A Framework for Efficient and Accurate Automated CLO and PLO Assessment
Received: 31 December 2023 | Revised: 19 January 2024 | Accepted: 23 January 2024 | Online: 19 February 2024
Corresponding author: Hafedh Mahmoud Zayani
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, accreditationDownloads
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Copyright (c) 2024 Hafedh Zayani, Walid Abdelfattah, Rahma Sellami, Jihane Ben Slimane, Amani Kachoukh
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