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<p>The formation of effective collaborative programming groups is vital for collaborative knowledge innovation. Previous research has predominantly examined the influence of group composition approaches from a computational perspective, yet there remains a limited resolution of their real-world educational impacts. This study offers empirical insights into the effects of homogeneous versus heterogeneous groups on student performance within collaborative programming contexts. The group composition system was established using a genetic algorithm, with the inclusion of socio-emotional competence, learning styles, and academic achievement. A total of <i>N</i> = 478 students aged between 13 and 15-years-old voluntarily participated in the study and were divided into 42 heterogeneous groups (<i>n</i> = 166), 40 homogeneous groups (<i>n</i> = 163), and 36 random groups (<i>n</i> = 149) with a group size of four. All participants were subjected to identical pedagogical conditions under a double-blinded study design. Collaborative programming performance was assessed both summatively and formatively, incorporating multi-source evidence from teacher observations, student self-reports, and peer evaluation scores. The results indicate that heterogeneous groups notably outperform homogeneous groups and random groups across most measurements. Implications for implementing collaborative programming in real-world classroom settings are provided.</p>

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Innovation-driven group composition for effective collaborative programming: integrating multi-evidences of teacher, student, and peer assessments

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Tao Xie,
  • Jiazhen Yu

摘要

The formation of effective collaborative programming groups is vital for collaborative knowledge innovation. Previous research has predominantly examined the influence of group composition approaches from a computational perspective, yet there remains a limited resolution of their real-world educational impacts. This study offers empirical insights into the effects of homogeneous versus heterogeneous groups on student performance within collaborative programming contexts. The group composition system was established using a genetic algorithm, with the inclusion of socio-emotional competence, learning styles, and academic achievement. A total of N = 478 students aged between 13 and 15-years-old voluntarily participated in the study and were divided into 42 heterogeneous groups (n = 166), 40 homogeneous groups (n = 163), and 36 random groups (n = 149) with a group size of four. All participants were subjected to identical pedagogical conditions under a double-blinded study design. Collaborative programming performance was assessed both summatively and formatively, incorporating multi-source evidence from teacher observations, student self-reports, and peer evaluation scores. The results indicate that heterogeneous groups notably outperform homogeneous groups and random groups across most measurements. Implications for implementing collaborative programming in real-world classroom settings are provided.