A Framework for Efficient and Accurate Automated CLO and PLO Assessment

Authors

  • Hafedh Mahmoud Zayani Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia
  • Walid Abdelfattah Department of Mathematics, College of Arts and Science, Northern Border University, Saudi Arabia
  • Rahma Sellami Department of Computer Science, Applied College, Northern Border University, Saudi Arabia
  • Jihane Ben Slimane Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Amani Kachoukh Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13362-13368 | April 2024 | https://doi.org/10.48084/etasr.6846

Abstract

Accurate and efficient learning outcome assessment is crucial for ensuring high-quality education, but traditional methods can be time-consuming, error-prone, and inconsistent. We developed a novel Excel Macro-enabled framework for automating the evaluation of Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) in higher education. The framework consists of two Excel Macro-enabled workbooks. The course section workbook guides instructors through the assessment process, automatically calculates CLO achievement levels, and generates reports for the coordinators and the Head of Department (HoD). The course-level workbook aggregates data from all course sections and calculates CLO and PLO achievement levels relative to the course. Proven successful in three FCIT (Faculty of Computer and Information Technology) programs at NBU (Northern Border University), the framework demonstrably reduces assessment time and errors, improves consistency, and facilitates data-driven program improvement, making it a valuable tool for enhancing program quality.

Keywords:

Course learning outcomes (CLOs), Program Learning Outcomes (PLOs), assessment automation, Excel macro-enabled workbooks, higher education, accreditation

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How to Cite

[1]
H. M. Zayani, W. Abdelfattah, R. Sellami, J. Ben Slimane, and A. Kachoukh, “A Framework for Efficient and Accurate Automated CLO and PLO Assessment”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13362–13368, Apr. 2024.

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