A Moodle Plugin for Handwritten Answer Input, Management, and Recognition Supporting Japanese, English, Mathematical, and Chemical Formulas
Year: 2026
Type: article
Abstract: This study presents the development of a Moodle plugin that integrates a web-based handwriting input interface with handwriting-to-text recognition, supporting handwritten input in Japanese, English, and mathematical and chemical formulas. Although computer-based learning systems are widely used in education, they predominantly emphasize multiple-choice or short-answer question types that require conventional keyboard and mouse input. Conversely, structured responses, such as mathematical and chemical expressions, often require specialized input methods, such as LaTeX and Equation Editor, which are challenging for many learners to use. To address these limitations, we developed a Moodle plugin that provides an intuitive and cognitively efficient handwriting-based input interface. The plugin enables learners to produce handwritten responses naturally while preserving raw handwriting data and recognition texts to support transparent scoring. This study bridges the gap between traditional pencil-and-paper assessments and contemporary digital learning environments. It also enhances the authenticity and interactivity of assessments conducted within Moodle. In particular, this paper describes the architecture of the plugin, implementation, and integration challenges encountered within the Moodle system. Usability testing results indicate the plugin can enhance learner engagement and assessment accuracy, representing a step toward personalized and interactive digital education.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
Awards: Development and evaluation of science and mathematics learning systems through digital transformation: towards transforming the quality of learning, Integration of handwritten descriptive answer recognition and automatic/semi-automatic scoring, machine learning and analysis, Development of assessment in mathematics and science for using advanced digital technology: Toward the cultivation of scientific thinking
OpenAlex ID: W7164831480
