用协作工具与评分框架提升医工课程学习公平性与参与度
Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodology
- 结合Google Colab与Weights & Biases实现实时编程与实验追踪
- 两年实施后成绩分布更广、熵值升高，体现评估更公平细致
- 适合关注教育公平与协作式学习的理工科教学研究者
本研究通过两年时间对生物医学图像处理课程进行重构，探索在机器学习健康硕士项目中整合数字协作工具与结构化同伴评价的效果。教学框架融合Google Colab实现实时编程、Weights & Biases进行实验追踪与报告，并采用量规指导的同伴评估机制，以增强学生参与度、过程透明性与评价公正性。与干预前对照组相比，两年实施期间最终项目得分的分散度和熵值均上升，表明评估结果更具区分度与公平性。问卷调查显示学生对课程内容及自身学习过程的投入度显著提高。研究结果表明，工具支持的协作与结构化评价机制有助于提升STEM教育的学习成效与教育公平性。
原文摘要 · Abstract (English)
This study examines the integration of digital collaborative tools and structured peer evaluation in the Machine Learning for Health master's program, through the redesign of a Biomedical Image Processing course over two academic years. The pedagogical framework combines real-time programming with Google Colab, experiment tracking and reporting via Weights & Biases, and rubric-guided peer assessment to foster student engagement, transparency, and fair evaluation. Compared to a pre-intervention cohort, the two implementation years showed increased grade dispersion and higher entropy in final project scores, suggesting improved differentiation and fairness in assessment. The survey results further indicate greater student engagement with the subject and their own learning process. These findings highlight the potential of integrating tool-supported collaboration and structured evaluation mechanisms to enhance both learning outcomes and equity in STEM education.
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