skip to main content
10.1145/3578527.3578533acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisecConference Proceedingsconference-collections
research-article

PRIORITY: An Intelligent Problem Indicator Repository

Published:23 February 2023Publication History

ABSTRACT

We report the design, development and deployment of PRIORITY, an intelligent portal aimed at reducing the workload of instructors, tutors and teaching assistants in large programming courses of creating lab, assignment and exam problems every week. PRIORITY offers a scalable, user friendly and indexed repository of problems that can be queried to retrieve problems related to a particular programming concept, say for loops. PRIORITY accomplishes this by casting problem retrieval as a multi-label learning problem and using solving it using novel feature selection and AI-techniques. We also report the results of an A/B test and user survey, both conducted while PRIORITY was being used to offer a CS1 course taught at IIT Kanpur with over 500 students. PRIORITY has been in deployment at IIT Kanpur for almost 2 years now and our experience thus far suggests that it not only presents a valuable tool for course administrators, but also opens up several intriguing problems at the intersection of programming instruction, pedagogy, machine learning, semi-supervised learning and information retrieval. Code for PRIORITY is available at https://github.com/purushottamkar/priority/

References

  1. Umair Z. Ahmed, Renuka Sindhgatta, Nisheeth Srivastava, and Amey Karkare. 2019. Targeted Example Generation for Compilation Errors, In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), 327–338. https://doi.org/10.1109/ASE.2019.00039Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Eli Bendersky [n. d.]. Pycparser: https://github.com/eliben/pycparser.Google ScholarGoogle Scholar
  3. Lijia Chen, Pingping Chen, and Zhijian Lin. 2020. Artificial Intelligence in Education: A Review. IEEE Access 8(2020), 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510Google ScholarGoogle ScholarCross RefCross Ref
  4. Rajdeep Das, Umair Z. Ahmed, Amey Karkare, and Sumit Gulwani. 2016. Prutor: A System for Tutoring CS1 and Collecting Student Programs for Analysis. arXiv:1608.03828 [cs.CY].Google ScholarGoogle Scholar
  5. Pablo A Estévez, Michel Tesmer, Claudio A Perez, and Jacek M Zurada. 2009. Normalized mutual information feature selection. IEEE Transactions on neural networks 20, 2 (2009), 189–201.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018. Deep Code Search. In Proceedings of the 40th International Conference on Software Engineering (ICSE).Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. 2017. DeepFix: Fixing Common C Language Errors by Deep Learning, In 31st AAAI Conference on Artificial Intelligence (AAAI). In 31st AAAI Conference on Artificial Intelligence (AAAI), 1345–1351.Google ScholarGoogle ScholarCross RefCross Ref
  8. Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016. Summarizing Source Code using a Neural Attention Model. In 54th Annual Meeting of the Association for Computational Linguistics (ACL).Google ScholarGoogle ScholarCross RefCross Ref
  9. Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. 2009. Mining multi-label data. In Data mining and knowledge discovery handbook. Springer, 667–685.Google ScholarGoogle Scholar
  10. Ziyu Yao, Jayavardhan Reddy Peddamail, and Huan Sun. 2019. CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning. In Proceedings of the 30th Web Conference (WebConf).Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Michihiro Yasunaga and Percy Liang. 2020. Graph-based, Self-Supervised Program Repair from Diagnostic Feedback.. In Proceedings of the 37th International Conference on Machine Learning (ICML).Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. PRIORITY: An Intelligent Problem Indicator Repository

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        ISEC '23: Proceedings of the 16th Innovations in Software Engineering Conference
        February 2023
        193 pages
        ISBN:9798400700644
        DOI:10.1145/3578527

        Copyright © 2023 ACM

        Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 23 February 2023

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate76of315submissions,24%
      • Article Metrics

        • Downloads (Last 12 months)28
        • Downloads (Last 6 weeks)0

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format