Expert evaluation found the hybrid system's generated MCQ stems promising in usefulness and grammatical correctness¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Expert evaluation of the generated MCQ stems showed they were promising in usefulness and grammatical correctness. → Praveen Kumar 2024

Evidence¶

Praveen Kumar 2024¶

Praveen Kumar, A., Nayak, A., Shenoy K., M., Chaitanya, & Ghosh, K. (2024). A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning Techniques. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00333-6

q2 · i? · design · r2

The article reports an expert evaluation of the MCQ stems produced by the hybrid OBT+MBT system, finding them "promising in terms of their usefulness and grammatical correctness". No effect sizes or test statistics are printed in the available text, so the result is qualitative in strength.

"Expert evaluation of the resultant MCQ stems demonstrated that they were promising in terms of their usefulness and grammatical correctness."

Discussion¶

Related Claims¶

• Ontology-based generation produced exam-appropriate case-based MCQs, with 30% of a stratified sample rated appropriate by both expert raters and 50% by at least one — related
• MCQs generated by the generate-then-validate SLM pipeline are generally answerable and unambiguous, as judged by seven human experts — related
• Prior AQG work shows ML yields grammatically correct Cloze questions while ontology approaches struggle with Cloze but produce meaningful Wh-type questions — related