In computer science, students could benefit from more opportunities to learn important, high-level concepts and to improve their learning skills. Peer review is one method to encourage this by providing students with the opportunity to evaluate other people's work and to receive feedback on their own projects. This allows for rich learning experience but it is not immediately obvious how to create a programming project review that will improve the students' conceptual understanding, require higher level thinking, and be engaging. The current literature does not typically address differences between review implementations or provide reasons for design decisions. This work explored how two different types of reviews affected the students' learning of concepts, high-level thinking, and engagement. There were indications that the type of review affected how well students addressed the concept they were reviewing and the comments' length. This shows that the review's type may affect student engagement and conceptual learning. There were also differences in how they reviewed the concepts of Abstraction, Decomposition, and Encapsulation, suggesting that the concepts are being learned in different ways. Both of these results have an impact on the use of peer review computer science but need further investigation.
Understanding Growth Mindset Practices in an Introductory Physical Computing Classroom: High School Students' Engagement with Debugging by Design Activities
RoboBuddy in the Classroom: Exploring LLM-Powered Social Robots for Storytelling in Learning and Integration Activities
Computer-Human Interaction in Learning and Instruction (CHILI) Lab, EPFL, Lausanne, Switzerland（计算机-人类交互在学习与教学（CHILI）实验室，EPFL，拉沃斯，瑞士）；Depart of Computer Science of the University College London, London, UK（大学学院伦敦计算机科学系，伦敦，英国）
A Case-Study on Teaching Undergraduate-Level Software Engineering Course Using Inverted-Classroom, Large-Group, Real-Client and Studio-Based Instruction Model
Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving
Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA（智能系统研究所，密苏里大学）；Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA（电气与计算机工程系，密苏里大学）；AI4STEM Education Center, University of Georgia, Athens, GA, USA（AI4STEM教育中心，佐治亚大学）；National GENIUS Center, Athens, GA, USA（国家GENIUS中心）；Department of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, GA, USA（数学、科学与社会科学教育系，佐治亚大学）；Faculty of Education, The University of Hong Kong, Hong Kong, PR China（教育学院，香港大学）；Educational Testing Service, Princeton, NJ, USA（教育测评服务）；Department of Applied Social Sciences, Hong Kong Polytechnic University, Hong Kong, PR China（应用社会科学系，香港理工大学）；Department of Psychology, University of Memphis, Memphis, TN, USA（心理学系，密苏里大学）
该研究使用 GPT-4o 在 MATH 数据集上比较双智能体数学解题的四种沟通模式，发现同伴协作准确率最高，并表明有效对话行为是提升 AI 教育协作解题的关键。
Do open citations give insights on the qualitative peer-review evaluation in research assessments? An analysis of the Italian National Scientific Qualification
