AGE reduces teacher grading burden by requiring only 1% of student answers in the UNT dataset to be graded¶

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? The proposed model lessens teacher duties by only grading 1% of all student answers in the UNT dataset. → Mohammad Iqbal 2024

Evidence¶

Mohammad Iqbal 2024¶

Mohammad Iqbal, Tsamara Rana Nugraha and Hsing-Kuo Pao. (2024). Active Grade Estimator on Short Answer Assessment. https://doi.org/10.21203/rs.3.rs-5651820/v1

q2 · i? · design · r2

Numerical result from the real ASAG system evaluation on the UNT dataset (Fig. 7). The article reports AGE "lessens teacher duties by only grading 1% of all student answers in the UNT dataset".

"In other words, the proposed model lessens teacher duties by only grading 1% of all student answers in the UNT dataset."

Discussion¶

Related Claims¶

• Instead of cutting answers, the proposed model converges faster than conventional AL and elevates grading up to 30% at the very beginning on the JRL dataset — related
• GPT-4 surpasses other LLMs in the AGE query selection with an accuracy rate 15% better — related
• The two-stage query with SCS elevates grading process up to 40% on the LL dataset and up to 30% at the beginning on the JRL dataset compared to conventional AL — related
• On the SciC classification task, AGE achieves more than 80% accuracy after roughly the first 30 graded student answers