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

Tao Xie; Jiazhen Yu

doi:10.1007/s11412-026-09469-8

February 5, 2026International Journal of Computer-Supported Collaborative Learning

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

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Key Points

Effective group composition can significantly improve collaborative programming tasks.
Assessment data from teachers, students, and peers illustrate collaborative effectiveness quantitatively.
Observational analysis of assessment methodologies shows varied impacts on group performance and innovation.
Enhancing collaborative programming strategies may lead to improved educational environments, promoting student engagement.

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Implication

This observational analysis reveals effective group composition influences collaborative programming outcomes, suggesting enhancements for learning environments.

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Xie et al. (2026) studied this question.

synapsesocial.com/papers/69a7674fbadf0bb9e87e069a https://doi.org/10.1007/s11412-026-09469-8

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

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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