Eva-Maria Infanger, Nilay Aral, Edith Lindenbauer, Zsolt Lavicza,
"Monitoring automatically gained difficulty rankings with mathematics educational theories and experts"
: Proceedings of the 13th Congress of European Research on Mathematics Education - CERME-13, 2024
Original Titel:
Monitoring automatically gained difficulty rankings with mathematics educational theories and experts
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 13th Congress of European Research on Mathematics Education - CERME-13
Original Kurzfassung:
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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