Nand, Ravneil and Sharma, Anuraganand and Reddy, Karuna G. (2018) Skill-Based Group Allocation of Students for Project-Based Learning Courses Using Genetic Algorithm: Weightless Penalty Model. [Conference Proceedings]
Full text not available from this repository.Abstract
Project-based learning (PBL) is an important component of the practical based assessment of software engineering courses. The success of PBL relies on team composition where all necessary skills to execute the project is needed. Conventionally, facilitators assign the students to the group randomly which results in biased groups where all the necessary skills to complete the project lacks in some of the groups. Most computational tools solve the group assignment problem (GAP) by assigning students to relevant groups based on some general criterion. However, there is a need for a system which allows taking skill preference as a parameter in a limited or unevenly distributed skill set. The system needs to have more or less same strength with the presence of all the skills required to complete the project. In this paper, a method is proposed that uses the canonical genetic algorithm to generate evenly balanced groups by minimizing the intergroup difference. We have employed penalty function to rank the skills and incur a penalty for the non-presence of required skills for proof of concept. Due to unavailability of benchmark datasets, we have used the real data of software engineering courses of our university where good results have been observed.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | computer aided instruction;computer science education;educational courses;genetic algorithms;software engineering;evenly balanced groups;weightless penalty function;software engineering courses;group allocation;project-based learning courses;genetic algorithm;weightless penalty model;PBL;essential counterpart;successful outcome;group assignment problem students;appropriate groups;general criterions;specific criterions;account specific constraints;skill preference;unevenly distributed skill;limited distributed skill;Optimization;Genetic algorithms;Education;Resource management;Australia;Software engineering;Minimization;project-based learning;penalty function;genetic algorithm;group assignment problem;skill set;weightless |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences |
| Depositing User: | Anuraganand Sharma |
| Date Deposited: | 26 Jun 2019 03:50 |
| Last Modified: | 09 Jul 2020 00:17 |
| URI: | https://repository.usp.ac.fj/id/eprint/11613 |
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