Digitized Cursive Handwriting for Determining FMS in Early School-Age Children
Abstract
Assessing fine motor skills (FMS) in early school-age children is crucial for insights into their school readiness. In many countries, including Indonesia, teachers assess FMS by observing handwriting, often with the aid of an educational psychologist. However, this approach can be subjective and prone to observer bias. This study aimed to classify children’s FMS based on their cursive writing abilities using a digitizer to capture data. The system recorded data in real-time as children wrote in cursive, capturing the stylus’s relative position on the digitizer board (including x, y, and z positions), and pressure values, which served as features in the classification process. The study involved 40 1st and 2nd-grade students from various elementary schools. The data recording process generated substantial raw datasets. The random forest algorithm, renowned for its effectiveness in analyzing large datasets, was employed for classification. The results demonstrated this method’s efficacy in identifying FMS, achieving an accuracy rate of approximately 97.3%. This study concludes that integrating a digitizer with the random forest classification method provides a reliable and objective approach to assessing FMS in children, reducing observer bias, and ensuring precise results. In the long term, this approach can significantly enhance the accuracy of FMS assessments, enabling better-targeted interventions and support for children in need.
References
D. Grissmer et al., “Fine motor skills and early comprehension of the world: Two new school readiness indicators,” Dev. Psychol., vol. 46, no. 5, pp. 1008–1017, Sep. 2010, doi: 10.1037/A0020104.
S.-M. Seo, “The effect of fine motor skills on handwriting legibility in preschool age children,” J. Phys. Ther. Sci., vol. 30, no. 2, pp. 324–327, Feb. 2018, doi: 10.1589/jpts.30.324.
K.P. Feder and A. Majnemer, “Handwriting development, competency, and intervention,” Dev. Med. Child Neurol., vol. 49, no. 4, pp. 312–317, Apr. 2007, doi: 10.1111/J.1469-8749.2007.00312.x.
H. Kim, S. Valentine, P. Taele, and T. Hammond, “EasySketch: A sketch-based educational interface to support children’s self-regulation and school readiness,” in The Impact of Pen and Touch Technology on Education. Cham, Swiss: Springer, 2015.
H.K. Gerde, T.D. Foster, and L.E. Skibbe, “Beyond the pencil: Expanding the occupational therapists’ role in helping young children to develop writing skills,” Open J. Occup. Ther., vol. 2, no. 1, pp. 1–19, Jan. 2014, doi: 10.15453/2168-6408.1070.
H. Schwellnus et al., “Effect of pencil grasp on the speed and legibility of handwriting in children,” Am. J. Occup. Ther., vol. 66, no. 6, pp. 718–726, Nov./Dec. 2012, doi: 10.5014/ajot.2012.004515.
C.S. Puranik and C.J. Lonigan, “From scribbles to scrabble: Preschool children’s developing knowledge of written language,” Read. Writ., vol. 24, no. 5, pp. 567–589, May 2011, doi: 10.1007/S11145-009-9220-8/tables/8.
C.S. Puranik and S. Alotaiba, “Examining the contribution of handwriting and spelling to written expression in kindergarten children,” Read. Writ., vol. 25, no. 7, pp. 1523–1546, Aug. 2012, doi: 10.1007/s11145-011-9331-x.
C. Zhang, J. Hur, K.E. Diamond, and D. Powell, “Classroom writing environments and children’s early writing skills: An observational study in head start classrooms,” Early Child. Educ. J., vol. 43, no. 4, pp. 307–315, Jul. 2015, doi: 10.1007/S10643-014-0655-4.
T. Erdogan and O. Erdogan, “An analysis of the legibility of cursive handwriting of prospective primary school teachers,” in 4th World Conf. Educ. Sci. (WCES-2012), 2012, pp. 5214–5218, doi: 10.1016/j.sbspro.2012.06.412.
E.S. Oche, “The influence of poor handwriting on students’ score reliability in mathematics,” Math. Educ. Trends Res., vol. 2014, pp. 1–15, Jan. 2014, doi: 10.5899/2014/metr-00035.
A. Comajuncosas, M. Faundez-Zanuy, J. Solé-Casals, and M. Portero-Tresserra, “Preliminary study on implications of cursive handwriting learning in schools,” in Multidisciplinary Approaches to Neural Computing. Cham, Switzerland: Springer, 2018.
A.P. Accardo, M. Genna, and M. Borean, “Development, maturation and learning influence on handwriting kinematics,” Hum. Mov. Sci., vol. 32, no. 1, pp. 136–146, Feb. 2013, doi: 10.1016/j.humov.2012.10.004.
V. Bevilacqua et al., “A model-free computer-assisted handwriting analysis exploiting optimal topology ANNs on biometric signals in parkinson’s disease research,” in 14th Int. Conf. Intell. Comput. Theor. Appl. (ICIC 2018), 2018, pp. 650–655, 2018.
M. Moetesum, I. Siddiqi, F. Javed, and U. Masroor, “Dynamic handwriting analysis for Parkinson’s disease identification using C-BiGRU model,” in 2020 17th Int. Conf. Front. Handwrit. Recognit. (ICFHR), 2020, pp. 115–120, doi: 10.1109/icfhr2020.2020.00031.
S. Polsley et al., “Detecting children’s fine motor skill development using machine learning,” Int. J. Artif. Intell. Educ., vol. 32, no. 4, pp. 991–1024, Dec. 2022, doi: 10.1007/s40593-021-00279-7.
N.Z. Fanani et al., “Two stages outlier removal as pre-processing digitizer data on fine motor skills (FMS) classification using covariance estimator and isolation forest,” Int. J. Intell. Eng. Syst., vol. 14, no. 4, pp. 571–582, Aug. 2021, doi: 10.22266/ijies2021.0831.50.
A. Zakrani, M. Hain, and A. Namir, “Software development effort estimation using random forests: An empirical study and evaluation,” Int. J. Intell. Eng. Syst., vol. 11, no. 6, pp. 300–311, Dec. 2018, doi: 10.22266/ijies2018.1231.30.
M. Longcamp, M.-T. Zerbato-Poudou, and J.-L. Velay, “The influence of writing practice on letter recognition in preschool children: A comparison between handwriting and typing,” Acta Psychol., vol. 119, no. 1, pp. 67–79, May 2005, doi: 10.1016/j.actpsy.2004.10.019.
E.M. Koppitz, The Bender Gestalt Test for Young Children. New York, NY, USA: Grune & Stratton, 1964.
N. Zainal, A.G. Sooai, S. Sumpeno, and M.H. Purnomo, “Children’s fine motor skill determination from Hanacaraka writing process using random forest,” J. Nas. Tek. Elekt. Teknol. Inf., vol. 9, no. 2, pp. 148–154, May 2020, doi: 10.22146/JNTETI.V9I2.153.
A. Mucherino, P.J. Papajorgji, and P.M. Pardalos, “K-nearest neighbor classification,” in Data Mining in Agriculture. New York, NY, USA: Springer, 2009.
K.P. Murphy, “A Probabilistic Perspective,” in Chance Encounters: Probability in Education. Dordrecht, Netherlands: Springer, 1991.
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