian Certificate of Secondary Education (ICSE) board to choose their electives using machine-learning algorithms to assist human advisers efficiently. It assisted Indian school students in analyzing their academic history and helped them to choose their electives wisely.…”

Section: Introductionmentioning

confidence: 99%

An intelligent and personalized course advising model for higher educational institutes

Tilahun

Şekeroğlu²

2020

SN Appl. Sci.

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In this study, we present an intelligent personalized course advising model by analyzing, selecting, and modifying effective course advising technologies. It overcomes the existing course advising systems by using common design for all Higher Educational Institutes and making the rule, regulations, curriculum, and grading system more flexible to change without affecting persistent academic data. The proposed model helps students by analyzing and suggesting appropriate courses according to their academic history and special interest via harmonized knowledge of different experts. To develop this model, analyze various requirements were gathered and explicitly analyzed from the course advisory systems and other related technologies, that were used in seven institutes. The proposed model contains seven components, in which they use association rule, rule-based expert system, and fundamental recommendation algorithms according to their suitability for collected requirements. We made more modifications and enhancements on those algorithms to achieve the purpose of each component. We used ISO-25010 quality model with the institutional rules and regulations of three departments to evaluate the proposed model. The obtained results showed that the proposed methodology of the model is an effective way to improve the efficiency of students course advisory systems. It also indicates that advisors and students can save more time through effective and efficient course suggestions during the course registration period.

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“…A typical course selection procedure starts with the framework of web-based academic advising information system [9].…”

Section: Introductionmentioning

confidence: 99%

EE Course Planning Software System

Laghari¹

2018

JSW

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Student course planning at the Department of Electrical Engineering (EE), UAE University, is important to ensure that a student fulfills the degree requirements of the university in an organized way, without encountering any redundant delays. Although the UAE University has a well-established Banner Registration System, however, course planning performed by the faculty and staff at department and college level sometimes is short of required expertise and qualification. Students have faced problems due to lack of a proper advising system as well as an inadequacy of seeking proper advice. An EE Course Planning Software System (EE-CPSS) package is devised in this respect. The software is developed by using the Python computer programming language. This system helps students in selecting from four to seven most suitable courses to register in the next semester. The outcome of the course selection is stored in a file for future reference.

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A conceptual framework of web-based academic advisory information system

Cited by 6 publications

References 4 publications

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

An intelligent and personalized course advising model for higher educational institutes

EE Course Planning Software System

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