<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

School of Computing Science & Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by...

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

Online veröffentlicht: 26. Sept. 2019

Seitenbereich: 25 - 44

Eingereicht: 11. Apr. 2019

Akzeptiert: 22. Aug. 2019

DOI: https://doi.org/10.2478/cait-2019-0024

Schlüsselwörter
Course sequence recommendation, Course credits, Course difficulty, Pre-requisite weight, Approximation Algorithm

© 2019 M. Premalatha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 26. Sept. 2019

Seitenbereich: 25 - 44

Eingereicht: 11. Apr. 2019

Akzeptiert: 22. Aug. 2019

DOI: https://doi.org/10.2478/cait-2019-0024

SchlüsselwörterCourse sequence recommendation, Course credits, Course difficulty, Pre-requisite weight, Approximation Algorithm

© 2019 M. Premalatha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Course sequence recommendation, Course credits, Course difficulty, Pre-requisite weight, Approximation Algorithm