Enabling secure and inclusive education for students with disabilities and ensuring data through machine learning

Abstract

The COVID-19 pandemic precipitated an abrupt transition to online learning, impacting students with disabilities uniquely. This study examines the experiences of 62 such students in the new educational paradigm, employing a mixed-methods approach. Quantitative data were collected through surveys and questionnaires to assess privacy and security concerns arising from online learning tools. Qualitative insights were gathered via interviews and focus groups, revealing that while students appreciate the flexibility of online learning, they express a critical need for enhanced guidance and support. Neurodiverse students, in particular, emphasized the necessity of a secure online environment. Addressing these challenges, our research integrates blockchain and machine learning technologies to enhance biometric authentication. Specifically, the Highly Secure Blockchain-Based Compressive Sensing (HSBCS) system is proposed, ensuring data integrity and improving accessibility for Personal Records. Preliminary testing of the HSBCS system showed promising results, with an average accuracy rate of 95% in biometric authentication among visually impaired students. Moreover, participants reported a 30% increase in perceived security and ease of access to their Personal Records compared to traditional authentication methods. These findings underscore the potential of integrating advanced technologies to meet the unique educational needs of students with disabilities while enhancing data security and accessibility in online learning environments.

Keywords

• Online learning
• Quantitative Data
• Highly Secure Blockchain-Based Compressive Sensing (HSBCS) system
• Authentication Methods.
• personal records security
• emerging technologies in education