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<p>Amid growing demands for scalable, inclusive, and evidence-based assessment in collaborative learning environments, traditional peer evaluation methods often fall short in ensuring equity, transparency, and adaptability. This study addresses these challenges by introducing the Open Assessment Framework (OPENASSESS)—a structured, ontology-informed architecture designed to optimize peer feedback in Project-Based Collaborative Learning (PBCL) settings. The proposed framework adopts a modular, multi-layered architecture comprising an ontological layer (PEERGROUPONTO), an analytic layer (Group Assessment Analytics), and integration components compatible with LMS platforms via API and xAPI protocols. OPENASSESS operationalizes two core innovations: (1) the intelligent formation of balanced student teams using hierarchical agglomerative clustering informed by cognitive, behavioral, and interpersonal learner data; and (2) the strategic pairing of these teams for reciprocal, open-aligned peer feedback. A quasi-experimental study involving 312 undergraduate programming students compared control groups (randomly assigned) with experimental groups formed and paired using OPENASSESS. Learning outcomes were evaluated across three conditions: before feedback, after conventional peer feedback, and following OPENASSESS-guided feedback. Results showed a 12% increase in project scores for experimental groups, with notable gains in creativity, critical thinking, engagement, and feedback quality. The framework ensures GDPR-compliant data handling, supports inclusivit<b>y</b>, and promotes transparency through explainable group and feedback decisions. While validated in a programming context, OPENASSESS is transferable to broader academic domains. It offers a practical, learner-centered solution for implementing adaptive and ethically grounded peer assessment in collaborative learning environments.</p>

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Open Assessment Framework for Intelligent Peer Feedback in the Project-Based Collaborative Learning Environment

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Asma Hadyaoui,
  • Lilia Cheniti-Belcadhi

摘要

Amid growing demands for scalable, inclusive, and evidence-based assessment in collaborative learning environments, traditional peer evaluation methods often fall short in ensuring equity, transparency, and adaptability. This study addresses these challenges by introducing the Open Assessment Framework (OPENASSESS)—a structured, ontology-informed architecture designed to optimize peer feedback in Project-Based Collaborative Learning (PBCL) settings. The proposed framework adopts a modular, multi-layered architecture comprising an ontological layer (PEERGROUPONTO), an analytic layer (Group Assessment Analytics), and integration components compatible with LMS platforms via API and xAPI protocols. OPENASSESS operationalizes two core innovations: (1) the intelligent formation of balanced student teams using hierarchical agglomerative clustering informed by cognitive, behavioral, and interpersonal learner data; and (2) the strategic pairing of these teams for reciprocal, open-aligned peer feedback. A quasi-experimental study involving 312 undergraduate programming students compared control groups (randomly assigned) with experimental groups formed and paired using OPENASSESS. Learning outcomes were evaluated across three conditions: before feedback, after conventional peer feedback, and following OPENASSESS-guided feedback. Results showed a 12% increase in project scores for experimental groups, with notable gains in creativity, critical thinking, engagement, and feedback quality. The framework ensures GDPR-compliant data handling, supports inclusivity, and promotes transparency through explainable group and feedback decisions. While validated in a programming context, OPENASSESS is transferable to broader academic domains. It offers a practical, learner-centered solution for implementing adaptive and ethically grounded peer assessment in collaborative learning environments.