Summary of Development Of An Nlp-driven Computer-based Test Guide For Visually Impaired Students, by Tubo Faustinah Nemieboka et al.
Development of an NLP-driven computer-based test guide for visually impaired students
by Tubo Faustinah Nemieboka, Ikechukwu E. Onyenwe, Doris C. Asogwa
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The presented research develops an NLP-driven Computer-Based Test guide for visually impaired students, revolutionizing accessibility and inclusivity in testing. By employing speech technology pre-trained methods, the system provides real-time assistance and support to VIS, converting text-based questions into machine-readable formats. The pre-trained model processes the converted text, enabling VIS to comprehend and analyze content. The study validates the model’s accuracy using sample audio datasets and obtains promising results for precision, recall, and F1-scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a computer-based test guide for visually impaired students, making exams more accessible. It uses special technology to help students with vision loss understand and answer questions. This system is like a translator that converts written text into a format the student can understand, helping them take tests more easily. The research also tested how well this system works and found it performs well in terms of accuracy. |
Keywords
» Artificial intelligence » Nlp » Precision » Recall
