Intelligent Decision Support System for guiding Students in Field Selection

Education and training play significant roles in the economy and social welfare of all countries. One of the primary objectives of high school education is the academic guidance of students, as the correct selection of a field of study leads to appropriate job choices. Academic guidance methods within the education system of any country are among the most crucial issues in education. Efficient academic guidance not only impacts students' future careers and satisfaction but also helps create a balance in the distribution of labor based on societal needs. Conversely, a failure to properly select a discipline increases the likelihood of school failure and can lead to job dissatisfaction in the future. Various factors influence the selection of fields of study and, ultimately, job choices. The multiplicity of factors affecting success in field selection complicates the decision-making process. Although advisers are available to assist students in selecting their fields of study, human cognitive limitations in processing and memorizing information prevent these consultants from considering all relevant factors simultaneously. Therefore, there is a need for a system that can account for all factors and aspects of the students' guidance program, thereby reducing the decline in educational quality. Given the researchers' emphasis on the necessity of utilizing intelligent systems in the student guidance process and the ability of these systems to enhance efficiency, this study has designed a decision support system based on a comprehensive set of criteria influencing field selection to assist advisers and students. Research Questions: What criteria are most important in choosing a student's field of study, and which will have a greater effect on the correct choice of field? 1. What is the effect of each criterion on the selection of a field of study? 2. What rules and regulations should be established for choosing a successful major in each field of study to provide a support system for decision-making? Method To address these questions, we first identified the effective variables for each discipline by examining the criteria influencing field selection through a review of the literature and interviews with experts. These criteria were categorized into three general categories: family-related factors, individual student factors, and educational variables. Family factors included parents' education levels, family social and economic status, and parental priorities regarding discipline selection. Individual student factors encompassed physical abilities, mental capacity for continuing education, and personal interests. Educational factors included the influence of educated individuals and educational advisers. After validating the criteria with experts, the final criteria were ranked using five different multi-criteria decision-making methods SAW, WASPAS, ARAS, CODAS, and MABAC. The best approach was then selected for use in the decision tree. In the next step, data were collected from twelfthgrade students via a questionnaire. The data were analyzed using RapidMiner software and applied to the decision tree, resulting in the identification of optimal rules for choosing fields of study.

To evaluate the accuracy of the decision tree rules across all disciplines, test data were also entered into the decision tree. Out of 40 test cases, only two did not yield the same output, with the discrepancy being a difference of only one code. With a correct prediction rate of 95% for the experimental data, this system demonstrates high efficiency and accuracy and can be utilized in schools as a support tool for advisers in guiding students.

The current study aims to comprehensively identify, integrate, and weight the criteria affecting academic guidance while providing a decision support system based on these criteria. The designed decision support system employs comprehensive criteria and sub-criteria for academic guidance, specifically tailored for each high school field. Another advantage of the system is its ability to prioritize (weight) the criteria and sub-criteria affecting academic guidance. While some other studies have designed decision support systems for academic guidance focused on limited high school fields of study, the system developed in this study encompasses all theoretical and practical high school fields. The rules derived from the decision tree have been used to create the model base of this decision support system. In addition to recommending the best choice for each student, this system can also predict their performance if they choose other fields. If any changes occur in the procedures of the educational system, it will be necessary to redesign the system; however, it seems that the structure of the system does not require alteration. Modifications can be achieved by simply adding or subtracting information received from users or adjusting certain rules.