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A MOOC-Based Hybrid Teaching Model of College English.

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Title: A MOOC-Based Hybrid Teaching Model of College English.
Authors: Tan Feng1 18407274@masu.edu.cn, Abualigah, Laith2,3,4
Source: International Journal of Emerging Technologies in Learning. 2023, Vol. 18 Issue 2, p50-66. 17p.
Subject Terms: *Teaching models, *College teaching, *Internet in education, *College students, *Universities & colleges, *Bayesian analysis, Right to be forgotten
Abstract: In the era of intelligence, Internet + technology is widely used in various fields, and English Teaching in the education industry of colleges and universities gradually tends to be an online and offline mixed teaching mode. However, under the MOOC model, the feedback of College Students' English learning and the recognition of their knowledge level has become new difficulties. Aiming at the feedback of students' learning situation under the mixed mode of College English teaching, this paper uses the optimized Bayesian knowledge tracking model (BKTM) to predict students' English learning situation and introduces students' learning behavior and forgetting behavior to optimize parameters. Finally, a performance verification experiment is carried out by analyzing the students' answer performance in College English mixed teaching. The results show that the prediction errors of the four knowledge points of 60 students in the two classes are all about 7%, and the maximum error is 11%. Experiments show that the model has high accuracy and stable performance in predicting the probability of mastering knowledge points. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: A MOOC-Based Hybrid Teaching Model of College English.
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  Data: <searchLink fieldCode="AR" term="%22Tan+Feng%22">Tan Feng</searchLink><relatesTo>1</relatesTo><i> 18407274@masu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Abualigah%2C+Laith%22">Abualigah, Laith</searchLink><relatesTo>2,3,4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2023, Vol. 18 Issue 2, p50-66. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Teaching+models%22">Teaching models</searchLink><br />*<searchLink fieldCode="DE" term="%22College+teaching%22">College teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Internet+in+education%22">Internet in education</searchLink><br />*<searchLink fieldCode="DE" term="%22College+students%22">College students</searchLink><br />*<searchLink fieldCode="DE" term="%22Universities+%26+colleges%22">Universities & colleges</searchLink><br />*<searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Right+to+be+forgotten%22">Right to be forgotten</searchLink>
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  Data: In the era of intelligence, Internet + technology is widely used in various fields, and English Teaching in the education industry of colleges and universities gradually tends to be an online and offline mixed teaching mode. However, under the MOOC model, the feedback of College Students' English learning and the recognition of their knowledge level has become new difficulties. Aiming at the feedback of students' learning situation under the mixed mode of College English teaching, this paper uses the optimized Bayesian knowledge tracking model (BKTM) to predict students' English learning situation and introduces students' learning behavior and forgetting behavior to optimize parameters. Finally, a performance verification experiment is carried out by analyzing the students' answer performance in College English mixed teaching. The results show that the prediction errors of the four knowledge points of 60 students in the two classes are all about 7%, and the maximum error is 11%. Experiments show that the model has high accuracy and stable performance in predicting the probability of mastering knowledge points. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3991/ijet.v18i02.35535
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        Text: English
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      – SubjectFull: Teaching models
        Type: general
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      – SubjectFull: Internet in education
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      – SubjectFull: College students
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      – SubjectFull: Universities & colleges
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      – SubjectFull: Bayesian analysis
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      – SubjectFull: Right to be forgotten
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              Text: 2023
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