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Management, structures and tools to scale up personal advising in large programming courses

Published: 20 October 2011 Publication History

Abstract

We see programming in higher education as a craft that benefits from a direct contact, support and feedback from people who already master it. We have used a method called Extreme Apprenticeship (XA) to support our CS1 education. XA is based on a set of values that emphasize actual programming along with current best practices, coupled tightly with continuous feedback between the advisor and the student. As such, XA means one-on-one advising which requires resources. However, we have not used abundant resources even when scaling up the XA model. Our experiments show that even in relatively large courses (n = 192 and 147), intensive personal advising in CS1 does not necessarily lead to more expensive course organization, even though the number of advisor-evaluated student exercises in a course grew from 252 to 17420. A thorough comparison of learning results and organizational costs between our traditional lecture/exercise-based course model and XA-based course model is presented.

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    cover image ACM Conferences
    SIGITE '11: Proceedings of the 2011 conference on Information technology education
    October 2011
    340 pages
    ISBN:9781450310178
    DOI:10.1145/2047594
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    Publication History

    Published: 20 October 2011

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    Author Tags

    1. continuous feedback
    2. course cost
    3. individual education
    4. instructional design
    5. programming education
    6. resource allocation

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    • (2023)Computing Education Research in FinlandPast, Present and Future of Computing Education Research10.1007/978-3-031-25336-2_16(335-372)Online publication date: 5-Jan-2023
    • (2020)The Extreme Apprenticeship MethodPRIMUS10.1080/10511970.2020.181833231:10(1106-1120)Online publication date: 24-Sep-2020
    • (2018)Introductory programming: a systematic literature reviewProceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3293881.3295779(55-106)Online publication date: 2-Jul-2018
    • (2017)Comparison of Time Metrics in ProgrammingProceedings of the 2017 ACM Conference on International Computing Education Research10.1145/3105726.3106181(200-208)Online publication date: 14-Aug-2017
    • (2016)Automatic Inference of Programming Performance and Experience from Typing PatternsProceedings of the 47th ACM Technical Symposium on Computing Science Education10.1145/2839509.2844612(132-137)Online publication date: 17-Feb-2016
    • (2016)Translating Principles of Effective Feedback for Students into the CS1 ContextACM Transactions on Computing Education10.1145/273759616:1(1-27)Online publication date: 28-Jan-2016
    • (2016)A “light” Application of Blended Extreme Apprenticeship in Teaching Programming to Students of MathematicsMethodologies and Intelligent Systems for Technology Enhanced Learning, 6th International Conference10.1007/978-3-319-40165-2_8(73-80)Online publication date: 26-May-2016
    • (2015)Identification of programmers from typing patternsProceedings of the 15th Koli Calling Conference on Computing Education Research10.1145/2828959.2828960(60-67)Online publication date: 19-Nov-2015
    • (2015)Exploring Machine Learning Methods to Automatically Identify Students in Need of AssistanceProceedings of the eleventh annual International Conference on International Computing Education Research10.1145/2787622.2787717(121-130)Online publication date: 9-Jul-2015
    • (2015)A Purposeful MOOC to Alleviate Insufficient CS Education in Finnish SchoolsACM Transactions on Computing Education10.1145/271631415:2(1-18)Online publication date: 27-Apr-2015
    • Show More Cited By

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