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¶

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? On the JRL dataset, the proposed model converges faster than conventional ones and elevates the grading process up to 30% at the very beginning of the iteration. → 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 comparison on the just-right-length (JRL) scenario of the UNT dataset shown in Fig. 4(b). The article reports the proposed model "elevates the grading process for AGSs up to 30% at the very b eginning of the iteration".

"Instead of cutting the answers, the proposed model not only converges faster than conventional ones but also elevates the grading process for AGSs up to 30% at the very b eginning of the iteration."

Discussion¶

Related Claims¶

• 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 — possibly the same claim (merge candidate)
• AGE reduces teacher grading burden by requiring only 1% of student answers in the UNT dataset to be graded — related
• On the mismatch-grade dataset, all models without the voting mechanism diverge, while AGE with VM estimates student answers increasingly precisely — related