A Grading Data Warehouse Approach to Measuring and Analyzing Learning Performance

Abstract

E-assessment comprises a variety of activities in and beyond the classroom. However, traditional e-learning platforms support only a part of assessment (e.g., individual and group assignments, the grading of such activities, and student record management). Typically, such platforms lack competency orientation, or face performance issues due to increasing application complexity and usage intensity. To overcome technical limitations and provide a basis for competency-based assessment, the authors present an analytics component that is inspired by data warehouses. The potential of this artifact is elaborated, and the improvements are evaluated through a case study about Learn@WU, the LMS of WU Vienna. Although the focus was competency-based aggregation of learning results, early experiences show performance increases for retrieving simple grades of 45% to 98%. Sample scenarios demonstrate how to define and calculate indicators along activity hierarchies and competency graphs to enable the measurement of learning performance along both generic indicators and competency-oriented assessment.