Inclusive study group formation at scale

Abstract

Underrepresented students face many significant challenges in their education. In particular, they often have a harder time than their peers from majority groups in building long-term high-quality study groups. This challenge is exacerbated in remote-learning scenarios, where students are unable to meet face-to-face and must rely on pre-existing networks for social support. We present a scalable system that removes structural obstacles faced by underrepresented students and supports all students in building inclusive and flexible study groups. One of our main goals is to make the usually informal and unstructured process of finding study groups for homework more equitable by providing a uniform but lightweight structure. We aim to provide students from underrepresented groups an experience that is similar in quality to that of students from majority groups. Our process is unique in that it allows students the opportunity to request group reassignments during the semester if they wish. Unlike other collaboration tools our system is not mandatory and does not use peer-evaluation. We trialed our approach in a 1000+ student introductory Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We find that students from underrepresented backgrounds were more likely to ask for group-matching support compared to students from majority groups. At the same time, underrepresented students that we matched into study groups had group experiences that were comparable to students we matched from majority groups. B-range students in high-comfort and high-activity groups had improved learning outcomes.

Article

BibTeX

 @inproceedings{kohli2023sigcse, 
    author = {Kohli, Sumer and Ramachandran, Neelesh and Tudor, Ana and 
    Tumushabe, Gloria and Hsu, Olivia and Ranade, Gireeja}, 
    title = {Inclusive Study Group Formation at Scale}, 
    year = {2023}, 
    isbn = {9781450394314}, 
    publisher = {Association for Computing Machinery}, 
    address = {New York, NY, USA}, 
    url = {https://doi-org.stanford.idm.oclc.org/10.1145/3545945.3569885}, 
    doi = {10.1145/3545945.3569885}, 
    abstract = {Underrepresented students face many significant challenges in 
    their education. In particular, they often have a harder time than their peers 
    from majority groups in building long-term high-quality study groups. This 
    challenge is exacerbated in remote-learning scenarios, where students are 
    unable to meet face-to-face and must rely on pre-existing networks for social 
    support.We present a scalable system that removes structural obstacles faced by 
    underrepresented students and supports all students in building inclusive and 
    flexible study groups. One of our main goals is to make the traditionally 
    informal and unstructured process of finding study groups for homework more 
    equitable by providing a uniform but lightweight structure. We aim to provide 
    students from underrepresented groups an experience that is similar in quality 
    to that of students from majority groups. Our process is unique in that it 
    allows students the opportunity to request group reassignments during the 
    semester if they wish. Unlike other collaboration tools our system is not 
    mandatory and does not use peer-evaluation.We trialed our approach in a 1000+ 
    student introductory Engineering and Computer Science course that was conducted 
    entirely online during the COVID-19 pandemic. We find that students from 
    underrepresented backgrounds were more likely to ask for group-matching support 
    compared to students from majority groups. At the same time, underrepresented 
    students that we matched into study groups had group experiences that were 
    comparable to students we matched from majority groups. B-range students in 
    high-comfort and high-quality groups had improved learning outcomes.}, 
    booktitle = {Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1}, 
    pages = {11–17}, 
    numpages = {7}, 
    keywords = {remote learning, group formation, study groups, education}, 
    location = {Toronto ON, Canada}, 
    series = {SIGCSE 2023} }