JOURNAL ARTICLE
Design and construction of cell engineering curriculum quality evaluation system for portfolio assessment.
Published In: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.), 2024, v. 24, n. 6. P. 3714 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yu, Xianghua; Shao, Jinhua; Zhang, Yong; Liao, Yang; Li, Huizhi 3 of 3
Abstract
The article focuses on the design and construction of a curriculum quality evaluation system for a cell engineering course based on portfolio assessment, aligned with China's "New Engineering" initiative and engineering education certification standards. Using the VALUE (Valid Assessment of Learning in Undergraduate Education) rubrics as a reference, the authors developed a multi-dimensional evaluation framework emphasizing learning ability, problem analysis ability, and knowledge application ability, with detailed scoring criteria for discussions, coursework quality, and peer evaluations. The system integrates formative, diagnostic, and summative assessments, adjusting traditional grading structures to prioritize continuous performance and comprehensive skill development. Analysis of student assessment data demonstrated improvements in learning engagement and work quality, while highlighting areas for further enhancement. This evaluation model aims to provide a standardized, localized reference for engineering education quality assessment in Chinese higher education institutions.
Additional Information
- Source:Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). 2024/11, Vol. 24, Issue 6, p3714
- Document Type:Article
- Subject Area:Education
- Publication Date:2024
- ISSN:1472-7978
- DOI:10.1177/14727978241299181
- Accession Number:182615049
- Copyright Statement:Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. 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.)
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