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Title: From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education
Authors: Yang, M. C. Albert
Y, Irene
Chen, L.
Flanagan, Brendan
Ogata, Hiroaki  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-5216-1576 (unconfirmed)
Author's alias: 緒方, 広明
Keywords: Text summarization
Marker grading
Self-regulated learning
Precision education
Text marking
Issue Date: 2021
Publisher: International Forum of Educational Technology & Society
Journal title: Educational Technology & Society
Volume: 24
Issue: 1
Start page: 164
End page: 175
Abstract: Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students’ learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students’ self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students’ learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students’ text marking skill and facilitating precision education.
Rights: This article of the journal of Educational Technology & Society is available under Creative Commons CC-BY-NC-ND 3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Journal Editors at ets.editors@gmail.com.
URI: http://hdl.handle.net/2433/261246
Related Link: https://www.j-ets.net/collection/published-issues/24_1
Appears in Collections:Journal Articles

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