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Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms
Published in Walter de Gruyter GmbH
2019
Volume: 19
   
Issue: 3
Pages: 25 - 44
Abstract
AbstractChoice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.
About the journal
JournalData powered by TypesetCybernetics and Information Technologies
PublisherData powered by TypesetWalter de Gruyter GmbH
ISSN13119702
Open AccessNo