Handwriting Difficulty Screening Tool based on Dynamic Data from Drawing Process
Keywords:
Dynamic Attributes, Drawing Process, Handwriting Difficulty, Support Vector Machine,Abstract
Children with handwriting difficulty are advised to join an intervention program to rectify the problem at an early stage. However, the available screening tools suffer from subjectivity judgement while lack of expertise reduces the chance for every student to be screened. Yet, digitalized screening tools that use dynamic data from writing activities are only applicable to those who know the language. These limitations had led this study to develop an objective handwriting difficulty screening tool based on dynamic data of drawings. Three attributes extracted from 120 sets of dynamic data from drawing process were found to be significant in differentiating below-average writers from average writers. The attributes were then used to train Support Vector Machine prediction model. To test the validity and reliability of the prediction model, additional sets of data were acquired from 36 pupils. The performance of the tool was compared with the results from the Handwriting Proficiency Screening Questionnaire (HPSQ) that employs teachers’ observations on pupils’ handwriting ability. With 78% reliability, 69% of the predictions made by the developed tool was in accordance with the teachers’ observation. Most importantly, 53% of the average writers were screened as having handwriting problems. This denotes the objectivity of the developed tool in identifying below-average writers who failed to be recognized through teacher’s observation.References
C. E. Cameron, L. L. Brock, W. M. Murrah, L. H. Bell, S. L. Worzalla, D. Grissmer and F. J. Morrison, “Fine motor skills and executive function both contribute to kindergarten achievement,” Child Development, vol. 83, no. 4, pp. 1229-1244, 2012.
M. J. Hartingsveldt, E. H. C. Cup, J. C. M. Hendriks, L. de Vries, I. J. M. de Groot, and M. W. G. Nijhuis-van der Sanden, “Predictive validity of kindergarten assessments on handwriting readiness,” Research in Developmental Disabilities, vol. 36, pp. 114-124, 2015.
E. Pagliarini, M. T. Guasti, C. Toneatto, E. Granocchio, F. Riva, D. Sarti, B. Molteni, and N. Stucchi, “Dyslexic children fail to comply with the rhythmic constraints of handwriting,” Human Movement Science, vol. 42, pp. 161-182, 2015.
K. P. Feder and A. Majnemer, “Handwriting development, competency and intervention,” Developmental Medicine and Child Neurology, 49, pp. 312-317, 2007.
P. I. Khalid, “Handwriting Ability Assessment Model Using Dynamic Characteristics of Drawing Process,” Ph.D thesis, Universiti Teknologi Malaysia, Malaysia, 2012.
M. J. M. Volman, B. M. Van Schendel, and M. J. Jongmans, “Handwriting difficulties in primary school children: A search for underlying mechanisms,” American Journal of Occupational Therapy, vol. 60, no. 4, pp. 451-460, 2006.
K. E. Beery and N. A. Beery, The Beery-Buktenica Developmental Test of Visual-Motor Integration: Beery VMI Administration, Scoring, and Teaching Manual. 5th. ed. Minneapolis, MN: NCS Pearson, 2004.
K. L. Rostan, J. Hinojosa, and H. Kaplan, “Using the Minnesota Handwriting Assessmnet and Handwriting Checklist in screening first and second graders’ handwriting legibility,” Journal of Occupational Therapy Schools, & Early Intervention, vol. 1, no. 2, pp. 100-115, 2008.
S. Rosenblum, “Development, reliability, and validity of the Handwriting Proficiency Screening Questionnaire (HPSQ),” American Journal of Occupational Therapy, vol. 62, no. 3, pp. 298- 307, 2008.
R. S. Kamini, “Raising the bar for all schools,” New Straits Times, September 24th 2010 [Retrieved December 20, 2010, from http://www.nst.com.my].
L. Hen, N. Josman and S. Rosenblum, “Tele-evaluation and intervention among adolescents with handwriting difficulties – computerized penmanship evaluation tool (ComPET) implementation,” Proceedings of the 7th International Conference on Disability, Virtual Reality, and Associate Technologies (ICDVRAT) with ArtAbilitation, Maia, Portugal, 2008, pp. 143-149.
T. F. Wu, G. S. Chen, and H. S. Lo, “The application of computerbased chinese handwriting assessment system to children with dysgraphia” in ICCHP 2014, Part 2, LNCS, vol. 8548, K. Miesenberger et al., Eds, 2014, Switzerland: Springer International Publishing, pp. 532-539.
P. I. Khalid, J. Yunus, and R. Adnan, “Extraction of dynamic features from hand drawn data for the identification of children with handwriting difficulty,” Research in Developmental Disabilities, vol. 31, pp. 256-262, 2010.
P. I. Khalid, J. Yunus, R. Adnan, M. Harun, R. Sudirman, and N. H. Mahmood, “The use of graphic rules in grade one to help identify children at risk of handwriting difficulties,” Research in Developmental Disabilities, vol. 31, no. 6, pp. 1685-1693, 2010.
M. L. Simner, “The grammar of action, printing, and reversals in children’s printing,” Developmental Psychology, vol. 17, no. 6, pp. 866-871, 1981.
S. Steffani and P. M. Selvester, “The relationship of drawing, writing, literacy and math in kindergarten children,” Reading Horizons, vol. 49, no. 2, pp. 125-142, 2009.
F. Bonoti, F. Vlachos and P. Metallidou, “Writing and drawing performance of school age children. Is there a relationship?” School Psychology International, vol. 26, no. 2, pp. 243-255, 2005.
C. C. Neo, E. L. M. Su, P. I. Khalid and C. F. Yeong, “Computerized assessment for early screening of children with handwriting difficulty,” Applied Mechanics and Materials, vol. 432, pp. 392-397, 2013.
N. A. Kamaruddin, “Temporal Spectral Approach to Surface Electromyography Based Fatigue Classification of Biceps Brachii During Dynamic Contraction,” Master. thesis, Universiti Teknologi Malaysia, Malaysia, 2016.
A. A. Hasseim, “Classification Techniques for Handwriting Difficulties Among Children in Early Stage of Academic Life,” Master thesis, Universiti Teknologi Malaysia, Malaysia, 2013.
K. H. Chin, “The Screening Tool for Handwriting Difficulty,” Final Year Project Report, Universiti Teknologi Malaysia, Malaysia, 2013.
C. C. Chang and C. J. Lin, “LIBSVM: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 21 – 27:27, 2011.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.
