CS-CLIMATE

Fostering collaborative dialogue for rigorous learning and diverse student retention in computer science. (Funding: National Science Foundation, CNS-1453520, CNS-1622438)

Introduction

A rich body of evidence suggests that collaborative learning holds many benefits for computer science students, yet there is growing recognition that neither collaborative learning itself, nor the innovative curricula in which it may be situated, are “magic bullets” capable of single-handedly solving the computing pipeline problem. In contrast to being a one-size-fits-all solution, collaborative learning is highly dependent upon characteristics of the collaborators and on fine-grained interactions. This project is led by PI Kristy Boyer at the University of Florida and is funded by the National Science Foundation through a CAREER award.

Project Description

The CS-CLIMATE project is investigating the facets of collaborative dialogue that are particularly effective for fostering learning, sense of identity, motivation, and continued engagement in computer science for diverse learners. The project is organized around three thrusts.

Thrust 1. Collect a rich set of computer science collaborative learning data. We implemented studies with hundreds of CS1 students and investigated their experiences during collaborative coding. By examining student reflections on collaborative coding, we found important themes centered around self-efficiency, social growth, deeper learning and confidence in coding. We also investigated the effects and prominence of these themes by gender. These datasets have provided a ground-truth measure of students' collaborative approaches within computer science learning tasks.

Thrust 2. Examine the fine-grained facets of collaborative dialogue that are particularly effective for diverse computer science learners. The project is examining the relationship between dialogue patterns and outcomes. To analyze these dialogues, we have used and developed a suite of manual and automated natural language dialogue analysis methods for tasks such as dialogue act classification and reference resolution. The findings have revealed features in students’ collaborative dialogue that are associated with improved performance in programming tasks or motivational outcomes, such as stress and perceived competence.

Thrust 3. Implement and evaluate evidence-based pedagogical support for fostering effective collaborative dialogue. The project is conducting experimental studies to identify best practices to implement pair programming activities with CS1 students. These studies evaluate how different forms of structuring collaboration can improve students' outcomes.

publications

2021
[20]Using Dialogue Analysis to Predict Women’s Stress During Remote Collaborative Learning in Computer Science. Kimberly Michelle Ying, Gloria Ashiya Katuka, Kristy Elizabeth Boyer. Proceedings of the 26th International Conference on Innovation and Technology in Computer Science Education (ITiCSE), 2021, pp. To appear. [bib]
[19]CS1 Students’ Perspectives on the Computer Science Gender Gap: Achieving Equity Requires Awareness. Kimberly Michelle Ying, Alexia Charis Martin, Fernando J. Rodríguez, Kristy Elizabeth Boyer. Proceedings of the International Conference on Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 2021, pp. 1-9. [bib]
[18]Confidence, Connection, and Comfort: Reports from an All-Women's CS1 Class. Kimberly Michelle Ying, Fernando J. Rodríguez, Alexandra Lauren Dibble, Alexia Charis Martin, Kristy Elizabeth Boyer, Sanethia V. Thomas, Juan E. Gilbert. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE), 2021, pp. 699-705. [bib]
2020
[17]Understanding Women's Remote Collaborative Programming Experiences: The Relationship between Dialogue Features and Reported Perceptions. Kimberly Michelle Ying, Fernando J. Rodríguez, Alexandra Lauren Dibble, Kristy Elizabeth Boyer. Proceedings of the ACM on Human-Computer Interaction (CHI), vol. 4 no. CSCW3, 2020. [bib]
[16]Gender Differences in Stress, Perceived Competence, and Perceived Choice during Remote Collaborative Problem Solving. Kimberly Michelle Ying, Fernando J. Rodríguez, Kristy Elizabeth Boyer. Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, Tennessee, 2020, pp. 799-800. [bib]
[15]Understanding Students' Needs for Better Collaborative Coding Tools. Kimberly Michelle Ying, Kristy Elizabeth Boyer. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, Hawaii, 2020, pp. 1-8. [bib]
[14]User-Centered Design of a Mobile Java Practice App: A Comparison of Question Formats. Mohona Ahmed, Kimberly Michelle Ying, Kristy Elizabeth Boyer. Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE), Portland, Oregon, 2020, pp. 1158-1164. [bib]
2019
[13]In Their Own Words: Gender Differences in Student Perceptions of Pair Programming. Kimberly Michelle Ying, Lydia G. Pezzullo, Mohona Ahmed, Kassandra Crompton, Jeremiah Blanchard, Kristy Elizabeth Boyer. Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE), 2019, pp. 1053-1059. [bib]
2018
[12]Predicting Student Performance Based on Eye Gaze During Collaborative Problem Solving. Mehmet Celepkolu, Kristy Elizabeth Boyer. Proceedings of the 4th International Workshop on Group Interaction Frontiers in Technology (GIFT), 2018. [bib]
[11]Thematic Analysis of Students’ Reflections on Pair Programming. Mehmet Celepkolu, Kristy Elizabeth Boyer. Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE), Baltimore, Maryland, 2018, pp. 771-776. [bib]
[10]The Importance of Producing Shared Code Through Pair Programming. Mehmet Celepkolu, Kristy Elizabeth Boyer. Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE), Baltimore, Maryland, 2018, pp. 765-770. [bib]
2017
[9]Think First: Fostering Substantive Contributions in Collaborative Problem-Solving Dialogues. Mehmet Celepkolu, Joseph B. Wiggins, Kristy Elizabeth Boyer, Kyla McMullen. Proceedings of the 12th International Conference on Computer Supported Collaborative Learning (CSCL), Philadelphia, Pennsylvania, 2017, pp. 295–302. [bib]
[8]Toward Conversational Agents that Support Learning: A Look at Human Collaborations in Computer Science Problem Solving. Fernando J. Rodríguez, Kimberly Michelle Price, Mickey Vellukunnel, Kristy Elizabeth Boyer. Proceedings of the Conversational UX Design CHI 2017 Workshop, Denver, Colorado, 2017. [bib]
[7]Deconstructing the Discussion Forum: Student Questions and Computer Science Learning. Mickey Vellukunnel, Philip Sheridan Buffum, Kristy Elizabeth Boyer, Jeffrey Forbes, Sarah Heckman, Ketan Mayer-Patel. Proceedings of the 48th ACM Technical Symposium on Computer Science Education (SIGCSE), Seattle, Washington, 2017, pp. 603-608. [bib]
[6]Exploring the Pair Programming Process: Characteristics of Effective Collaboration. Fernando J. Rodríguez, Kimberly Michelle Price, Kristy Elizabeth Boyer. Proceedings of the 48th ACM Technical Symposium on Computer Science Education (SIGCSE), Seattle, Washington, 2017, pp. 507-512. [bib]
2016
[5]Gender Differences in Facial Expressions of Affect During Learning. Alexandria K. Vail, Joseph F. Grafsgaard, Kristy Elizabeth Boyer, Eric N. Wiebe, James C.. Lester. Proceedings of the 24th International Conference on User Modelling, Adaptation, and Personalization, Halifax, Canada, 2016, pp. 65-74. [bib]
[4]Predicting Learning from Student Affective Response to Tutor Questions. Alexandria K. Vail, Joseph F. Grafsgaard, Kristy Elizabeth Boyer, Eric N. Wiebe, James C. Lester. Proceedings of the 13th International Conference on Intelligent Tutoring Systems, Zagreb, Croatia, 2016, pp. 154-164. [bib]
[3]The Affective Impact of Tutor Questions: Predicting Frustration and Engagement. Alexandria K. Vail, Joseph B. Wiggins, Joseph F. Grafsgaard, Kristy Elizabeth Boyer, Eric N. Wiebe, James C.. Lester. Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, North Carolina, 2016, pp. 247-254. [bib]
[2]Reference Resolution in Situated Dialogue with Learned Semantics. Xiaolong Li, Kristy Elizabeth Boyer. Proceedings of the 17th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2016), 2016, pp. 329–338. [bib]
[1]Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Science. Joseph B. Wiggins, Joseph F. Grafsgaard, Kristy Elizabeth Boyer, Eric N. Wiebe, James C. Lester. International Journal of Artificial Intelligence in Education, 2016, pp. 1-24. [bib]