Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

Abstract

Choice 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.

An optimization‐based decision support system for students' personalized long‐term course planning

“…Premalatha and Viswanathan [19] have used the course difficulty index in course sequence recommendation process. They proposed a new subset sum approximation problem (SSAP) which distributes courses to the semesters with near equal difficulty level.…”

“…• Three objective functions, Equations (15)(16)(17), are replaced with three goal-based constraints Equations (18)(19)(20). • An aggregated objective functions is formulated to minimize total weighted deviations from goals determined in the goal-based constraint Equation (25).…”

“…Equation (19) calculates the deviation from the mean complexity target. So, the target for this deviation is set to zero as the right-hand-side value.…”