International Journal of
Information and Education Technology

Abstract

Dysgraphia is a learning disability that makes writing and handwriting much more difficult. Traditional remediation methods that target only single aspects, such as motor skills or letter tracing, do not capture the multidimensional nature of this disability. This research aims to propose and validate a new intervention framework, the Dysgraphic Children Handwriting Model (DCHM), that consists of three principles: visualization, imagination, and automation. A mixed-methods approach was employed; the study focused on 50 dysgraphic students (age 8–12 years) whose handwriting quality was evaluated pre- and post-intervention through the Handwriting Legibility Scale (HLS). The DCHM is based on interactive, technology-assisted prototype applications that provide animated guidance on letter formation, timely feedback, and gamified activities to improve literacy engagement. Results revealed substantial improvements: 63.0% in alignment, 60.7% in letter formation, and 58.6% in overall legibility. Qualitative results included increased student motivation, decreased frustration, and greater confidence around writing tasks, backed up by positive teacher feedback validating the model’s usability and scalability. These findings underscore the DCHM’s efficacy as a comprehensive, scalable solution for dysgraphia. By bridging cognitive, motor, and visual strategies, this research contributes to the field of special education, paving the way for further exploration of adaptive technologies to support diverse learning needs.