2026-10-12T07:49:48Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33970426 2026-09-23T04:42:39Z category_129 portal_316 item_type_3 month_year_09_2026

Table 1_Process-based digital assessment of children’s handwriting: a scoping review of graphomotor, orthographic, and educational indicators.xlsx Siyuan Liu (696701) Norsafinar Rahim (25093969) Sai Ying (25093972) Education children digital handwriting assessment dysgraphia educational assessment graphomotor development handwriting kinematics process data scoping review Background<p>Handwriting is central to children's written-language development, but conventional assessment usually emphasizes the completed product and provides limited information about the processes that produced it. Digital technologies can capture handwriting as an unfolding motor and orthographic activity.</p>Objective<p>This scoping review mapped technologies for capturing children's handwriting process data, the raw signals and derived features obtained, the assessment constructs supported by process measures, and their educational, developmental, and clinical applications.</p>Methods<p>Following scoping-review guidance and PRISMA-ScR, we searched Scopus, PubMed, ERIC, Web of Science Core Collection, and IEEE Xplore and conducted backward citation searching of 11 relevant review or framework articles. Of 198 reports sought for full-text assessment, one could not be retrieved, 197 were assessed, 39 were excluded, and 158 were included. All included reports were charted with the same 16-field framework. Independent verification by a second researcher covered randomly selected samples of 40 of 197 full-text eligibility decisions (20.3%; 97.5% agreement; Cohen's κ = 0.918) and 32 of 158 extractions (20.3%).</p>Results<p>Digitizing or graphics tablets were used in 136 reports (86.1%), and 117 (74.1%) used Wacom hardware. Position or trajectory (145, 91.8%) and time or timestamps (124, 78.5%) were the most common raw signals, followed by pen pressure or normal force (82, 51.9%) and pen-state or in-air information (71, 44.9%). Common derived feature families included speed or velocity (97, 61.4%), temporal or duration measures (84, 53.2%), spatial or geometric measures (67, 42.4%), and pressure- or force-derived measures (59, 37.3%). Developmental or normative characterization (75, 47.5%) and clinical characterization or differential assessment (74, 46.8%) were the most frequent applications. Latin-alphabetic handwriting appeared in 114 reports (72.2%), followed by Hebrew (23, 14.6%), Chinese (19, 12.0%), and Arabic (5, 3.2%).</p>Conclusions<p>Digital handwriting assessment is well established in process capture but less consistently translated into applied educational or clinical decisions. Classification performance should not be equated with diagnostic validity. A theory-informed and review-refined four-level framework organizes raw signals, derived features, assessment constructs, and empirical interpretation or action without implying a validated developmental or psychometric hierarchy.</p> 2026-09-23T04:42:39Z Dataset Dataset 10.3389/feduc.2026.1923428.s001 https://figshare.com/articles/dataset/Table_1_Process-based_digital_assessment_of_children_s_handwriting_a_scoping_review_of_graphomotor_orthographic_and_educational_indicators_xlsx/33970426 CC BY 4.0