Use of formative assessment to improve the online teaching materials content quality
Abstract
Due to the evolution of new information technologies, several courses have been made available on the web via educational platforms, websites, or educational applications. But there is no follow-up for the detection of faults in these online courses which allows the improvement of their quality. In this study, we used online formative assessment to provide useful information to writers of teaching materials to improve their quality and to identify areas for improvement. Based on the learners' responses, we calculated the failure rate per unit, per question and the frequency of false answers per question, which allowed us to detect faults in course material, and to ensure from the effectiveness of the corrective measures carried out by the teaching materials editors based on the information we have talked about, we calculated the failure rate by version of the teaching materials.
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Various critical teachers' prerequisites-knowledge and skills, social factors, and psychological factors-for formative assessment are found useful for improving the quality of teaching and learning.
“Various critical teachers' prerequisites-knowledge and skills, social factors, and psychological factors-for formative assessment are found useful for improving the quality of teaching and learning [40,41].”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [41]
Cites
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A proposed model for evaluating the success of WebCT course content management system
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A Model for Evaluating E-Learning Systems Quality in Higher Education in Developing Countries
Hadullo, K., Oboko, R. O., & Omwenga, E. (2017) · The International Journal of Education and Development using Information and Communication Technology (The University of the West Indies) · Cited by 79 · see citing works
An approach for LMS assessment
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Multicriteria analysis of e-learning courses
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Responsive, Formative Evaluation : A Flexible Means for Improving Distance Learning Materials
Thompson, R. D. (1987) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 3 · see citing works
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Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education
Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/5215722 · Cited by 444 · see citing works
A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning
Ge, D., Wang, X., & Liu, J. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i03.20471 · Cited by 37 · see citing works
Peran Pembelajaran Daring, Komunikasi, Dan System E-Learning Terhadap Motivasi Belajar Mahasiswa
Fambudianto, A. R., Amriza, R. N. S., & Kusuma, D. J. A. (2022) · Indonesian Journal of Computer Science · doi:10.33022/ijcs.v11i2.3049
- Authors
- Zarqtouni, M., Haddi, A., & Allali, H.
- Affiliations
- Université Hassan 1er (MA)
- Venue
- Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications
- Publisher
- ACM
- Published
- 2020-09-23
- Pages
- 1-5
- OpenAlex
- W3101515419
- Cited by
- 3
- References
- 6
- Citations by year
- 2022: 2 · 2021: 1
- FWCI
- 0.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Systems: Theories and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Keywords
- formative assessment, online teaching materials, course content quality, online courses, student response, failure rate analysis, question difficulty
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