Model-Driven Framework for Evaluating Learning Outcomes Process

Abstract

Evaluating learning outcomes in academic institution can be complex and challenging. Several quantitative and qualitative assessment approaches have been adopted to enhance the process of managing, measuring and visualizing the learning outcomes. The difficulty of implementing and analyzing the evaluation process is mainly caused by the nature of the raw data used in assessment. The data is usually unstructured, complex, text-heavy and collected in high volumes. It may also be extracted from heterogeneous platforms and require privileged accessibility. Using paper-based assessment, such as rubric, in complex evaluation process may cause error prone, confusion in analyzing the learning outcomes and subject to different interpretations of the assessment by academic constituencies. In this study, we propose a model-driven framework for evaluation process of the learning outcomes. The framework has four activities: The data collection and data processing activities are used to extract complex data into a useful information for assessment. The model-driven assessment activity is used to generate and analyze goal models of the learning outcomes in a formal way and allows the assessment at different level of academic institutions. Finally, the evaluation reporting activity is used to generate reports that summarizes the institutional status, metrics and real-time data in a form visual object. A prototype implementation of the framework is evaluated using a case study of an ongoing project at Al-Ahliyya Amman University.