Graphonomics, automaticity and handwriting handwriting analysis and the findings of grapho-nomic research about handwriting automaticity. ... 148 Graphonomics, automaticity and handwriting

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  • Graphonomics, automaticity andhandwriting assessmentOliver Tucha, Lara Tucha and Klaus W. Lange


    A recent review of handwriting research in Literacyconcluded that current curricula of handwritingeducation focus too much on writing style andneatness and neglect the aspect of handwritingautomaticity. This conclusion is supported by evidencein the field of graphonomic research, where a range ofexperiments have been used to investigate this issuefrom a movement perspective. The present articleoffers a brief introduction to a graphonomic approachto handwriting analysis and the findings of grapho-nomic research about handwriting automaticity. Thesefindings indicate that attentional control to anycharacteristic of the writing process (e.g. direction,lexical status, movement, style) results in an impair-ment of handwriting automaticity. These findingssupport and add a new dimension to previousconclusions.

    Key words: kinematics, writing, motor control


    In an excellent discussion published in a recent issue ofthis journal, Medwell and Wray (2007) summarised thefindings of recent research on handwriting, whichhave been reported in the fields of neuropsychology,cognitive psychology and special needs education.One of the main conclusions of the authors was that thepresent curriculum of handwriting education focusesprimarily on the aspect of well-formed, joined hand-writing while speed of handwriting and handwritingfluency or even automaticity are neglected.

    The terms fluency and automaticity refer to a generaldistinction between two broad classes of processes. Onthe one hand there are automatic processes that areexecuted rapidly and with minimal conscious effort.On the other hand controlled processes have beendescribed as effort demanding (Hasher and Zacks,1979; Kahneman, 1973; Morray, 1967; Norman andBobrow, 1975; Pribram and McGuiness, 1975; Schnei-der and Shiffrin, 1977; Shiffrin and Schneider, 1977).However, processes are not inherently controlled orautomatic but are controlled under certain conditionsand automatic under other conditions (Cohen, 1993;

    Cohen et al., 1990; Kahneman and Henik, 1981;MacLeod and Dunbar, 1988; Neumann, 1984; Sanderset al., 1987; Van Zomeren and Brouwer, 1994). Forexample, handwriting is a task that at first requiresattentional control but becomes automatic withincreasing practice (Sassoon, 1993). When handwritingbecomes automated, cognitive resources are freed up.These resources can be used for higher-level processessuch as the generation of ideas or vocabulary selection(Berninger and Swanson, 1994; Medwell and Wray,2007). Therefore, as discussed by Medwell and Wray(2007), the neglect of fluency or automaticity ofhandwriting in the present curriculum might bedetrimental for a significant number of children withwriting difficulties. For that reason, the authorsconclude that there is a need for a screening instrumentthat has to be designed to identify children withdifficulties regarding the automaticity of handwriting.With regard to Medwell and Wray (2007), speed ofletter generation may be a good measure for hand-writing fluency.

    Besides the findings presented by Medwell and Wray(2007), there is additional empirical evidence indicat-ing that the emphasis placed on neatness of hand-writing in the school curriculum is misplaced. Thisevidence is produced in the field of graphonomicresearch. Graphonomic research may also give supportin finding a definite measure of automaticity. The termgraphonomics describes a multidisciplinary and inter-disciplinary research field that is analysing therelationships between the planning and generation ofhandwriting and drawing movements, the resultingspatial traces of writing and drawing instruments andthe dynamic features of these traces. The aim of thepresent article is to make the findings of graphonomicresearch more accessible to educators by describing thegraphonomic approach to handwriting analysis andby presenting briefly the experimental graphonomicresearch confirming the conclusions of Medwell andWray (2007). For illustration and clarification of theresults provided by the graphonomic approach tohandwriting assessment, a number of figures depictinghandwriting specimens and corresponding velocityprofiles are included in the text. These handwritingspecimens were produced by the same right-handedadult writer who performed the same writing tasks asreported in the underlying studies but who did notparticipate in these studies.

    Literacy Volume 42 Number 3 November 2008 145

    r UKLA 2008. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

  • The graphonomic approach

    In graphonomic research, handwriting is not under-stood as a product. The style and neatness of hand-writing are therefore not of particular interest ingraphonomic research. Handwriting is rather under-stood as a process that is characterised by spatial andkinematic parameters. For example, when performingan ascending stroke of a letter, e.g. the letter l, one canmeasure various parameters such as the position of thepen tip on a sheet of paper and the time course. Velocityof movement execution can be calculated from theparameters time course and position; acceleration ofmovement execution can be derived from velocity.Medwell and Wray (2007) implicitly made a distinctionbetween a product-oriented and a process-orientedapproach to the examination of handwriting bycriticising that national testing in England focuses onneatness of handwriting but neglects handwritingspeed.

    The above-mentioned spatial and kinematic para-meters of movement execution during handwriting(position, time, velocity and acceleration) can easily berecorded by digitising tablets (Figure 1). Commerciallyavailable digitising tablets are able to measure spatialand kinematic parameters continuously during writ-ing and are able to localise the tip of the pen with anaccuracy of 0.2 mm in both directions (x/y). Further-more, movements of the pen tip above the paper can

    also be recorded (up to a maximum of 1.3 cm). Spatialand kinematic data are stored on a personal computer,which is connected to the tablet. Data processing can beperformed with commercially available computationalprograms for the analysis of handwriting movementssuch as CS, Oasis or Neurosoft (De Jong et al., 1996;Mai and Marquardt, 1992; Teulings and Van Gemmert,2003).

    In the literature, various kinematic parameters havebeen discussed (e.g. maximum velocities and accelera-tions) to describe the execution of handwriting move-ments of healthy children and adults (Van Galen et al.,1993) as well as children and adults with a variety ofdisorders such as attention-deficit hyperactivity dis-order or major depression (Tucha and Lange, 2001;Tucha et al., 2002). The parameter number ofinversions in the velocity profile of a movement(abbreviated as NIV) has been shown to be ofparticular importance in the assessment of highlyskilled motor activities (the term skilled refers in thisarticle to the fluent execution of movements). Toexplain this parameter the example of a car standingat a red traffic light by a crossing will be used. The roadahead is straight and about 150 m further on is a secondtraffic light on red. The first traffic light now changes togreen (Figure 2A). The car is now able to move and thiswill be with a different velocity over time: at thebeginning of the movement the velocity is modest butthe velocity will continuously increase until a max-imum is reached. The maximum velocity is reached atabout the midpoint of the distance. After the midpointof the distance the velocity has to be reducedcontinuously. Otherwise, the car would not stop atthe second traffic light, which is still showing a redsignal. This reduction of velocity is not performed byan emergency stop. The velocity is reduced slowly sothat it is more comfortable for the driver and thepassengers. The result is that the profile of velocity hassome specific properties (Figure 2B), such as a smoothcourse, a bell-shaped profile and only one inversion(NIV 5 1). The parameter NIV describes the number ofdirectional changes in velocity during movementexecution. It represents a measure of how smooth,and therefore fluent (automatic), handwriting move-ments are. A velocity profile with only one inversion(NIV 5 1) indicates a fully automated movement (alsodescribed as open-loop or absolutely fluent move-ment). Automated movements are those performedwith the least motor effort possible (only one change invelocity).

    However, this driver was, of course, not such a skilleddriver all of the time. When the driver steered a car forthe first time, for example during a driving lesson, andhe approached the same traffic situation, the profile ofvelocity presumably had a different course. When thefirst traffic light changed to green the driver may haveaccelerated (Figure 2C), but possibly far too muchbecause of a lack of familiarity with driving a car.Consequently, the velocity was reduced by slowingFigure 1: Subject writing on a writing tablet.

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    r UKLA 2008

  • down the car (also described as negative acceleration).Because the driver had not yet developed a feeling ofhow the car performed, the velocity may have beenreduced too much, and an increase of velocity (positiveacceleration) was therefore necessary again. Thisresulted possibly in a velocity that was not appropriatefor the situation so that a reduction of the speed wasnecessary again. Acceleration and deceleration mayrepeatedly be necessary in this particular situationuntil the car stops at the second traffic light. Multiplechanges in velocity leave a more jagged pattern, whichindicates lack of automaticity (non-automated move-ment). In conclusion, automated and non-automatedmovements can be distin