Literature DB >> 19432056

Handwriting performance, self-reports, and perceived self-efficacy among children with dysgraphia.

Batya Engel-Yeger1, Limor Nagauker-Yanuv, Sara Rosenblum.   

Abstract

OBJECTIVE: This study examined the relationships between children's self-reports on their handwriting performance, their actual handwriting process and product, and wider motor-perceived self-efficacy.
METHOD: Twenty-one children with dysgraphia and 21 typically developing children copied a paragraph on an electronic tablet as part of a Computerized Penmanship Evaluation Tool. Handwriting product was evaluated by the Hebrew Handwriting Evaluation. Participants completed the Children's Questionnaire for Handwriting Proficiency (CHaP) and the Perceived Efficacy and Goal Setting System (PEGS).
RESULTS: The study group's CHaP scores significantly correlated with handwriting process, product measures (rs = .46 - .59, ps = .034 - .005), and PEGS scores, all of which were significantly poorer compared with those of the control participants.
CONCLUSIONS: Children are aware of their handwriting deficits and are able to report them. Children's reports may contribute to the identification of dysgraphia and facilitate their participation in occupational therapy intervention and in class.

Entities:  

Mesh:

Year:  2009        PMID: 19432056     DOI: 10.5014/ajot.63.2.182

Source DB:  PubMed          Journal:  Am J Occup Ther        ISSN: 0272-9490


  4 in total

Review 1.  Review of occupational therapy research in the practice area of children and youth.

Authors:  Roxanna M Bendixen; Consuelo M Kreider
Journal:  Am J Occup Ther       Date:  2011 May-Jun

2.  Handwriting measures as reflectors of executive functions among adults with Developmental Coordination Disorders (DCD).

Authors:  Sara Rosenblum
Journal:  Front Psychol       Date:  2013-06-26

3.  Inter-relationships between objective handwriting features and executive control among children with developmental dysgraphia.

Authors:  Sara Rosenblum
Journal:  PLoS One       Date:  2018-04-24       Impact factor: 3.240

4.  Dysgraphia detection through machine learning.

Authors:  Peter Drotár; Marek Dobeš
Journal:  Sci Rep       Date:  2020-12-09       Impact factor: 4.379

  4 in total

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