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Using Fine Grained Programming Error Data to Enhance CS1 Pedagogy

Topics: Active Learning; Blended Learning; Classroom Management; Community Building; Computer-Supported Collaborative Learning; Constructivism and Social Constructivism ; Educational Data Mining; e-Learning Hardware and Software; e-Learning Platforms, Portals; Flipped Classroom; Learning Analytics; Mentoring and Tutoring; Metrics and Performance Measurement; Synchronous and Asynchronous Learning; Tools for Educational Communication and Collaboration; Tools to Assess Learning

Authors: Fatima Abu Deeb ; Antonella DiLillo and Timothy Hickey

Affiliation: Brandeis University, United States

ISBN: 978-989-758-291-2

Keyword(s): Near-peer Mentoring, Peer Led Team Learning, Study Group Formation, Online IDEs, Educational Data Mining, Hierarchical Clustering, Classroom Orchestration, Markov Models, Machine Learning, Learning Analytics.

Abstract: The paper reports on our experience using the log files from Spinoza, an online IDE for Java and Python, to enhance the pedagogy in Introductory Programming classes (CS1). Spinoza provides a web-based IDE that offers programming problems with automatic unit-testing. Students get immediate feedback and can resubmit until they get a correct program or give up. Spinoza stores all of their attempts and provides orchestration tools for the instructor to monitor student programming performance in real-time. These log files can be used to introduce a wide variety of effective pedagogical practices into CS1 and this paper provides several examples. One of the simplest is forming recitation groups based on features of student’s problem solving behavior over the previous week. There are many real-time applications of the log data in which the most common errors that students make are detected during an in-class programming exercise and those errors are then used to either provide debugging prac tice or to provide the examples of buggy programming style. Finally, we discuss the possible use of machine learning clustering algorithms in recitation group formation. (More)

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Paper citation in several formats:
Abu Deeb, F.; DiLillo, A. and Hickey, T. (2018). Using Fine Grained Programming Error Data to Enhance CS1 Pedagogy.In Proceedings of the 10th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-291-2, pages 28-37. DOI: 10.5220/0006666400280037

@conference{csedu18,
author={Fatima Abu Deeb. and Antonella DiLillo. and Timothy Hickey.},
title={Using Fine Grained Programming Error Data to Enhance CS1 Pedagogy},
booktitle={Proceedings of the 10th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2018},
pages={28-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006666400280037},
isbn={978-989-758-291-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Using Fine Grained Programming Error Data to Enhance CS1 Pedagogy
SN - 978-989-758-291-2
AU - Abu Deeb, F.
AU - DiLillo, A.
AU - Hickey, T.
PY - 2018
SP - 28
EP - 37
DO - 10.5220/0006666400280037

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