IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 4: August 2026

Determining student scaffolding levels in geometry problem-solving: a fuzzy inference system using Mamdani method

Yuniar Ika Putri Pranyata (Universitas Negeri Malang)
Susiswo Susiswo (Universitas Negeri Malang)
Tjang Daniel Chandra (Universitas Negeri Malang)



Article Info

Publish Date
01 Aug 2026

Abstract

This study aims to adopt a fuzzy logic inference system using Mamdani method to determine the appropriate scaffolding level for students based on errors in solving geometry problems. The fuzzy inference system (FIS) assessed the students' understanding and provided scaffolding to address specific learning needs by analyzing common mistakes in geometry problem-solving. This method optimized the educational process by offering personalized support, enhancing students' problem-solving skills, and reducing error rates. The data processing criteria using the FIS involved analyzing the scores of mathematics education students at a private university in Malang when solving geometry problems, based on Pólya's stages. Mamdani was used to provide recommendations based on students' cognitive data while solving geometry problems, in line with scaffolding components. The results showed that the system personalized scaffolding levels for students working on geometry problems. This contributed to the field of educational technology by presenting a new approach to adaptive learning and emphasized the importance of personalized pedagogical support. The approach was particularly beneficial for geometry teachers who provided individualized scaffolding based on students' errors, contributing to improved learning outcomes.

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Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...