Abstract: In recent years, advancements in Natural Language Processing (NLP) techniques have revolutionized the field of accessibility and exclusivity of testing, particularly for visually impaired individuals. CBT has shown in years back its relevance in terms of administering exams electronically, making the test process easier, providing quicker and more accurate results, and offering greater flexibility and accessibility for candidates. Yet, its relevance was not felt by the visually impaired students as they cannot access printed documents. Hence, in this paper, we present an NLP-driven Computer-Based Test guide for visually impaired students where the NLP-driven Computer-Based Test guide employs state-of-the-art machine learning algorithms to provide real-time assistance and support to visually impaired students. The system utilizes optical character recognition (OCR) technology to convert the text-based questions and the options and also uses automatic speech recognition (ASR) technology to convert spoken words into text-based in a machine-readable format. Subsequently, the NLP model processes the converted text, enabling the system to comprehend and analyze the content. The system uses a pre-trained model to interpret the spoken answers and provides instant feedback to the students, validating their responses and guiding them through the test. The methodology adopted for this system is Object Oriented Analysis and Design Methodology (OOADM) where Objects are discussed and built by modeling real-world instances. To validate that the system is not perverse, the system is further evaluated to test for accuracy using sample labels (A, B, C, D, E, F, G) to compare with the voice recordings obtained from 20 visually impaired students which is been predicted by the system to attain values for precision, recall, and F1-scores. These metrics are used to assess the performance of the model and have indicated that this model is proficient enough to give its better performance to the evaluated system.

Keywords: Natural Language Processing (NLP), Computer-Based Test (CBT), Visual Impairment, Multiple-Choice Question, MCQ, Screen reader.

Works Cited:

Tubo, Faustinah Nemieboka, Ikechukwu E. Onyenwe, Doris C. Asogwa " DEVELOPMENT OF AN NLP-DRIVEN COMPUTER-BASED TEST GUIDE FOR VISUALLY IMPAIRED STUDENTS", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 1-10, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12901

PDF | DOI: 10.17148/IJARCCE.2023.12901

Open chat
Chat with IJARCCE