Feasible and Robust Indicators for Technology- and Analytics-enhanced Assessment Environments

• In Event: ID - Breakthroughs in Technology-Supported Assessment

Short Description

An initial step toward reliable and valid technology- and analytics-enhanced assessment environments is to determine the indicators that are related to the intended learning process or outcome. This study presents a use case where Computer Classification Testing was utilized to classify the learners into two or more categories. Principal Component Analysis of different datasets identified feasible and robust indicators across different domains. Determining such indicators supports the evidence-based design and development of assessment analytics systems.

Authors

• Contributor: Muhittin Sahin, University of Mannheim

• Contributor: Dirk Ifenthaler, University of Mannheim