Development of an NLP-driven computer-based test guide for visually
  impaired students
Tubo Faustinah Nemieboka Ikechukwu E. Onyenwe Doris C. Asogwa
In recent years, advancements in Natural Language Processing (NLP) techniques
have revolutionized the field of accessibility and exclusivity of testing,
particularly for visually impaired students (VIS). 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. It employs a speech technology
pre-trained methods to provide real-time assistance and support to visually
impaired students. The system utilizes NLP technologies to convert the
text-based questions and the associated options in a machine-readable format.
Subsequently, the speech technology pre-trained model processes the converted
text enabling the VIS to comprehend and analyze the content. Furthermore, we
validated that this pre-trained model is not perverse by testing for accuracy
using sample audio datasets labels (A, B, C, D, E, F, G) to compare with the
voice recordings obtained from 20 VIS 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 pre-trained model and have indicated that it is
proficient enough to give its better performance to the evaluated system. 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.