The student peer-group is one of the most important influences on student development. Group work is essential for creating positive learning experiences, especially in remote-learning where student interactions are more challenging. While the benefits of study groups are established, students from underrepresented communities often face challenges in finding social support for their education when compared with those from majority groups. We present a system for flexible and inclusive study group formation that can scale to thousands of students. Our focus is on long-term study groups that persist throughout the semester and beyond. Students are periodically provided opportunities to obtain a new study group if they feel their current group is not a good fit. In contrast to prior work that generates single-use groups, our process enables continuous refinement of groups for each student, which in turn informs our algorithm for future iterations. We trialed our approach in a 1000+ student introductory Electrical Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We found that of all students matched to study groups through our algorithm, a large majority felt comfortable asking questions (78%) and sharing ideas (74%) with their group. Students from underrepresented backgrounds were more likely to request software-matching for study groups when compared with students from majority groups. However, underrepresented students that we did match into study groups had group experiences that were just as successful as students from' majority groups. Students in engaged, regularly participating study groups had more positive results across all other indicators of the study group experience, and certain positive group experiences were associated with higher exam scores overall.
翻译:学生同龄群是影响学生发展的最重要因素之一; 学生同龄群是学生发展的最重要影响之一; 集体工作对于创造积极的学习经验至关重要, 特别是在学生互动更具挑战性的远程学习中; 研究群体的好处虽然已经确立,但代表性不足社区的学生在寻求社会对其教育的支持方面往往与多数群体的学生相比,面临着挑战; 我们提出了一个灵活和包容性的学习群体形成系统,可以推广到数千名学生; 我们的重点是在整个学期及以后长期存在的长期学习群体; 如果学生认为他们目前的群体不适合,则定期为他们提供获得新的学习小组的机会; 与以前生成单一使用群体的工作不同,我们的进程使得每个学生都能够不断改进群体,这反过来又为我们今后的迭代算法提供了信息; 我们尝试了我们的方法,在1 000+学生介绍性电气工程和计算机科学课程中,这种课程可以完全在线进行,可以推广到数千名学生。 我们发现,所有学生都通过我们的算法学习团体,大多数学生都觉得自己可以询问问题(78%),并与他们群体分享想法(74 %)。 与以前具有代表性的总体考试背景的学生相比,我们的过程可以不断改进,反过来学习背景的学生群体的学生群体学习,我们更可能要求多数群体的学生群体学习中的大多数群体中的学生与参加多数群体。