Design and Implementation of Intelligent Scoring System for Handwritten Short Answer Based on Deep Learning

Lin, Yubei; Zheng, Liubin; Chen, Feng; Sun, Song Yung; Lin, Zilong; Chen, Pingping

doi:10.1109/icaiis49377.2020.9194943

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Short answer Python, LSTM with GWO ITD-AATSU [14] 2021. Short answer Python, NLP, LSTM SSSC-UTG/CMIE-GUCM [15] 2020. Short answer Python, Semantic Matching, CNN…”

Section: Related Workmentioning

confidence: 99%

“…The chosen systems satisfying the previously given criteria were developed at the School of Electrical Engineering, University of Belgrade (SEE-UB) [8], School of Engineering, Edith Cowan University (SE-ECU) [9], Department of Telematic Engineering, University Carlos III of Madrid (DTE-UCM) [10], School of Electronic and Information Engineering, Foshan University (SEIE-FU) [11], Prince Mohammad bin Fahd University (PMFU) [12], Artificial intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University (FCAI-BU) [13], Information Technologies Division, Adana Alparslan Turkes Science and Technology University (ITD-AATSU) [14], School of Software South China, University of Technology Guangzhou and College of Medical Information Engineering, Guangzhou University of Chinese Medicine (SSSC-UTG/CMIE-GUCM) [15].…”

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confidence: 99%

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Software System for Automatic Grading of Paper Tests

Jocovic,

Nikolic,

Bacanin

2023

Electronics

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The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.

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“…Short answer Python, LSTM with GWO ITD-AATSU [14] 2021. Short answer Python, NLP, LSTM SSSC-UTG/CMIE-GUCM [15] 2020. Short answer Python, Semantic Matching, CNN…”

Section: Related Workmentioning

confidence: 99%