AI scoring for international large-scale assessments using a deep learning model and multilingual data:
Artificial Intelligence (AI) scoring for constructed-response items, using recent advancements in multilingual, deep learning techniques utilising models pre-trained with a massive multilingual text corpus, is examined using international large-scale assessment data. Historical student responses to...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | , , , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2023
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| Schriftenreihe: | OECD Education Working Papers
no.287 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Artificial Intelligence (AI) scoring for constructed-response items, using recent advancements in multilingual, deep learning techniques utilising models pre-trained with a massive multilingual text corpus, is examined using international large-scale assessment data. Historical student responses to Reading and Science literacy cognitive items developed under the PISA analytical framework are used as training data for deep learning together with multilingual data to construct an AI model. The trained AI models are then used to score and the results compared with human-scored data. The score distributions estimated based on the AI-scored data and the human-scored data are highly consistent with each other; furthermore, even item-level psychometric properties of the majority of items showed high levels of agreement, although a few items showed discrepancies. This study demonstrates a practical procedure for using a multilingual data approach, and this new AI-scoring methodology reached a practical level of quality, even in the context of an international large-scale assessment. |
| Beschreibung: | 1 Online-Ressource (34 p.) 21 x 28cm. |
| DOI: | 10.1787/9918e1fb-en |
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| spelling | Okubo, Tomoya VerfasserIn aut AI scoring for international large-scale assessments using a deep learning model and multilingual data Tomoya, Okubo ... [et al] Paris OECD Publishing 2023 1 Online-Ressource (34 p.) 21 x 28cm. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OECD Education Working Papers no.287 Artificial Intelligence (AI) scoring for constructed-response items, using recent advancements in multilingual, deep learning techniques utilising models pre-trained with a massive multilingual text corpus, is examined using international large-scale assessment data. Historical student responses to Reading and Science literacy cognitive items developed under the PISA analytical framework are used as training data for deep learning together with multilingual data to construct an AI model. The trained AI models are then used to score and the results compared with human-scored data. The score distributions estimated based on the AI-scored data and the human-scored data are highly consistent with each other; furthermore, even item-level psychometric properties of the majority of items showed high levels of agreement, although a few items showed discrepancies. This study demonstrates a practical procedure for using a multilingual data approach, and this new AI-scoring methodology reached a practical level of quality, even in the context of an international large-scale assessment. Education Science and Technology Houlden, Wayne MitwirkendeR ctb Montuoro, Paul MitwirkendeR ctb Reinertsen, Nate MitwirkendeR ctb Tse, Chi Sum MitwirkendeR ctb Bastianic, Tanja MitwirkendeR ctb FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/9918e1fb-en Volltext |
| spellingShingle | Okubo, Tomoya AI scoring for international large-scale assessments using a deep learning model and multilingual data Education Science and Technology |
| title | AI scoring for international large-scale assessments using a deep learning model and multilingual data |
| title_auth | AI scoring for international large-scale assessments using a deep learning model and multilingual data |
| title_exact_search | AI scoring for international large-scale assessments using a deep learning model and multilingual data |
| title_full | AI scoring for international large-scale assessments using a deep learning model and multilingual data Tomoya, Okubo ... [et al] |
| title_fullStr | AI scoring for international large-scale assessments using a deep learning model and multilingual data Tomoya, Okubo ... [et al] |
| title_full_unstemmed | AI scoring for international large-scale assessments using a deep learning model and multilingual data Tomoya, Okubo ... [et al] |
| title_short | AI scoring for international large-scale assessments using a deep learning model and multilingual data |
| title_sort | ai scoring for international large scale assessments using a deep learning model and multilingual data |
| topic | Education Science and Technology |
| topic_facet | Education Science and Technology |
| url | https://doi.org/10.1787/9918e1fb-en |
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