Automatic Correction of Free Format MCQ Tests

Habeek, Muaaz; Dridi, Charaf Eddine; Badeche, Mounira

doi:10.4018/ijsi.2020010103

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…However, due to the lack of infrastructure and maintenance, online assessment under such conditions cannot yet be applied in all countries of the world and traditional paper-based assessment still has a bright future ahead. Multiple forms for automatic grading of MCQ are available [20] [21]. In the exam database, each candidate name is associated with a special code called an anonymity number which is pending the corresponding grade.…”

Section: Application In the Educational Fieldmentioning

confidence: 99%

A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

Noulamo,

Djimeli-Tsajio,

Kameugne

et al. 2023

WJET

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Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-making, understanding, learning, etc. This work presents an approach to designing a generic Intelligent Agent that can be used in a multi-agent system to solve a complex problem. The generic agent that is proposed can be instantiated as a concrete agent, which is enabled with learning and autonomy capabilities by using Artificial Neural Networks. To highlight the generic aspect, the proposition is instantiated to be used in agriculture, health and education. The instantiated software agent applied in agriculture can process images in real time and detect defect on plants' leaf. In the health field, the agent process image to diagnose breast cancer. When applied in Education, the agent can load an image of a student's script and grade it. The performance of the designed agent system has the same accuracy as that of the respective neural networks used to instantiate them. In the educational field, the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the agricultural field, it has an accuracy of 97.2%.

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Section: Application In the Educational Fieldmentioning

confidence: 99%

A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

Noulamo,

Djimeli-Tsajio,

Kameugne

et al. 2023

WJET

View full text Add to dashboard Cite

show abstract

“…To reduce human intervention in the correction processes, how to automate the correction of a traditional paper evaluation and automatically publish the results as in the case of an online evaluation? Multiple forms for automatic correction of MCQ are available [1], [2]. In the exam database, each candidate's name is associated with a special code called an anonymity number which is pending the corresponding grade.…”

Section: Introductionmentioning

confidence: 99%

Local Fourier features for handwriting digit images classification

Alain Bernard,

Thierry,

Jean-Pierre

et al. 2024

IJECE

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Multiple choice questions (MCQ) are effective in normative assessment and offline testing is still relevant due to the lack of efficient mass infrastructures and maintenance. For the automatic correction of MCQ paper form and reporting of the grade, it is generally necessary to read and recognize a handwriting digit in a box. This paper focuses on local feature extraction in the frequency domain using Fourier transform. The pre-process begins with the extraction of the fields from the entity map, followed by the application of 2D fast Fourier transform (2DFFT) and the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The experimental results of the Modified National Institute of Standards and Technology (MNIST) handwriting digits dataset show that the local characteristics extracted in the frequency domain used as input to a support vector machine (SVM) classifier are efficient in terms of 99.51% accuracy. The proposed system successfully helped in the reporting of all the marks for seven subjects in a class of 98 students during the automatic correction of the MCQ exam papers.

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Design of Automatic Correction System for English Pronunciation Errors based on HMM Algorithm

Tian

2023

2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC)

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Automatic Correction of Free Format MCQ Tests

Cited by 3 publications

References 16 publications

A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

Local Fourier features for handwriting digit images classification

Design of Automatic Correction System for English Pronunciation Errors based on HMM Algorithm

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