Abstract—Exam is an evaluation tool to measure teaching and learning outcomes of educators and their learners respectively. Nowadays, an automated exam question set generator is a must have to reduce educator’s time on preparation of exam question set and increase the quality of exam question set. This paper proposes an Automated Exam Question Set Generator (AEQSG) using Utility Based Agent (UBA) and Learning Agent (LA). Furthermore, AEQSG applies Bloom Taxonomy (BT) scaling to automate Bloom’s Taxonomy (BT) difficulty level distribution and Genetic Algorithm (GA) to optimize the generation of exam question set and generate high quality exam question set that follow educational institution’s guide-lines. The purpose of utility based agent in AEQSG is to give the user an option to choose actions based on a user’s preference (utility) for each generation state. Meanwhile, the purpose of learning agent in AEQSG is to learn from its past exam results (past generation experiences).
Index Terms—Automated exam question generator, bloom’s taxonomy scaling, genetic algorithm, learning agent, utility based agent.
Tengku Nurulhuda Tengku Abd Rahim, Ma. Stella Tabora Domingo, Mohamed Farid Noor Batcha are with the MIMOS Berhad, Technology Park Malaysia, Kuala Lumpur 57000 Malaysia (e-mail: huda.rahim@mimos.my, stella.domingo@mimos.my, farid.batcha@mimos.my).
Zalilah Abd Aziz is with the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Boulder, 40450 Shah Alam, Selangor, Malaysia (e-mail: zalilah@tmsk.uitm.edu.my).
Cite: Tengku Nurulhuda Tengku Abd Rahim, Ma. Stella Tabora Domingo, Mohamed Farid Noor Batcha, and Zalilah Abd Aziz, "Automated Exam Question Set Generator Using Utility Based Agent and Learning Agent," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 164-169, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).