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

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Title: Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT
Language: English
Authors: Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil Heffernan, Xintao Wu, Sean McGrew, Dongwon Lee
Source: Grantee Submission. 2021Paper presented at the International Conference on Artificial Intelligence in Education (AIED) (2021).
Peer Reviewed: Y
Page Count: 12
Publication Date: 2021
Sponsoring Agency: National Science Foundation (NSF)
Institute of Education Sciences (ED)
Office of Elementary and Secondary Education (OESE) (ED), Education Innovation and Research (EIR)
Office of Naval Research (ONR) (DOD)
Contract Number: 1940236
1940076
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Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: Mathematics Education, Knowledge Level, Video Technology, Educational Technology, Prediction, Classification, Accuracy, Intelligent Tutoring Systems, Natural Language Processing
DOI: 10.1007/978-3-030-78292-4_33
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 [This paper was published in: "AIED 2021, LNAI1 2748," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 408-19.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2024
Accession Number: ED637573
Database: ERIC
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  Data: 10.1007/978-3-030-78292-4_33
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  Data: 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 [This paper was published in: "AIED 2021, LNAI1 2748," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 408-19.]
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      – SubjectFull: Classification
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      – SubjectFull: Accuracy
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