期刊
ELECTRONICS
卷 10, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/electronics10060727
关键词
education and learning; data analytics; bloom taxonomy; assessment; course learning outcomes; student performance
资金
- Prince Sultan University, Saudi Arabia [RIC2020-18]
The study explores the evaluation of course learning outcomes in the context of engineering and business education using the Rasch model. It demonstrates that the Rasch model can effectively categorize students based on their educational knowledge levels.
The literature on engineering education research highlights the relevance of evaluating course learning outcomes (CLOs). However, generic and reliable mechanisms for evaluating CLOs remain challenges. The purpose of this project was to accurately assess the efficacy of the learning and teaching techniques through analysing the CLOs' performance by using an advanced analytical model (i.e., the Rasch model) in the context of engineering and business education. This model produced an association pattern between the students and the overall achieved CLO performance. The sample in this project comprised students who are enrolled in some nominated engineering and business courses over one academic year at Prince Sultan University, Saudi Arabia. This sample considered several types of assessment, such as direct assessments (e.g., quizzes, assignments, projects, and examination) and indirect assessments (e.g., surveys). The current research illustrates that the Rasch model for measurement can categorise grades according to course expectations and standards in a more accurate manner, thus differentiating students by their extent of educational knowledge. The results from this project will guide the educator to track and monitor the CLOs' performance, which is identified in every course to estimate the students' knowledge, skills, and competence levels, which will be collected from the predefined sample by the end of each semester. The Rasch measurement model's proposed approach can adequately assess the learning outcomes.
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