Authors : S Zindove; S Chaputsira

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/4sze97pp

Scribd : https://tinyurl.com/wznfcv2k

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1710

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Automated grading of short answer questions is a challenging task that involves understanding and evaluating free-text responses. This research presents an innovative model that combines the capabilities of the language model all-mpnet-base-v2 with a machine learning-based lenience adjustment mechanism to enhance the accuracy and fairness of automated grading systems. The proposed model utilizes all-mpnet-base-v2 for natural language understanding and feature extraction from student responses. To address the variability in acceptable answers and provide a fair grading system, a machine learning-based model is integrated to adjust the level of lenience dynamically. This dual approach ensures that the grading system can handle a wide range of responses while maintaining consistency and reliability. The experimental results demonstrate that the combination of all-mpnet-base-v2 with the lenience adjustment model significantly improves grading accuracy compared to traditional methods. This model represents a significant advancement in the field of educational technology, offering a robust solution for automated grading systems that can adapt to diverse educational contexts and requirements.

Keywords : All-Mpnet-Base-V2, Lenience, Convolutional Neural Networks, Pretrained Models.

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