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Informing Expert Feature Engineering through Automated Approaches: Implications for Coding Qualitative Classroom Video Data

Authors:

Nessrine Machaka,

Christina Krist,

Nigel BoschAuthors Info & Claims

LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference

Pages 630 - 636

https://doi.org/10.1145/3576050.3576090

Published: 13 March 2023 Publication History

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Abstract

While classroom video data are detailed sources for mining student learning insights, their complex and unstructured nature makes them less than straightforward for researchers to analyze. In this paper, we compared the differences between the processes of expert-informed manual feature engineering and automated feature engineering using positional data for predicting student group interaction in four middle school and high school mathematics classroom videos. Our results highlighted notable differences, including improved model accuracy for the combined (manual features + automated features) models compared to the only-manual-features models (mean AUC = .778 vs. .706) at the cost of feature interpretability, increased number of features for automated feature engineering (1523 vs. 178), and engineering approach (domain-agnostic in automated vs. domain-knowledge-informed in manual). We carried out feature importance analyses and discuss the implications of the results for potentially augmenting human perspectives about qualitatively coding classroom video data by confirming and expanding views on which body areas and characteristics may be relevant to the target interaction behavior. Lastly, we discuss our study’s limitations and future work.

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Cited By

Suraworachet WSeon JCukurova M(2024)Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approachesProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636905(473-485)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636905
Pinto JPaquette L(2024)Deep Learning for Educational Data ScienceTrust and Inclusion in AI-Mediated Education10.1007/978-3-031-64487-0_6(111-139)Online publication date: 28-Sep-2024
https://doi.org/10.1007/978-3-031-64487-0_6

Index Terms

Informing Expert Feature Engineering through Automated Approaches: Implications for Coding Qualitative Classroom Video Data
1. Applied computing
  1. Education

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cover image ACM Other conferences

LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference

March 2023

692 pages

ISBN:9781450398657

DOI:10.1145/3576050

Program Chairs:
Isabel Hilliger
Pontificia Universidad Católica de Chile, Chile
,
Hassan Khosravi
University of Queensland, Australia
,
Bart Rienties
Open University, United Kingdom
,
Shane Dawson
University of South Australia, Australia

Copyright © 2023 ACM.

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Published: 13 March 2023

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LAK 2023

LAK 2023: 13th International Learning Analytics and Knowledge Conference

March 13 - 17, 2023

TX, Arlington, USA

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Cited By

Suraworachet WSeon JCukurova M(2024)Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approachesProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636905(473-485)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636905
Pinto JPaquette L(2024)Deep Learning for Educational Data ScienceTrust and Inclusion in AI-Mediated Education10.1007/978-3-031-64487-0_6(111-139)Online publication date: 28-Sep-2024
https://doi.org/10.1007/978-3-031-64487-0_6

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