The power of genetic algorithm in automated assessment of school readiness

Date

2010-01-01

Author

Suleiman, Iyad
Salman, Tamer
Gao, Shang
Polat, Faruk
Arslan, Maha
Alhajj, Reda
Ridley, Mick

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. We argue that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the kids to be seriously involved in the test.We have integrated a modified genetic algorithm into a computerized assessment tool for school readiness. Our goal is to create a computerized assessment tool that can learn the user's skill and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be chosen for the user according to his/her skill and status. In this paper, we describe the modified GA and the learning procedure. We integrate a penalizing system into the GA and a fitness heuristic for best choice selection. We present two methods for learning, a memory system and a no-memory system. Furthermore, we present several methods for the improvement of the speed of learning. © 2010 IEEE.

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77958001027&origin=inward
https://hdl.handle.net/11511/106844

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Department of Computer Engineering, Conference / Seminar