A Genetic Algorithm-based Group Formation to Assign Student with Academic Advisor: A Study on User Acceptance using UTAUT - Ying, Tan Xue; Kassim, Azleena Mohd; Abdullah, Nor Athiyah (Society of Visual Informatics, 2023)
Group formation to assign students with academic advisors based on student demography can be exhaustive as various possibilities and combinations can be formed. Hence, this paper proposed a genetic algorithm-based approach to automate group formation based on student demography to assign students to their academic advisors. The genetic algorithm (GA) will optimize the group formation of students with a balanced number of nationalities, races, and genders. Also, this paper examines the user acceptance of the proposed genetic algorithm-based application to automate group formation using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The survey aims to study the impact of independent and moderating variables on dependent variables. The result proved that all the independent variables, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC), have a positive impact on the dependent variable, Behavioral Intention (BI). In contrast, the moderating variable Experience (EX) and Voluntariness of Use (VU) have a negative impact on Behavioral Intention (BI). Thus, this paper concludes that the proposed application can increase the performance and efficiency of group formation and automatically assign students to academic advisors. However, respondents are reluctant and not ready to use the system. Thus, training and workshops can be conducted to introduce and train the users to utilize the system. Future works can be done where the application of the proposed genetic algorithm-based system can be further expanded to different academic purposes such as team formation for group assignment and team member selection for competition.