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Communication Dans Un Congrès Année : 2023

Monitoring automatically gained difficulty rankings with mathematics educational theories and experts

Résumé

Automatically difficulty-ranked tasks would benefit technology-enhanced learning in mathematics, opening adaptive testing for a broader audience. How to achieve this goal in a resource-saving way and guarantee high-ranking quality? This paper follows a community approach for calibration based on the Elo-Rating-System and seeks an instrument to monitor gained task difficulty rankings automatically. Thus, rankings of 18 Algebra-tasks, elaborated following Bloom’s Revised Taxonomy, Webb’s DOK Framework, and Smith & Stein’s LCD, are compared to 5 expert rankings and contrasted to empirical solution frequencies from 64 students in grades 11 and 12. A mixed methods approach will guide the decision for a monitoring instrument for the automatic calibration process implemented in an open test- and trainings-platform based on the GeoGebra classroom containing final exam topics, providing formative assessment and sustaining bridge courses in the STEM fields.
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hal-04410840 , version 1 (22-01-2024)

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  • HAL Id : hal-04410840 , version 1

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Eva-Maria Infanger, Nilay Aral, Edith Lindenbauer, Zsolt Lavicza. Monitoring automatically gained difficulty rankings with mathematics educational theories and experts. Thirteenth Congress of the European Society for Research in Mathematics Education (CERME13), Alfréd Rényi Institute of Mathematics; Eötvös Loránd University of Budapest, Jul 2023, Budapest, Hungary. ⟨hal-04410840⟩

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