Assistant Dashboard Plus – Enhancing an Existing Instructor Dashboard with Difficulty Detection and GPT-based Code Clustering
Authors: 
Samuel D George, Tao Huang, Chandler Robinson, Gabriel Schell, Wei Shan, Ziqian Zhao, Zeqi Zhou, Prasun DewanAuthors Info & Claims
Pages 54 - 57
Abstract
As interest in programming as a major grows, instructors must accommodate more students in their programming courses. One particularly challenging aspect of this growth is providing quality assistance to students during in-class and out-of-class programming exercises. Prior work proposes using instructor dashboards to help instructors combat these challenges. Further, the introduction of ChatGPT represents an exciting avenue to assist instructors with programming exercises but needs a delivery method for this assistance. We propose a revision of a current instructor dashboard Assistant Dashboard Plus that extends an existing dashboard with two new features: (a) identifying students in difficulty so that instructors can effectively assist them, and (b) providing instructors with pedagogically relevant groupings of students’ exercise solutions with similar implementations so that instructors can provide overlapping code style feedback to students within the same group. For difficulty detection, it uses a state-of-the-art algorithm for which a visualization has not been created. For code clustering, it uses GPT. We present a first-pass implementation of this dashboard.
Supplemental Material
MP4 File
"Presentation Video"
- Download
- 108.23 MB
References
[1]
Nick Anderson. [n. d.]. College is remade as tech majors surge and humanities dwindle. The Washington Post ([n. d.]). https://www.washingtonpost.com/education/2023/05/19/college-majors-computer-science-humanities/ Accessed: 2024-01-06.
[2]
Jason Carter and Prasun Dewan. 2009. Automatically Identifying That Distributed Programmers Are Stuck. In Proceedings of the 2009 ICSE Workshop on Cooperative and Human Aspects on Software Engineering(CHASE ’09). IEEE Computer Society, USA, 12. https://doi.org/10.1109/CHASE.2009.5071403
[3]
Jason Carter and Prasun Dewan. 2015. Mining Programming Activity to Promote Help. 23–42. https://doi.org/10.1007/978-3-319-20499-4_2
[4]
Nicholas Diana, Michael Eagle, John Stamper, Shuchi Grover, Marie Bienkowski, and Satabdi Basu. 2017. An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (Vancouver, British Columbia, Canada) (LAK ’17). Association for Computing Machinery, New York, NY, USA, 272–279. https://doi.org/10.1145/3027385.3027441
[5]
Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, and Robert C. Miller. 2015. OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale. ACM Trans. Comput.-Hum. Interact. 22, 2, Article 7 (mar 2015), 35 pages. https://doi.org/10.1145/2699751
[6]
Philip J. Guo. 2015. Codeopticon: Real-Time, One-To-Many Human Tutoring for Computer Programming. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (Charlotte, NC, USA) (UIST ’15). Association for Computing Machinery, New York, NY, USA, 599–608. https://doi.org/10.1145/2807442.2807469
[7]
Teemu Koivisto and Arto Hellas. 2022. Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions. In 2022 IEEE Frontiers in Education Conference (FIE). 1–9. https://doi.org/10.1109/FIE56618.2022.9962751
[8]
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, and Bao Ge. 2023. Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models. arxiv:2304.01852 [cs.CL]
[9]
Y. Mao. [n. d.]. One minute is enough: Early Prediction of Student Success and Event-level Difficulty during Novice Programming Tasks. In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019) ([n. d.]). https://par.nsf.gov/biblio/10136495
[10]
Christian Murphy, Gail Kaiser, Kristin Loveland, and Sahar Hasan. 2009. Retina: Helping Students and Instructors Based on Observed Programming Activities. SIGCSE Bull. 41, 1 (mar 2009), 178–182. https://doi.org/10.1145/1539024.1508929
[11]
Natasha Singer. [n. d.]. The Hard Part of Computer Science? Getting Into Class. The New York Times ([n. d.]). https://www.nytimes.com/2019/01/24/technology/computer-science-courses-college.html Accessed: 2024-01-06.
[12]
Aaron J. Smith, Kristy Elizabeth Boyer, Jeffrey Forbes, Sarah Heckman, and Ketan Mayer-Patel. 2017. My Digital Hand: A Tool for Scaling Up One-to-One Peer Teaching in Support of Computer Science Learning. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 549–554. https://doi.org/10.1145/3017680.3017800
[13]
David Stotts and Yu Ji. 2020. Bricks: Extreme Active Learning for Introductory Programming. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE ’20). Association for Computing Machinery, New York, NY, USA, 1418. https://doi.org/10.1145/3328778.3372547
[14]
Eliane S. Wiese, Michael Yen, Antares Chen, Lucas A. Santos, and Armando Fox. 2017. Teaching Students to Recognize and Implement Good Coding Style. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S ’17). Association for Computing Machinery, New York, NY, USA, 41–50. https://doi.org/10.1145/3051457.3051469
[15]
Ashley Ge Zhang, Yan Chen, and Steve Oney. 2023. VizProg: Identifying Misunderstandings By Visualizing Students’ Coding Progress. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 596, 16 pages. https://doi.org/10.1145/3544548.3581516
Index Terms
- Assistant Dashboard Plus – Enhancing an Existing Instructor Dashboard with Difficulty Detection and GPT-based Code Clustering
Recommendations
Designing a learning analytics dashboard for twitter-facilitated teaching
L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at ScaleSocial media sites are increasingly being adopted to support teaching practice in higher education. Learning Analytics (LA) dashboards can be used to reveal how students engage with course material and others in the class. However, research on the best ...
Tracking Students’ Progress in Educational Escape Rooms Through a Sequence Analysis Inspired Dashboard
Technology Enhanced Learning for Inclusive and Equitable Quality EducationAbstractLearning analytics dashboards are the main vehicle for providing educators with a visual representation of data and insights related to teaching and learning. Recent research has found that the data visualizations provided by dashboards are often ...
An instructor dashboard for real-time analytics in interactive programming assignments
LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge ConferenceMany introductory programming environments generate a large amount of log data, but making insights from these data accessible to instructors remains a challenge. This research demonstrates that student outcomes can be accurately predicted from student ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
March 2024
182 pages
ISBN:9798400705090
DOI:10.1145/3640544
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 April 2024
Check for updates
Author Tags
Qualifiers
- Demonstration
- Research
- Refereed limited
Data Availability
"Presentation Video" https://dl.acm.org/doi/10.1145/3640544.3645231#AssistantDashboardPlus-Demo-Video.mp4
Funding Sources
Conference
IUI '24: 29th International Conference on Intelligent User Interfaces
March 18 - 21, 2024
SC, Greenville, USA
Acceptance Rates
Overall Acceptance Rate 746 of 2,811 submissions, 27%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 70Total Downloads
- Downloads (Last 12 months)70
- Downloads (Last 6 weeks)19
Reflects downloads up to 10 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format