We study the automatic identification of educational items worthy of content authors’ attention. Based on
the 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 complexity
measures, item discrimination, and various forms of content representation. We analyze the potential
usefulness of these properties using both simulation and analysis of real data from a large-scale learning
environment. We also describe two case studies where we practically apply the identification of attention-worthy
items. Based on the analysis and case studies, we provide recommendations for practice and
impulses for further research.
How to Cite
learning environment, outliers, anomaly detection, interpretability, reliability, difficulty, content analysis, attention-worthiness
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