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Factors Influencing Students' Help-Seeking Behavior while Programming with Human and Computer Tutors

Published: 14 August 2017 Publication History

Abstract

When novice students encounter difficulty when learning to program, some can seek help from instructors or teaching assistants. This one-on-one tutoring is highly effective at fostering learning, but busy instructors and large class sizes can make expert help a scarce resource. Increasingly, programming environments attempt to imitate this human support by providing students with hints and feedback. In order to design effective, computer-based help, it is important to understand how and why students seek and avoid help when programming, and how this process differs when the help is provided by a human or a computer. We explore these questions through a qualitative analysis of 15 students' interviews, in which they reflect on solving two programming problems with human and computer help. We discuss implications for help design and present hypotheses on students' help-seeking behavior.

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    cover image ACM Conferences
    ICER '17: Proceedings of the 2017 ACM Conference on International Computing Education Research
    August 2017
    316 pages
    ISBN:9781450349680
    DOI:10.1145/3105726
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    Publication History

    Published: 14 August 2017

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    Author Tags

    1. computer science education
    2. help-seeking
    3. intelligent tutoring systems
    4. novice programmers

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    ICER '17: International Computing Education Research Conference
    August 18 - 20, 2017
    Washington, Tacoma, USA

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    ICER '17 Paper Acceptance Rate 29 of 180 submissions, 16%;
    Overall Acceptance Rate 189 of 803 submissions, 24%

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    ACM Conference on International Computing Education Research
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    Cited By

    View all
    • (2025)Student Perceptions of the Help Resource LandscapeProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701851(596-602)Online publication date: 12-Feb-2025
    • (2024)AI in CS Education: Opportunities, Challenges, and Pitfalls to AvoidACM Inroads10.1145/367920515:3(52-57)Online publication date: 21-Aug-2024
    • (2024)The Trees in the Forest: Characterizing Computing Students' Individual Help-Seeking ApproachesProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671099(343-358)Online publication date: 12-Aug-2024
    • (2024)Debugging with an AI Tutor: Investigating Novice Help-seeking Behaviors and Perceived LearningProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671092(84-94)Online publication date: 12-Aug-2024
    • (2023)Factors Influencing the Social Help-seeking Behavior of Introductory Programming Students in a Competitive University EnvironmentACM Transactions on Computing Education10.1145/363905924:1(1-27)Online publication date: 30-Dec-2023
    • (2023)Effects of Automated Feedback in Scratch Programming TutorialsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588803(396-402)Online publication date: 29-Jun-2023
    • (2023)Analysis of Novices' Web-Based Help-Seeking Behavior While ProgrammingProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569852(945-951)Online publication date: 2-Mar-2023
    • (2023)iSnap: Evolution and Evaluation of a Data-Driven Hint System for Block-Based ProgrammingIEEE Transactions on Learning Technologies10.1109/TLT.2022.322357716:3(399-413)Online publication date: 1-Jun-2023
    • (2022)Development and Use of Domain-specific Learning Theories, Models, and Instruments in Computing EducationACM Transactions on Computing Education10.1145/353022123:1(1-48)Online publication date: 29-Dec-2022
    • (2022)Using Adaptive Parsons Problems to Scaffold Write-Code ProblemsProceedings of the 2022 ACM Conference on International Computing Education Research - Volume 110.1145/3501385.3543977(15-26)Online publication date: 3-Aug-2022
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