Aron Karatayev

Work place: Kazakh-British Technical University, School of Information Technology and Engineering, Almaty, 050000, Kazakhstan

E-mail: ar_karatayev@kbtu.kz

Website: https://orcid.org/0009-0000-3824-3065

Research Interests:

Biography

Aron Karatayev received a B.S. degree in information systems from the Kazakh-British Technical University, Almaty, Kazakhstan. He completed his M.S. degree in software engineering at the same institution. He participated in conferences like KBTU AGSRW 2023 and IEEE SIST 2024. He is currently a PhD student. Professionally, he is a Senior Quality Assurance Engineer at a leading outsourcing company in Kazakhstan. His research interests include fuzzy logic and sets, software testing, and finance.

Author Articles
Fuzzy Intelligent System for Student Software Project Evaluation

By Anna Ogorodova Pakizar Shamoi Aron Karatayev

DOI: https://doi.org/10.5815/ijmecs.2025.04.02, Pub. Date: 8 Aug. 2025

Developing software projects allows students to put knowledge into practice and gain teamwork skills. However, assessing student performance in project-oriented courses poses significant challenges, particularly as class sizes increase. This paper introduces a fuzzy intelligent system designed to evaluate academic software projects using an object-oriented programming and design course as an example. Our methodology involved conducting a survey of student project teams (n=31) and faculty (n=3) to identify key evaluation parameters and their applicable ranges. The critical criteria—clean code, use of inheritance, and functionality—were represented as fuzzy variables with corresponding fuzzy sets. We collaborated with three experts, including one professor and two course instructors, to define a set of fuzzy rules for a fuzzy inference system. This system processes the input criteria to produce a quantifiable measure of project success. Our fuzzy intelligent system demonstrated promising results in automating project evaluation, standardizing assessments, and reducing subjective bias in manual grading. The key findings show that the system effectively manages the increasing instructor workload, provides consistent and transparent evaluations, and offers timely and accurate feedback to students.

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