TAGRA 2024

The success of education systems is directly related to the extent to which students successfully achieve the determined learning and program outcomes. This study addresses the design of a decision support system (DSS) for evaluating students' learning and program outcomes. The designed system enables students' success levels to be measured more precisely and curricula to be effectively optimized. This paper presents the design process, data collection methods, algorithms and implementation details of the system.
The education and training process aims to ensure that students gain academic, social and professional competencies. However, measuring the level of achievement of these goals and implementing output-based feedback mechanisms is a complex problem. At this point, it is important to process education data effectively and provide solutions that will guide decision makers.
The project aims to evaluate students based on both course learning outcomes and program outcomes based on the scores they receive from measurement and evaluation tools using Multi-Criteria Decision Making (MCDM) as a method and artificial intelligence-based approaches in the future. The following steps were followed in the system design:
• Data Collection: Data obtained from various dimensions such as students' academic achievement, attendance status, project and laboratory studies were analyzed.
• Output Analysis: Criteria were defined and prioritized to relate the determined program outcomes to each student's performance.
• Modeling and Algorithm Development: A multi-criteria decision-making system algorithm is being designed and optimized for the decision support system. In addition, fuzzy logic, decision trees and machine learning algorithms will be included in the system with artificial intelligence support in the future.
• User Interface Design: The system is equipped with an interface that faculty members and administrators can easily use and continues to be developed.
The prototype system was tested with pilot applications and provided faculty members with the opportunity to analyze student performance in detail. The system has presented an approach to measuring the achievement levels of program outcomes according to learning outcomes and the impact rates of these outcomes, and it is thought that it has the potential for wider applicability in the future, will contribute positively to education quality assurance processes and can be adapted to different education levels.