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iSnap: Towards Intelligent Tutoring in Novice Programming Environments

Authors:

Thomas W. Price,

Dragan LipovacAuthors Info & Claims

SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

Pages 483 - 488

https://doi.org/10.1145/3017680.3017762

Published: 08 March 2017 Publication History

Abstract

Programming environments intentionally designed to support novices have become increasingly popular, and growing research supports their efficacy. While these environments offer features to engage students and reduce the burden of syntax errors, they currently offer little support to students who get stuck and need expert assistance. Intelligent Tutoring Systems (ITSs) are computer systems designed to play this role, helping and guiding students to achieve better learning outcomes. We present iSnap, an extension to the Snap programming environment which adds some key features of ITSs, including detailed logging and automatically generated hints. We share results from a pilot study of iSnap, indicating that students are generally willing to use hints and that hints can create positive outcomes. We also highlight some key challenges encountered in the pilot study and discuss their implications for future work.

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Cited By

Zhang YPaquette LBosch N(2024)Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event SequencesJournal of Educational and Behavioral Statistics10.3102/10769986241248772Online publication date: 9-May-2024
https://doi.org/10.3102/10769986241248772
Birillo A(2024)Bringing Industry-Grade Code Quality and Practices into Software Engineering Education (Doctoral Consortium)Proceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699571(1-2)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699571
Birillo AArtser EPotriasaeva AVlasov IDzialets KGolubev YGerasimov IKeuning HBryksin T(2024)One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming TasksProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699556(1-12)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699556
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iSnap: Towards Intelligent Tutoring in Novice Programming Environments

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Published In

cover image ACM Conferences

SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

March 2017

838 pages

ISBN:9781450346986

DOI:10.1145/3017680

General Chairs:
Michael E. Caspersen
Aarhus University
,
Stephen H. Edwards
Virginia Tech
,
Program Chairs:
Tiffany Barnes
North Carolina State University
,
Daniel D. Garcia
University of California, Berkeley

Copyright © 2017 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].

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Published: 08 March 2017

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SIGCSE '17: The 48th ACM Technical Symposium on Computer Science Education

March 8 - 11, 2017

Washington, Seattle, USA

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SIGCSE '17 Paper Acceptance Rate 105 of 348 submissions, 30%;

Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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Cited By

Zhang YPaquette LBosch N(2024)Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event SequencesJournal of Educational and Behavioral Statistics10.3102/10769986241248772Online publication date: 9-May-2024
https://doi.org/10.3102/10769986241248772
Birillo A(2024)Bringing Industry-Grade Code Quality and Practices into Software Engineering Education (Doctoral Consortium)Proceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699571(1-2)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699571
Birillo AArtser EPotriasaeva AVlasov IDzialets KGolubev YGerasimov IKeuning HBryksin T(2024)One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming TasksProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699556(1-12)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699556
Kong MHe OLouis Mauriello MPollock L(2024)"Anything That Can Be Streamlined Would Be Great": Validating Elementary School Teachers’ Preferences for a Block-Based Programming Teaching Augmentation SystemProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699539(1-9)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699539
Pankiewicz MBaker RMonga MLonati VBarendsen ESheard JPaterson J(2024)Navigating Compiler Errors with AI Assistance - A Study of GPT Hints in an Introductory Programming CourseProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653608(94-100)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653608
Hou IMettille SMan OLi ZZastudil CMacNeil S(2024)The Effects of Generative AI on Computing Students’ Help-Seeking PreferencesProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636248(39-48)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636248
Tabarsi BReichert HLytle NCatete VBarnes T(2024)Scaffolding Novices: Analyzing When and How Parsons Problems Impact Novice Programming in an Integrated Science AssignmentProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671110(42-54)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671110
Xiao RHou XStamper J(2024)Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint NovicesExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650937(1-10)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650937
Deiner AFraser GRoychoudhury APaiva AAbreu RStorey M(2024)NuzzleBug: Debugging Block-Based Programs in ScratchProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623331(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3623331
Yang ALin JLin COgata H(2024)Enhancing Python Learning with PyTutor: Efficacy of a ChatGPT-Based Intelligent Tutoring System in Programming EducationComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100309(100309)Online publication date: Sep-2024
https://doi.org/10.1016/j.caeai.2024.100309
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