From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education

Autor/inn/en	Yang, Albert C. M.; Chen, Irene Y. L.; Flanagan, Brendan; Ogata, Hiroaki
Titel	From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education
Quelle	In: Educational Technology & Society, 24 (2021) 1, S.164-175 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Grading; Computer Assisted Testing; Automation; Artificial Intelligence; Electronic Publishing; Books; Learning Analytics; Notetaking; Scores; Independent Study; Reading Skills; Time Management; Reading Comprehension; Accounting; Graduate Students + Suchen Sie Ihr Suchwort? Notengebung; Schulnote; Künstliche Intelligenz; Elektronisches Publizieren; Book; Buch; Monographie; Monografie; Selbststudium; Reading skill; Lesefertigkeit; Zeitmanagement; Leseverstehen; Abrechnung; Buchführung; Buchhaltung; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin
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. (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01