Proposing an algorithm for selecting and evaluating the level of questions for Gifted Schools

Abstract

Generating tests from question banks using the elements extracted manually and randomly consumes a great deal of time and effort. The quality of the resulting tests is often inaccurate. The tests that have been created may not fully correspond to the requirements that were previously formulated, so this study focused on innovative ways to enhance this. The process by optimizing execution time and generating results closely meeting the requirements for extraction, this paper proposes a Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in configuring the Hybrid PSP-GA algorithm to generate multiple-choice tests based on the difficulty levels of the questions and the extracted evaluations, shown Experimental results that PSO speeds up the extraction process, improves the quality of tests, and is more efficient in most criteria such as execution time, search area and contrast, and that the experiments and analyses with the proposed mutation operator have proven the success of the GA method with a very high rate, the research is distinguished by a hybrid PSO-GA technique to solve optimization problems, Further improving the balance between exploration and exploitation capabilities through the integration of genetic factors, Designed an electronic system for selecting and evaluating For questions and their level of difficulty, based on the hybrid algorithm.