Journal of Vocational, Informatics and Computer Education
Vol 4, No 2 (2026): June 2026

Improving Learner Autonomy and Problem-Solving Skills Through The STAD Cooperative Learning Strategy In Digital Pattern Making for Vocational Fashion Education

Nurhijrah (Universitas Negeri Makassar)
Syarifah Suryana (Universitas Negeri Makassar)



Article Info

Publish Date
12 Jun 2026

Abstract

Purpose – The rapid digital transformation of the fashion industry has increased the need for vocational students to develop not only technical competencies but also learner autonomy and problem-solving skills in software-intensive learning environments. This study aimed to investigate the effectiveness of the Student Teams Achievement Divisions (STAD) cooperative learning model integrated with digital pattern-making instruction in improving these competencies among vocational fashion students. Methods – The study employed a quasi-experimental design using a non-equivalent control group pretest–posttest approach. Participants consisted of 59 eleventh-grade students enrolled in a vocational fashion program in Indonesia, divided into an experimental group (n = 30) and a control group (n = 29). Data were collected using a learner autonomy questionnaire and a performance-based problem-solving assessment related to digital pattern-making tasks. Data analysis involved descriptive statistics, paired sample t-tests, independent sample t-tests, and Analysis of Covariance (ANCOVA). Findings – The findings indicated that students who participated in STAD-based digital pattern-making instruction demonstrated higher improvements in learner autonomy and problem-solving skills compared to students who received conventional instruction. ANCOVA results revealed statistically significant treatment effects for learner autonomy and problem-solving skills (p < 0.001). Although large effect sizes were observed, the findings should be interpreted cautiously due to the relatively small sample size, intact-class design, single-institution setting, and partial reliance on self-report measures. Therefore, the study provides preliminary quasi-experimental evidence rather than broad generalizable conclusions. The contribution of this study lies in applying the STAD cooperative learning model to software-intensive vocational fashion learning tasks, particularly digital pattern making. Research implications – The findings suggest that collaborative digital learning environments may support the development of self-regulated learning behaviors and authentic vocational problem-solving skills in fashion education contexts Originality – The contribution of this study lies in applying the STAD cooperative learning model to software-intensive vocational fashion learning tasks, particularly digital pattern making.

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Journal Info

Abbrev

VOICE

Publisher

Subject

Computer Science & IT Education

Description

1. Informatics and Computing Research addressing the design, development, implementation, and evaluation of computing technologies relevant to educational, professional, and digital learning environments, including but not limited to: Artificial Intelligence and Machine Learning Deep Learning and ...