Implementing an efficient preference-based academic advising system

Lam, S.S.; Choi, Samuel P. M.

doi:10.1504/ijams.2013.057110

Cited by 4 publications

(2 citation statements)

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“…However, the lack of knowledge about the modules and the lecturers, coupled with the contradictory advice they receive from their colleagues and the complexity of the academic plan of their programs may affect the students' ability to select the right choice. This creates a need for professional advice as taking a wrong choice may lead to unwanted situations such as failing the module, or more seriously, academic warning, program withdrawal or even leaving the university [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent Recommender Systems (RSs) are widely used in different application areas including online shopping, movies, social networking, and others. However, they are less common in education and this area is still under development [1][2][3]. RSs can provide the students with personalized recommendations on suitable modules based on the student's academic history.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-based Adaptive Framework for Module Advising Expert System

Alhabashneh¹

2021

AETiC

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In the enrolment process, selecting the right module and lecturer is very important for students. The wrong choice may put them in a situation where they may fail the module. This could lead to a more complicated situation, such as receiving an academic warning, being de-graded, as well as withdrawn from the program or the university. However, module advising is time-consuming and requires knowledge of the university legislation, program requirements, modules available, lecturers, modules, and the student's case. Therefore, the creation of effective and efficient systems and tools to support the process is highly needed. This paper discusses the development of a fuzzy-based framework for the expert recommender system for module advising. The proposed framework builds three main spaces which are: student-space (SS), module-space (MS), and lecturer-space (LS). These spaces are used to estimate the risk level associated with each student, module, and lecturer. The framework then associates each abnormal student case in the students’ grade history with the estimated risk level in the SS, MS, and LS involved in that particular case. The fuzzy-based association-rule learning is then used to extract the dominant rules that classify the consequent situation for each eligible module if it is to be taken by the student for a specific semester. The proposed framework was developed and tested using real-life university data which included student enrollment records and student grade records. A five-fold cross-validation process was used for testing and validating the classifying accuracy of the fuzzy rule base. The fuzzy rule base achieved a 92% accuracy level in classifying the risk level for enrolling on a specific module for a specific student case. However, the average classifying accuracy achieved was 89.2% which is acceptable for this problem domain as it involves human behavior modeling and decision making.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%