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2021 | OriginalPaper | Buchkapitel

Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT

verfasst von : Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil Heffernan, Xintao Wu, Sean McGrew, Dongwon Lee

Erschienen in: Artificial Intelligence in Education

Verlag: Springer International Publishing

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Abstract

Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research proposed machine learning based solutions to auto-label educational content with limited success. In this work, we significantly improve prior research by (1) expanding the input types to include KC descriptions, instructional video titles, and problem descriptions (i.e., three types of prediction task), (2) doubling the granularity of the prediction from 198 to 385 KC labels (i.e., more practical setting but much harder multinomial classification problem), (3) improving the prediction accuracies by 0.5–2.3% using Task-adaptive Pre-trained BERT, outperforming six baselines, and (4) proposing a simple evaluation measure by which we can recover 56–73% of mispredicted KC labels. All codes and data sets in the experiments are available at: https://github.com/tbs17/TAPT-BERT

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Titel: Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT
verfasst von: Jia Tracy Shen
Michiharu Yamashita
Ethan Prihar
Neil Heffernan
Xintao Wu
Sean McGrew
Dongwon Lee
Verlag: Springer International Publishing
Buch: Artificial Intelligence in Education
Print ISBN: 978-3-030-78291-7

Electronic ISBN: 978-3-030-78292-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-78292-4_33

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