Towards Design-Loop Adaptivity: Identifying Items for Revision (Journal of Educational Data Mining, 2022)
We study the automatic identification of educational items worthy of content authors’ attention. Based onthe results of such analysis, content authors can revise and improve the content of learning environments.We provide an overview of item properties relevant to this task, including difficulty and complexitymeasures, item discrimination, and various forms of content representation. We analyze the potentialusefulness of these properties using both simulation and analysis of real data from a large-scale learningenvironment. We also describe two case studies where we practically apply the identification of attention-worthyitems. Based on the analysis and case studies, we provide recommendations for practice andimpulses for further research.