Systematic Framework: Profiling Visual Attributes of Visually Impaired Students

This paper employed the System Development Life Cycle (SDLC) to create a visual attribute profile for visually impaired students. The profile, drawn from a substantial dataset, considers age, tasks, and severity of impairments. It is crucial for inclusive education in blind and special schools, aiding teachers and physicians. Demographic information, including age, gender, education, medical records, and low vision aid prescriptions, is gathered. Visual attributes are assessed through comprehensive eye examinations, calculating students' visual demands based on classroom settings. The study follows the Waterfall Model within the SDLC, encompassing stages like requirement gathering and analysis, system design and implementation. Demographic data and visual attributes are integrated into big data, generating output based on calculated demands. Feedback from most of school database users underscores the importance of information on visual characteristics for effective management of visually impaired students. Notably, 67% highlight the current database's lack of such information. Thus, this profiling initiative is a strategic move toward addressing evidence-based visual characteristics and demands, providing valuable insights for educators, optometrists, and clinical instructors to tailor management strategies.