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Adapting to the surface: A comparison of handwriting measures when writing on a tablet computer and on paper
Human Movement Science 48 (2016) 62–73
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Full Length Article
Adapting to the surface: A comparison of handwriting measures when writing on a tablet computer and on paper Sabrina Gerth a,⇑, Thomas Dolk a, Annegret Klassert a, Michael Fliesser a, Martin H. Fischer b, Guido Nottbusch c, Julia Festman a a
Research Group: Diversity and Inclusion, University of Potsdam, Germany Cognitive Sciences, University of Potsdam, Germany c Primary School Education/German, University of Potsdam, Germany b
a r t i c l e
i n f o
Article history: Received 16 October 2015 Revised 8 April 2016 Accepted 16 April 2016
Keywords: Handwriting Movement kinematics Tablet computer Handwriting movements adaptation Graphomotor execution
a b s t r a c t Our study addresses the following research questions: Are there differences between handwriting movements on paper and on a tablet computer? Can experienced writers, such as most adults, adapt their graphomotor execution during writing to a rather unfamiliar surface for instance a tablet computer? We examined the handwriting performance of adults in three tasks with different complexity: (a) graphomotor abilities, (b) visuomotor abilities and (c) handwriting. Each participant performed each task twice, once on paper and once on a tablet computer with a pen. We tested 25 participants by measuring their writing duration, in air time, number of pen lifts, writing velocity and number of inversions in velocity. The data were analyzed using linear mixed-effects modeling with repeated measures. Our results reveal differences between writing on paper and on a tablet computer which were partly task-dependent. Our findings also show that participants were able to adapt their graphomotor execution to the smoother surface of the tablet computer during the tasks. Ó 2016 Elsevier B.V. All rights reserved.
1. Introduction Handwriting involves the skilled coordination and timing of activities of multiple joints (e.g., hand, arm, shoulder) in order to generate planar movements of a pen tip (e.g., Latash, 1993, p. 212). Writing is organized in a specific sequence, first, individual letter strokes are chunked into production units which are then, with the appropriate timing, transformed into trajectories of the pen tip that, in turn, must be adjusted on the basis of proprioceptive and tactile feedback to produce a smooth writing movement (Tresilian, 2012, p. 723). These lower-level processes of handwriting, such as graphomotor execution, need to be mastered first in handwriting acquisition, because they demand conscious attention to the writing process and a close sensory guidance of the pen during writing (Grabowski, 2010). Despite its importance in handwriting acquisition only a few writing models include graphomotor execution as a fundamental skill of writing (Kandel, Peereman, Grosjacques, & Fayol, 2011; Van Galen, 1991). Therefore, our study focusses on the graphomotor execution and how it is influenced by the writing surface, namely the smoother writing surface of a tablet computer compared to more familiar paper. ⇑ Corresponding author at: Research Group: Diversity and Inclusion, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany. E-mail address: [email protected] (S. Gerth). http://dx.doi.org/10.1016/j.humov.2016.04.006 0167-9457/Ó 2016 Elsevier B.V. All rights reserved.
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1.1. Depicting the handwriting process Previous studies in handwriting research primarily focussed on the product of writing – the quality and the speed of production (Berninger et al., 1992; Graham, Harris, & Fink, 2000; Medwell & Wray, 2007, 2014; Rosenblum, Weiss, & Parush, 2003; Weintraub & Graham, 1998). The quality of handwriting refers to the consistent size and the legibility of letters and words. The speed of production mostly relates to the number of letters or words written in a certain amount of time (e.g., the number of letters written correctly in 15 s during a given task such as writing the alphabet or copying the sentence ‘‘the quick brown fox jumps over the lazy dog”; [Berninger et al., 1992, 1997; Graham & Weintraub, 1996; Medwell & Wray, 2014]). However, handwriting is not simply a product of distinct hand and finger movements, but it ‘‘is rather understood as a process that is characterised by spatial and kinematic parameters” (Tucha, Tucha, & Lange, 2008, p. 146). 1.2. New technologies in handwriting research Recently a shift from a product-oriented to a process-oriented approach in handwriting occurred. With the advent of graphic digitizers and tablet computers, researchers can track the process of handwriting directly instead of being restricted to the final product (Medwell & Wray, 2007; Rosenblum et al., 2003; Tucha et al., 2008). These media provide a reliable and objective measure of the dynamic parameters of handwriting performance (Marquardt & Mai, 1994). The surface of a graphic digitizer or tablet computer is usually smoother when compared to paper. Consequently, the writer needs to adapt the graphomotor execution during writing to generate a regular and fluent handwriting movement. Previous studies have shown that skilled writers are able to adapt their handwriting movements to different writing surfaces (e.g., when producing a signature on the smooth surface of a credit card) through a modulation of pen pressure (Wann & Nimmo-Smith, 1991) or a modulation of writing height (Denier van der Gon & Thuring, 1965). The early study of Denier van der Gon and Thuring (1965) showed that adults’ writing times stay constant but the height of letters changes when the friction between pen and writing surface is decreased. In other words, participants wrote faster and bigger when the writing surface became noticeably smoother. The authors show that such adjustments of handwriting performance have a delay of about 100 ms. We suspect that this sensitivity to the writing surface is tuned through the motor components of handwriting which leads to an adaption of graphomotor execution via proprioceptive and tactile feedback (Tresilian, 2012, p. 723). Therefore, our study investigates how experienced writers adapt their graphomotor execution to the smoother writing surface of a tablet computer in comparison to paper. 1.3. Comparing different writing surfaces To our knowledge, there were very few studies that investigated if it makes a difference to write on a tablet computer or on paper. For instance, Alamargot and Morin (2015) asked second and ninth graders to write the alphabet and their names and surnames on a tablet computer and on paper. Both groups wrote less legible letters in the name-surname task and larger letters (in the alphabet and name-surname task) on the tablet screen than on paper. Further, the ninth graders showed faster writing speed and higher pen pressure while the second graders exhibited more pauses during writing on the tablet computer than on paper. Alamargot and Morin (2015) suggested that the two surfaces differently influenced the writing of younger and older pupils. More specifically, the ninth graders compensated for the smoother surface by producing larger letters and by increasing their pen pressure and pen speed which is similar to the behavior observed in adults (Wann & Nimmo-Smith, 1991). However, previous research has shown that children differ in handwriting performance compared to adults. For instance, the study by Bourdin and Fayol (1994) directly compared spoken and written language production of children and adults in French. Participants had to recall word lists in oral or written mode. Their results show that children, compared to experienced writers such as most adults, performed better for oral recall than written recall while there was no difference in recall mode for adults. Bourdin and Fayol argued that children are not fully automatized in low-level skills of writing (e.g., spelling and graphomotor skills) which reduces the working memory capacity available for the memory task. Recently, Grabowski (2010) replicated the results of Bourdin and Fayol (1994) for German, even though the orthography of German is more predictable than the French orthography and would therefore pose fewer demands on the cognitive system of young writers. Similar to Bourdin and Fayol (1994), Grabowski (2010) concluded that the lack of graphomotor automization (not spelling differences) is responsible for a slower performance of children compared to adults. Since children’s movement execution during handwriting is not yet automatized, we hypothesize that modifications in writing conditions, for instance a smoother writing surface such as a tablet computer, might influence children’s handwriting performance in a different way compared to adults’ handwriting movements. Therefore we decided to investigate handwriting performance on different writing surfaces of experienced writers rather than children. 1.4. Handwriting measures Tablet computers and digitizers provide handwriting measures such as writing and pause duration, writing velocity and the number of inversions in velocity (NIV) that capture the dynamic processes during writing (Adi-Japha & Freeman, 2001; Kandel & Perret, 2015; Sumner, Connelly, & Barnett, 2014; Tucha et al., 2008; Wicki, Hurschler Lichtsteiner, Saxer Geiger, &
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Müller, 2014). The NIVs describe the number of directional changes in velocity during the handwriting performance and reflect how smooth and fluent such movements are. These process measures of handwriting, especially the NIVs, are considered to reflect the degree of automaticity of handwriting, i.e., the lower the NIVs the more fluent and automatized the movement. Therefore, NIVs provide a more fine-grained and more direct measure for the degree of automaticity of handwriting than, for instance, the handwriting speed measured via the number of letters in a certain amount of time (Marquardt, Gentz, & Mai, 1996; Tucha et al., 2008). Studies have shown that even when the final writing products look similar, the NIVs and the velocity profiles during writing can be quite different (Marquardt et al., 1996; Tucha & Lange, 2005; Tucha et al., 2008). The latter can depict the directed attention of the writer to the handwriting process that leads to an interference with automatic writing. For instance, Marquardt and colleagues asked participants to repeatedly write a sentence in four different conditions: (1) during normal handwriting, (2) while foveally tracking the tip of the pen, (3) without vision – the sight of the digitizer and the writing hand were blocked by a screen – and (4) without vision while participants were instructed to mentally track the top position of each up-stroke. Interestingly, the writing products showed very little variation between conditions. The style of letters, the number of strokes and the position of pen lifts were almost the same for all conditions. Only the velocity profiles and the NIVs showed interesting differences between conditions: The velocity profiles of conditions (1) and (3) were similar, participants produced smooth and single-peaked velocity profiles. Probably visual feedback was only used to monitor the stroke size, the form and the position of letters during writing (Tucha & Lange, 2005). However, the velocity profiles of conditions (2) and (4) were associated with frequent inversions of velocity, suggesting that focussing on the writing process – visually or mentally – slowed down writing and increased the NIVs. Marquardt et al. (1996) concluded that directing attention to the writing process, for example the visual feedback or the ongoing movements during writing, interferes with automatized writing movements. These findings are in line with studies by Tucha et al. (2008; see also Tucha & Lange, 2005) showing that directing attention to the direction of writing (mirror writing), the lexical status (writing of non-words), the writing movement itself (visual and mental control of writing) and the style of writing (neat handwriting) increased the NIVs, thereby indicating a non-automatized handwriting performance in adults, whereas normal writing and writing without vision typically showed no such increase in NIVs. Tucha et al. (2008) concluded that automatized handwriting is independent from visual feedback, but conscious attention to the process of writing hampers the automaticity of handwriting movements. The findings of these studies indicate that NIVs are an adequate handwriting measure to quantify the level of automaticity in graphomotor execution and the amount of directed attention to the writing process. 1.5. The present study Nowadays, tablet computers are used widely for writing (Mangen & Velay, 2010). Even though they are employed in research with increasing frequency (e.g., to evaluate the handwriting speed in comparison to the handwriting product [Rosenblum et al., 2003] or to distinguish proficient and non-proficient handwriters [Smits-Engelsman & Van Galen, 1997]), they represent a challenge for the writer when a pen is used on the rather smooth surface. Until now there is only one study (Alamargot & Morin, 2015) that systematically investigated whether the process of handwriting is different when using tablet computers in contrast to writing on paper. However, this study tested children and not adults. Since previous research has shown that children are less automatized in graphomotor execution compared to adults (Bourdin & Fayol, 1994; Grabowski, 2010), we decided to investigate the handwriting performance of experienced writers rather than children. Additionally, we suspected that students are currently still more familiar with typing or writing on paper than writing with a pen on a tablet. Therefore, the research questions of our study are: Are there differences between handwriting on paper and on a tablet computer? Can adults adapt their graphomotor control during writing on a rather unfamiliar surface such as a tablet computer? This study directly compares the handwriting performance of adults on a tablet computer and on paper attached to a digitizer. This way, we are able to obtain the same handwriting measures in both conditions. 2. Methods 2.1. Participants Twenty-five students (21 female, mean age 21.8 years (SD: 2.64)) recruited at the University of Potsdam participated in this study. They were all right-handed German native speakers, naïve to the purpose of the experiment and had normal or corrected-to-normal vision. All participants gave written informed consent before the experiment. The study was approved by the ethics commission of the University of Potsdam (Reference number 41/2014) and tests were administered in accordance with the ethical standards specified in the Declaration of Helsinki. 2.2. Apparatus The study was conducted in two conditions: (1) writing with a Lenovo Pen on a ThinkPad X61 and (2) writing on paper with an Intuos Inking Pen. The paper in condition (2) was placed on a digitizer (Intuos4 XL DTP) to obtain the same measures
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as in condition (1) and the digitizer was connected via USB cable to a ThinkPad X61. This ThinkPad X61 was the same tablet computer that we used in condition (1), hence we recorded exactly the same handwriting measures with the same temporal and spatial resolution in both conditions on the ThinkPad X61 (henceforth referred to as tablet computer). The surfaces of the tablet computer and the digitizer are made of tarnished plastic. The paper has a density of 80 g/m2. The pressure sensitivity of the Lenovo Pen is measured in 512 pressure levels and the Intuos Inking Pen generates up to 2048 pressure levels. Note that we did not run any comparisons for pen pressure between the two media. We used a wooden frame (width: 62 cm, length: 46 cm, height: 3 cm) to level the height of the ThinkPad and the forearm of the participant. The digitizer was wide enough so that the forearm could be positioned comfortably on it during writing. The operating system of the tablet computer is Microsoft Windows 7 Professional Service Pack 1. The sampling frequency capability of the ThinkPad X61 is 133 Hz and of the digitizer 200 Hz. Since we used the ‘‘standard mode” for the transmission rate of the Wacom software, the sampling frequency was set to 133 Hz for both media. As a development environment to record the handwriting data we used Visual Studio Community 2013 Update 4 and the Windows Presentation Foundation runtime libraries which are part of the Microsoft .Net Framework 4.5 Ó Microsoft. The software was programmed in C# and XAML. 2.3. Friction assessment To quantify the difference in friction between the two surfaces we used an experimental set-up (Fig. 1) in which the pen was attached to a swivel arm. The tractive force was varied by different loads (20–50 g) connected to the swivel arm with a string running over a cable run at the end of the table. The height of the pen measured from the surface was the same for both media. The pressure on the pen was kept constant for all trials using a counterweight at the other end of the swivel arm. Table 1 shows the writing velocity in mm/s for each of the conditions. The results clearly show a higher velocity for the plastic tipped pen on the tablet computer compared to a ballpoint pen on paper in all conditions. We conclude that the ballpoint pen on the paper has a higher friction compared to the plastic pen on the surface of the tablet computer screen. 2.4. Procedure Participants were seated comfortably in front of a table in a silent laboratory room. Half of the participants began with the tablet computer condition, the other half with the paper condition. Each condition started with a short warm-up, i.e. writing their first name and drawing circles around a dot, to become familiarized with the medium. In the beginning of the experiment, the pen was placed centrally in front of the participant to prevent any bias on handedness. One session took approximately 20 min. 2.5. Materials We examined the handwriting performance in three tasks with different complexity: (a) graphomotor abilities, (b) visuomotor abilities and (c) handwriting (copying the phrase ‘‘Sonne und Wellen” [German for ‘‘sun and waves”]). Each participant performed each task twice, on a tablet computer with a pen in condition (1) and in a separate session on paper attached to a digitizer in condition (2). The writing space and the order of tasks were kept parallel for both media. There were no time constraints for any of the tasks. In the following we will explain each task in more detail. 2.5.1. Graphomotor abilities We investigated the prerequisites of handwriting using 4 different continuous movement patterns because they constitute basic components of handwriting. In the first task, participants were asked to generate loop patterns twice. First the experimenter drew the loop pattern to illustrate the task (Fig. 2a) and then the participant had to repeat it on the next screen. For the other three tasks the picture to copy was presented in the upper half of the screen (Fig. 2b–d) and the experimenter did not show how to draw the pattern. The tasks required the participant to copy a loop pattern around dots (Fig. 2b), a zigzag line around dots (Fig. 2c) and a given staircase pattern around dots (Fig. 2d). Each task was conducted twice. We chose these three tasks to capture three basic handwriting patterns: round, diagonal and horizontal-vertical lines. The writing space for all 4 tasks had a size of 24.7 8.5 cm. 2.5.2. Visuomotor abilities In our study we took also into account the visuomotor abilities underlying handwriting measures. For that purpose, we selected two parts of the Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI) 6th Edition (Beery & Beery, 2010) because this test assesses the degree to which visual perception and finger-hand movements are coordinated in children and adults (e.g., during handwriting) (Volman, van Schendel, & Jongmans, 2006). We used the first nine forms of the Visual-Motor Integration (VMI) and the Motor Coordination (MC) tasks in our study. We chose only the first nine forms because, according to Weil and Amundson (1994), these are the typical forms that a child can master even before knowing how to write. For adults, they are well-learned and easy to perform. Moreover, the first nine items of the VMI are exactly the same geometric forms as those in the MC. Our first task, the VMI, requires the participants simply to copy the geometric forms (Fig. 3a). Usually the evaluation of the VMI score is achieved through the analysis of the product. A rater quantifies the drawings according to a rubric and sums
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Fig. 1. Experimental set-up to determine the difference in friction on pen movement speed between the two media using 4 different loads (20, 30, 40 and 50 g) to move the pen across the paper on the digitizer (or across the tablet computer in the tablet computer condition).
Table 1 Writing velocity (mm/s) on the two writing surfaces (tablet computer and paper) for four loads attached to the pen. Load on pen
Paper/ballpoint
Tablet computer/plastic
20 g 30 g 40 g 50 g Mean
2.67 5.48 24.36 39.14 17.91
2.94 25.57 49.65 62.43 35.15
Fig. 2. (a) Instruction: ‘‘Copy the loop pattern on the next screen.”, (b–d) Instruction: ‘‘Copy the pattern above in the space below.”
up a total score. However, we created a digital version of the first nine geometric forms of the VMI to conduct the task on a tablet computer and track the process of handwriting. In our version of the task, three forms were given in the upper half of the screen and the participants had to copy each form into a square directly below (Fig. 3c). Our second visuomotor task, the MC (Fig. 3b), requires the participants to trace a geometric form with a pencil without crossing a double-lined path. In our digital version, three figures were shown in the upper half of the screen and participants were asked to connect the dots in the form below with a pen, starting at the black dot. The figures in the upper half of the
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Fig. 3. The first nine geometric forms of the Visual-Motor Integration task (a) and the Motor Coordination task (b) (Beery & Beery, 2010). Writing space of the VMI (c) and MC (d).
page were given in a smaller scale than the writing space, in accordance with guidelines of the standardized test (Fig. 3d). Each square of writing space for both tasks had a size of 7.5 7.5 cm. 2.5.3. Handwriting Finally, we were interested in the underlying handwriting measures during the process of writing the phrase ‘‘Sonne und Wellen” (German for ‘‘sun and waves”). The phrase was presented in printed letters at the top of the screen (Fig. 4). We asked the participants to copy the phrase 10 times on lines (two screens with five lines each) in their own writing speed. We chose this phrase because it contains the letter combinations nn and elle, since during writing these letters the pen usually remains on the writing surface and these letter combinations constitute a simple continuous handwriting movement (as compared to e.g., ff). There were no constraints on the type of handwriting – printed or cursive. Each line was 15 cm long and the space between the lines was 2.4 cm (100 pixels). 2.6. Data analysis The x- and y-coordinates of the pen were recorded at a frequency of 133 Hz. In a first step we computed the velocity and number of inversions in velocity (NIVs) for the x- and y-coordinates using Perl v5 scripts. The velocity profiles of hand movements were smoothed by an implementation of the non-parametric kernel estimation (Marquardt & Mai, 1994) using the software R version 3.0.1 (R Development Core Team, 2013). The writing velocity was calculated by taking the first derivative of the x- and y-coordinates with respect to time. Even though the NIVs are sometimes calculated as the sum of all NIVs per up-stroke or down-stroke (Tucha et al., 2008), we computed the sum of NIVs occurring in the item because we did not exclusively test the writing of words. 2.7. Handwriting measures 2.7.1. Writing duration Writing duration is the time that the pen is on the screen surface of the tablet computer or paper (pressure > 0). We calculated the total duration it took participants to execute each task in milliseconds (ms). This gives an indication of temporal performance and is linked to average velocity (Rosenblum et al., 2003). 2.7.2. Writing velocity We computed the writing velocity in millimeter per second (mm/s). This measure is used to evaluate the fluidity in handwriting performance (Rosenblum et al., 2003).
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Fig. 4. Writing space for ‘‘Sonne und Wellen”.
2.7.3. In air time The digitizer can record the pen position at a distance of up to 1 cm above the surface. We measured the total in air time of the pen in ms. This measure indicates breaks in writing and might be linked to higher-level processes (Rosenblum et al., 2003; Sumner, Connelly, & Barnett, 2013). 2.7.4. Number of pen lifts The number of pen lifts determines how often the pen is lifted from the surface (pressure = 0) per item, including the final pen lift after finishing the item. We counted the number of pen lifts per pattern or phrase. A high number of pen lifts might reveal irregular and non-automatized writing. 2.7.5. Number of inversions in velocity (NIV) The number of inversions in velocity is related to the number of accelerations and decelerations during writing. The NIVs indicate the level of handwriting automaticity (Marquardt et al., 1996; Tucha et al., 2008). While low NIVs characterize an automatized and smooth movement, higher NIVs are associated with a lower degree of automaticity (Marquardt et al., 1996; Tucha et al., 2008). 2.8. Statistical analysis First, an outlier adjustment of the data was performed. We excluded data that were 3 standard deviations (SDs) above the group mean for all handwriting measures. These were mainly due to technical problems, misunderstandings of instructions or other external factors. For velocity we additionally excluded data 3 SDs below the group mean. Since the item complexity of the VMI and MC differed substantially, we excluded the data based on the mean of each item. We excluded 3.3% for the paper condition and 3.8% for the tablet computer condition. After the data exclusion via SDs for each condition we removed a data point in the data set for tablet computer if it was previously excluded for paper and vice versa, because we were only interested in the direct comparison of the surfaces between the media. This way we were able to use repeated-measures for the statistical analyses without the problem of having missing data. In total we excluded only 6.3% of the data. We analyzed the data using linear mixed-effect models for each task separately. The models were fit by maximizing the log-likelihood function with repeated measures using the software R version 3.0.1 (R Development Core Team, 2013) and the nlme-package (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2014). The five handwriting measures (dependent variables) were analyzed in separate models. The independent variable was the factor Medium (tablet vs. paper). Writing durations and the in air times were log-transformed in order to avoid skewed distributions. In addition to the p-value we will report the R2 for significant effects. This value describes the proportion of variance that is explained by the factor. R2 allows us to compare the effect sizes of different handwriting measures on different scales (e.g., time in ms, velocity in mm/s, etc.; Lakens, 2013). 3. Results 3.1. Analysis of graphomotor abilities Table 2 presents a summary of the descriptive data and statistical effects for the handwriting measures of the graphomotor abilities. The writing duration was longer for loops with dots when writing on paper compared to writing on a tablet computer (p = 0.026). Since the staircase pattern on paper was the only condition where participants used more than one
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Table 2 Means and standard deviations in parentheses for writing measures of the graphomotor abilities. The p-value refers to the comparison between the tablet computer and paper for the factor Medium. The R2 gives the effect size of significant effects. Writing duration (ms)
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
4760.90 (1273.86) 4549.95 (934.84) 0.216
0 0 –
1 1 –
104.55 (36.75) 125.06 (31.97) <0.001⁄ 0.45
34.88 (7.46) 32.3 (5.75) 0.021⁄ 0.20
8168.95 (2420.36) 7636.17 (2165.83) 0.026⁄ 0.19
0 0 –
1 1 –
43.03 (16.07) 53.17 (18.17) <0.001⁄ 0.55
43.36 (12.50) 42.63 (9.90) 0.249
Zigzag lines Paper Tablet computer p-value R2
7938.73 (2511.17) 7552.21 (2467.75) 0.188
0 0 –
1 1 –
40.54 (15.37) 51.83 (18.95) <0.001⁄ 0.64
42.52 (12.70) 40.50 (10.45) 0.211
Staircase pattern Paper Tablet computer p-value R2
8301.65 (2647.80) 7663.69 (2462.25) 0.051
48.86 (288.61) 0 –
1.10 (0.57) 1 –
20.76 (8.14) 26.78 (10.32) <0.001⁄ 0.60
48.88 (18.13) 44.43 (15.60) 0.047⁄ 0.18
Loops without dots Paper Tablet computer p-value R2 Loops with dots Paper Tablet computer p-value R2
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
pen lift, we did not run any analyses comparing in air time and number of pen lifts on tablet versus paper for the graphomotor ability tasks. The writing velocity was higher on the tablet computer compared to paper for all four tasks (all p < 0.001). The participants produced significantly fewer NIVs on the tablet computer than on paper for loops without dots (p = 0.021) and the staircase pattern (p = 0.047). Importantly, the writing velocity and the NIVs for all of the four tasks are correlated (Pearson’s r for loops without dots r = 0.33, p < 0.001; Kendall’s tau coefficient for loops with dots s = 0.62, p < 0.001; zizag lines s = 0.59, p < 0.001; staircase pattern s = 0.68, p < 0.001). This result shows that a smoother writing movement (=higher velocity) produces fewer NIVs (=more automatized and smoother movement) (Meulenbroek & Van Galen, 1990). The effect size (indicated by R2) shows that, depending on the task, writing velocity explains between 45% and 64% of the variance in the data for the comparison between the tablet computer and paper. 3.2. Analysis of visuomotor abilities We evaluated the VMI and MC tasks according to the manual of the Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI) 6th Edition (Beery & Beery, 2010). Two raters visually quantified each drawing according to the rubric of the test manual. The total test score constitutes how accurately the participants copied the forms. The scores of both tasks yielded a performance at ceiling for our participants. The mean score for the VMI performed on paper was 9 (out of 9) and on the tablet computer 8.97 (t(29) = 1, p = 0.33), the mean MC score on paper was 9 (out of 9) and on the tablet computer 8.87 (t (29) = 2.11, p = 0.04). The results of the paired t-tests show that there is no difference between the media for the performance of the VMI but there is for the performance of the MC. Participants performed significantly less well on the tablet computer for this task. Table 3 shows a summary of the descriptive data and statistical effects for the handwriting measures of the tests of visuomotor abilities. The participants wrote longer on the tablet computer than on paper only for the MC (p = 0.028, R2 = 0.19). The velocity was higher on the tablet computer than on paper only for the VMI (p < 0.001). The correlation analysis between the velocity and the NIVs yields Kendall’s tau coefficients for VMI s = 0.40 and MOC s = 0.34. Similar to the results obtained for the graphomotor abilities, these correlations reveal an inverse relationship between the writing velocity and the NIVs. For VMI the velocity explains 38% of the variance in the data for the comparison between the tablet computer and paper. Since both tasks involved the same set of items, we ran another analysis with the additional factor Task (VMI vs. MC) to check whether the tasks differed in graphomotor demands for the handwriting measures (see Table 4). We found one main effect for Medium (tablet computer vs. paper) for writing velocity. The factor Task yielded significant differences between VMI and MC for writing duration, writing velocity and NIVs (all p < 0.001). Compared to VMI, for MC the participants wrote longer (VMI: 1841.86 ms, MC: 2763.59 ms), slower (VMI: 32.01 mm/s, MC: 21.10 mm/s) and produced more NIVs (VMI: 11.32, MC: 16.93). Interestingly, the interaction of Task and Medium was significant for writing velocity (participants wrote faster on the tablet computer than on paper for the VMI, but there was no difference for MC).
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Table 3 Means and standard deviations in parentheses for writing measures of the visuomotor abilities. The p-value refers to the comparison between the tablet computer and paper for the factor Medium. The R2 gives the effect size of significant effects. Writing duration (ms) Visual-Motor Integration Paper Tablet computer p-value R2
(VMI) 1830.15 (1243.23) 1792.31 (1300.92) 0.562
Motor Coordination (MC) Paper 2582.54 (1546.54) Tablet computer 2938.22 (1694.12) p-value 0.028⁄ R2 0.19
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
219.59 (400.72) 200.85 (359.22) 0.586
1.45 (0.80) 1.35 (0.62) 0.104
29.88 (19.19) 37.04 (23.89) <0.001⁄ 0.38
11.79 (7.80) 10.51 (7.78) 0.107
199.66 (380.76) 232.25 (429.96) 0.682
1.40 (0.73) 1.41 (0.71) 0.946
21.53 (11.40) 21.00 (10.29) 0.964
16.77 (9.98) 17.02 (10.55) 0.969
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
Table 4 Summary of statistical effects for the factors Medium (tablet computer vs. paper), Task (VMI vs. MC) and the interaction Medium Task.
Medium Task Interaction
Writing duration (ms)
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
p = 0.230 p < 0.001⁄ p = 0.055
p = 0.932 p = 0.799 p = 0.500
p = 0.233 p = 0.775 p = 0.261
p < 0.002⁄ p < 0.001⁄ p < 0.001⁄
p = 0.312 p < 0.001⁄ p = 0.403
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
3.3. Analysis of graphomotor abilities of handwriting Table 5 presents a summary of the descriptive data and statistical effects for the handwriting measures of writing the phrase ‘‘Sonne und Wellen”. We found significant differences between the tablet computer and paper for all five handwriting measures. The participants wrote longer (p < 0.001), had a longer in air time (p = 0.002), lifted the pen more often (p = 0.031), wrote quicker (p < 0.001) and produced more NIVs (p = 0.010) on the tablet computer compared to paper. The correlation between velocity and NIVs yielded a Kendall’s tau coefficient of s = 0.20, revealing that a faster velocity is associated with fewer NIVs. Notably, the participants showed a longer writing duration and a higher velocity on the tablet computer compared to paper. This apparently paradoxical result, that participants wrote faster but longer on the tablet computer, turned out to be a consequence of letter size. A paired t-test revealed that the participants wrote larger letters on the tablet computer (M: 1.47 cm, SD: 0.29 cm) compared to paper (M: 1.20 cm, SD: 0.32 cm; t(299) = 18.20, p < 0.001). The effect sizes for the different measures reveal that writing duration explains 71% of the data for the comparison of the tablet computer versus paper, velocity explains 59%, the in air time 33%, the NIVs 24% and the number of pen lifts only 18%.1 To test if the participants adapt to the smoother surface of the tablet computer, we ran an additional linear mixed-effects model with the pressure of the pen as dependent variable. The independent repeated measure was item number in increasing order. We hypothesized that if participants adapt to the smoother surface in the course of the task then the pressure should decrease while writing the phrase 10 times, indicating a change in graphomotor control and an adaptation to the surface. Since the tips of the pens differed slightly according to the medium (inked vs. non-inked pen) we ran the analysis for each medium separately. For both media the pressure significantly (Paper: p < 0.001; Tablet computer: p < 0.001) declined from the first (mean pressure for the first item on paper: M: 0.49, SD: 0.11; on the tablet computer: M: 0.76, SD: 0.09) to the last item (Paper: M: 0.46, SD: 0.12; Tablet computer: M: 0.71, SD: 0.08) of writing repetitively the same phrase in accordance with our predictions (Fig. 5). 4. Discussion The present study investigated whether the writing surface influences the handwriting process in skilled writers when they write on a tablet computer or on paper. Furthermore we sought to show if skilled writers can adapt their graphomotor execution during writing to the rather unfamiliar surface such as a tablet computer. Our results demonstrate that it makes a difference if experienced writers write on a tablet computer in comparison to paper. However, the difference in handwriting performance depends on the task and the specific writing demands of the task. Our study also reveals an adaptation of handwriting movements to the smoother surface of the tablet computer during the tasks. Again, the degree of adaptation is dependent on the specific features of the task. We will interpret our findings in more detail in the following sections.
1
The percentages do not add up to 100% because each measure was used as a dependent variable in a separate model.
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Table 5 Means and standard deviations in parentheses for writing measures of writing the phrase ‘‘sun and waves”. The p-value refers to the comparison between the tablet computer and paper for the factor Medium. The R2 gives the effect size of significant effects. Writing duration (ms) Writing ‘‘sun and waves” Paper 5039.54 (1414.62) Tablet computer 5872.78 (1823.93) p-value <0.001⁄ 2 R 0.71
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
1550.43 (642.99) 1741.86 (617.29) 0.002⁄ 0.33
8.83 (3.31) 8.51 (3.25) 0.031⁄ 0.18
30.46 (7.90) 35.88 (8.93) <0.001⁄ 0.59
56.45 (15.63) 59.68 (15.67) 0.010⁄ 0.24
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
Fig. 5. Mean of pen pressure for each item of ‘‘sun and waves” on paper and on the tablet computer (with 95% confidence intervals).
4.1. Handwriting performance on the tablet computer versus paper We found faster writing velocity for all tasks on the tablet computer compared to paper. These findings show that the pen was apparently sliding more on the smoother surface of the tablet computer. Consequently, the participants needed to adapt their motor execution to achieve fluent and regular handwriting. Additional post-hoc analyses of the effect size for the handwriting measures revealed that writing velocity accounted for nearly half of the variance in the tasks. Therefore, the difference between writing on a tablet computer and on paper is mostly explained by the writing velocity. We used three writing tasks with different complexity. The first task – graphomotor abilities – required copying continuous patterns. For all four patterns we found higher writing velocity on the tablet computer compared to paper. Additionally, the loops with dots showed a longer writing duration for paper. This results from the fact that participants drew loops wider and taller on paper than on the tablet computer. In other words, the pen passed a longer pathway in a shorter time, hence the writing duration was long and the velocity was high. Seemingly, our participants were more careful when copying the loops with dots compared to the other three tasks because they focussed their visual attention on drawing the loops around the dots (longer writing duration for paper) and the pen was sliding more on the smoother surface of the tablet computer (higher velocity on the tablet computer). When comparing the two tasks which probed the tests of visuomotor abilities – VMI and MC – we obtained a main effect of task (see Table 4). Furthermore, we found interactions of task and medium for the writing duration, the writing velocity and the NIVs. These results suggest that the task demands on the tablet computer were higher for MC compared to VMI. This is unsurprising since the MC required participants to stay in a particular writing space. This draws attention to the writing process and is similar to previous findings about mental or visual tracking of the writing process (Marquardt et al., 1996; Tucha & Lange, 2005; Tucha et al., 2008). Consciously attending to the handwriting movements hampers the automaticity of writing and leads to slower execution. Our last task examined participants’ actual handwriting. In this task they were asked to copy a phrase in their own handwriting. This task is different from the two previous tasks because participants did not have to copy a pattern. They only had to plan and execute the necessary motor movements for the letters of the phrase ‘‘Sonne und Wellen” from memory and write the phrase onto five lines twice. In our view this is a very easy task for experienced writers, hence this task probes handwriting automaticity on two different media. The results showed task-dependent differences for all five handwriting measures selectively for the comparison between the tablet computer and paper. We will discuss these findings in more detail in the following paragraph.
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4.2. Adaptation of handwriting performance Our study reveals that skilled writers are able to adapt their handwriting movements to the writing surface. In the handwriting task the results show longer writing duration and higher writing velocity on the tablet computer compared to paper. An analysis of the letter sizes produced a larger writing product on the tablet computer. Therefore, the time of the pen on the surface (=writing duration) was longer but the execution of the writing was faster (=writing velocity). A possible explanation for this finding might be the adaptation of the graphomotor execution to the smoother surface of the tablet computer compared to paper. The smoother surface of the tablet computer presumably needs to be countered with increased attention to the motor execution of the handwriting movements. We suggest that writers who are automatized in handwriting (‘‘who have had regular and long experience in comparison to children”, Chanquoy & Negro, 1996, p. 556) compensate for the decrease in friction between the pen and the writing surface by increasing their writing velocity and enlarging the letter size, which is similar to findings by Alamargot and Morin (2015) and Denier van der Gon and Thuring (1965). Another clue for our participants’ adaptation to the surface is a modulation in pen pressure during the execution of our handwriting task (copying ‘‘Sonne und Wellen”), in line with previous research (Wann & Nimmo-Smith, 1991). At the beginning of the task, execution involved higher pen pressure with the tip on the surface and during the course of the task the pen pressure declined. The handwriting task was the last task in our study, hence the participants were probably already familiar with the writing surface. But this task actually depicts handwriting performance while the previous tasks merely measured handwriting prerequisites. It might have been easier for the participants to use an automatized handwriting routine while executing this last, more natural task because they were less constrained in their performance, since in the previous tasks they either had to copy patterns or to stay in a particular writing space. In other words, the participants did not consciously concentrate on the handwriting movements because writing words is an everyday skill for which they have achieved automatized execution. Therefore, we interpret the decline in pen pressure during this task as an adaptation to the writing surface. Interestingly, these findings are in contrast to some results obtained in the study by Alamargot and Morin (2015). The 9th graders in their study increased the pen pressure to adapt to the smoother surface of the tablet computer. The authors interpret this as a disturbance in the ‘‘online regulation of initial motor commands” (Alamargot & Morin, 2015, p. 38). Nevertheless, the 9th graders’ adaptation was not sufficient since they exhibited less legible letters and longer pauses on the tablet compared to paper. This is probably due to a lower degree of automatization in graphomotor execution of 9th graders compared to the adults in our study. It might be that at this stage in handwriting perceptual feedback still plays an essential role in controlling handwriting movements (Bourdin & Fayol, 1994; Grabowski, 2010). We hypothesize that this may be a reason for a slower adaptation to a smoother writing surface.
5. Conclusions The results of our study provide a first answer to the still open question of whether the writing surface influences skilled writers’ execution of writing movements. We found differences between writing on a tablet computer compared to writing on paper which were partly modulated by the writing task. In general, we found a higher writing velocity when writing on a tablet computer. Apparently even experienced writers, such as most adults, are influenced by the friction of the writing surface. Nevertheless they are able to adapt quite quickly – even within ten items of copying a given word phrase. Our findings corroborate previous research that shows that writers compensate for a smoother writing surface with larger letters and a modulation in pen pressure. It would be interesting to see whether less skilled writers or even students who have not acquired handwriting yet are sensitive to the writing surface in a similar way and whether they are able to adapt their handwriting movements. As mentioned above, our findings were partly task-dependent. When copying basic and continuous graphomotor patterns, we found mixed results depending on having to focus attention on drawing around dots or not. In our second task – testing of visuomotor abilities – we clearly saw that the more demanding task (the MC) hampered the automaticity of the writing movements because participants had to focus their attention on the writing process to stay in the limited writing space. Finally, the results of our actual handwriting task show a clear adaptation to the writing surface. Participants wrote letters quicker and larger on the tablet computer and exhibited more NIVs when writing on the tablet computer. The explanation for this might be higher graphomotor control when writing on a smoother surface. Our observations have important implications for handwriting research. For example, we doubt that it is wise to simply digitize a paper-pencil-version of a test to obtain measures of the handwriting process. The difference in task demands might lead to unexpected and spurious results which are mostly related to the writing surface itself. Nevertheless we recommend using tablet computers in writing research to evaluate the process of handwriting in a more sophisticated manner than by assessing only the writing product.
Acknowledgements This research was funded by the Land Brandenburg, Germany. We thank the students at the University of Potsdam who participated in our study, as well as Elisabeth Fleischhauer, Sophia Czapka and David Ziegert for their help with the data
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collection, Jessica Grasso and Eliah Aila Wolff for their support with the data preparation and the members of the Research Group: Diversity and Inclusion for detailed comments. References Adi-Japha, E., & Freeman, N. H. (2001). Development of differentiation between writing and drawing systems. Developmental Psychology, 37(1), 101–114. http://dx.doi.org/10.1037//0012-1649.37.1.101. Alamargot, D., & Morin, M.-F. (2015). Does handwriting on a tablet screen affect students’ graphomotor execution? A comparison between grades two and nine. Human Movement Science, 44, 32–41. http://dx.doi.org/10.1016/j.humov.2015.08.011. Beery, K. E., & Beery, N. A. (2010). Beery VMI (6th ed.)Bloomington: Pearson. Berninger, V., Yates, C., Cartwright, A., Rutberg, J., Remy, E., & Abbott, R. (1992). Lower-level developmental skills in beginning writing. Reading and Writing, 4 (3), 257–280. Berninger, V. W., Vaughan, K., Abbott, R., Abbott, S., Woodruff Rogan, L., Brooks, A., ... Graham, S. (1997). Treatment of handwriting problems in beginning writers: Transfer from handwriting to composition. Journal of Educational Psychology, 89(4), 652–666. Bourdin, B., & Fayol, M. (1994). Is written language production more difficult than oral language production?: A working memory approach. International Journal of Psychology, 29(5), 591–620. http://dx.doi.org/10.1080/00207599408248175. Chanquoy, L., & Negro, I. (1996). Subject-verb agreement errors in written productions: A study of French children and adults. Journal of Psycholinguistic Research, 25(5), 553–570. Denier van der Gon, J. J., & Thuring, J. P. (1965). The guiding of human writing movements. Kybernetik, 2(4), 145–148. Grabowski, J. (2010). Speaking, writing, and memory span in children: Output modality affects cognitive performance. International Journal of Psychology, 45 (1), 28–39. http://dx.doi.org/10.1080/00207590902914051. Graham, S., Harris, K. R., & Fink, B. (2000). Is handwriting causally related to learning to write? Treatment of handwriting problems in beginning writers. Journal of Educational Psychology, 92(4), 620–633. Graham, S., & Weintraub, N. (1996). A review of handwriting research: Progress and prospects from 1980 to 1994. Educational Psychology Review, 8(1), 7–87. Kandel, S., Peereman, R., Grosjacques, G., & Fayol, M. (2011). For a psycholinguistic model of handwriting production: Testing the syllable-bigram controversy. Journal of Experimental Psychology: Human Perception and Performance, 37(4), 1310–1322. http://dx.doi.org/10.1037/a0023094. Kandel, S., & Perret, C. (2015). How does the interaction between spelling and motor processes build up during writing acquisition? Cognition, 136, 325–336. http://dx.doi.org/10.1016/j.cognition.2014.11.014. Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. http://dx.doi.org/10.3389/fpsyg.2013.00863. Latash, M. L. (1993). Control of human movement.IL: Human Kinetics Publishers. Mangen, A., & Velay, J.-L. (2010). Digitizing literacy: Reflections on the haptics of writing. In M. H. Zadeh (Ed.), Advances in haptics (pp. 385–401). INTECH.. Marquardt, C., Gentz, W., & Mai, N. (1996). On the role of vision in skilled handwriting. In M. L. Simner, C. G. Leedham, & A. Thomassen (Eds.), Handwriting and drawing research: Basic and applied issues (pp. 87–97). Amsterdam: IOS Press. Marquardt, C., & Mai, N. (1994). A computational procedure for movement analysis in handwriting. Journal of Neuroscience Methods, 52(1), 39–45. http://dx. doi.org/10.1016/0165-0270(94)90053-1. Medwell, J., & Wray, D. (2007). Handwriting: what do we know and what do we need to know? Literacy, 41(1), 10–15. Medwell, J., & Wray, D. (2014). Handwriting automaticity: The search for performance thresholds. Language and Education, 28(1), 1–18. http://dx.doi.org/ 10.1080/09500782.2013.763819. Meulenbroek, R. G. J., & Van Galen, G. P. (1990). Perceptual-motor complexity of printed and cursive letters. The Journal of Experimental Education, 58, 95–110. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team (2014). nlme: Linear and nonlinear mixed effects models R package version 3.1-117. . URL . R Development Core Team (2013). R: A language and environment for statistical computing.Vienna and Austria: R Foundation for Statistical Computing. Retrieved from
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Full Length Article
Adapting to the surface: A comparison of handwriting measures when writing on a tablet computer and on paper Sabrina Gerth a,⇑, Thomas Dolk a, Annegret Klassert a, Michael Fliesser a, Martin H. Fischer b, Guido Nottbusch c, Julia Festman a a
Research Group: Diversity and Inclusion, University of Potsdam, Germany Cognitive Sciences, University of Potsdam, Germany c Primary School Education/German, University of Potsdam, Germany b
a r t i c l e
i n f o
Article history: Received 16 October 2015 Revised 8 April 2016 Accepted 16 April 2016
Keywords: Handwriting Movement kinematics Tablet computer Handwriting movements adaptation Graphomotor execution
a b s t r a c t Our study addresses the following research questions: Are there differences between handwriting movements on paper and on a tablet computer? Can experienced writers, such as most adults, adapt their graphomotor execution during writing to a rather unfamiliar surface for instance a tablet computer? We examined the handwriting performance of adults in three tasks with different complexity: (a) graphomotor abilities, (b) visuomotor abilities and (c) handwriting. Each participant performed each task twice, once on paper and once on a tablet computer with a pen. We tested 25 participants by measuring their writing duration, in air time, number of pen lifts, writing velocity and number of inversions in velocity. The data were analyzed using linear mixed-effects modeling with repeated measures. Our results reveal differences between writing on paper and on a tablet computer which were partly task-dependent. Our findings also show that participants were able to adapt their graphomotor execution to the smoother surface of the tablet computer during the tasks. Ó 2016 Elsevier B.V. All rights reserved.
1. Introduction Handwriting involves the skilled coordination and timing of activities of multiple joints (e.g., hand, arm, shoulder) in order to generate planar movements of a pen tip (e.g., Latash, 1993, p. 212). Writing is organized in a specific sequence, first, individual letter strokes are chunked into production units which are then, with the appropriate timing, transformed into trajectories of the pen tip that, in turn, must be adjusted on the basis of proprioceptive and tactile feedback to produce a smooth writing movement (Tresilian, 2012, p. 723). These lower-level processes of handwriting, such as graphomotor execution, need to be mastered first in handwriting acquisition, because they demand conscious attention to the writing process and a close sensory guidance of the pen during writing (Grabowski, 2010). Despite its importance in handwriting acquisition only a few writing models include graphomotor execution as a fundamental skill of writing (Kandel, Peereman, Grosjacques, & Fayol, 2011; Van Galen, 1991). Therefore, our study focusses on the graphomotor execution and how it is influenced by the writing surface, namely the smoother writing surface of a tablet computer compared to more familiar paper. ⇑ Corresponding author at: Research Group: Diversity and Inclusion, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany. E-mail address: [email protected] (S. Gerth). http://dx.doi.org/10.1016/j.humov.2016.04.006 0167-9457/Ó 2016 Elsevier B.V. All rights reserved.
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1.1. Depicting the handwriting process Previous studies in handwriting research primarily focussed on the product of writing – the quality and the speed of production (Berninger et al., 1992; Graham, Harris, & Fink, 2000; Medwell & Wray, 2007, 2014; Rosenblum, Weiss, & Parush, 2003; Weintraub & Graham, 1998). The quality of handwriting refers to the consistent size and the legibility of letters and words. The speed of production mostly relates to the number of letters or words written in a certain amount of time (e.g., the number of letters written correctly in 15 s during a given task such as writing the alphabet or copying the sentence ‘‘the quick brown fox jumps over the lazy dog”; [Berninger et al., 1992, 1997; Graham & Weintraub, 1996; Medwell & Wray, 2014]). However, handwriting is not simply a product of distinct hand and finger movements, but it ‘‘is rather understood as a process that is characterised by spatial and kinematic parameters” (Tucha, Tucha, & Lange, 2008, p. 146). 1.2. New technologies in handwriting research Recently a shift from a product-oriented to a process-oriented approach in handwriting occurred. With the advent of graphic digitizers and tablet computers, researchers can track the process of handwriting directly instead of being restricted to the final product (Medwell & Wray, 2007; Rosenblum et al., 2003; Tucha et al., 2008). These media provide a reliable and objective measure of the dynamic parameters of handwriting performance (Marquardt & Mai, 1994). The surface of a graphic digitizer or tablet computer is usually smoother when compared to paper. Consequently, the writer needs to adapt the graphomotor execution during writing to generate a regular and fluent handwriting movement. Previous studies have shown that skilled writers are able to adapt their handwriting movements to different writing surfaces (e.g., when producing a signature on the smooth surface of a credit card) through a modulation of pen pressure (Wann & Nimmo-Smith, 1991) or a modulation of writing height (Denier van der Gon & Thuring, 1965). The early study of Denier van der Gon and Thuring (1965) showed that adults’ writing times stay constant but the height of letters changes when the friction between pen and writing surface is decreased. In other words, participants wrote faster and bigger when the writing surface became noticeably smoother. The authors show that such adjustments of handwriting performance have a delay of about 100 ms. We suspect that this sensitivity to the writing surface is tuned through the motor components of handwriting which leads to an adaption of graphomotor execution via proprioceptive and tactile feedback (Tresilian, 2012, p. 723). Therefore, our study investigates how experienced writers adapt their graphomotor execution to the smoother writing surface of a tablet computer in comparison to paper. 1.3. Comparing different writing surfaces To our knowledge, there were very few studies that investigated if it makes a difference to write on a tablet computer or on paper. For instance, Alamargot and Morin (2015) asked second and ninth graders to write the alphabet and their names and surnames on a tablet computer and on paper. Both groups wrote less legible letters in the name-surname task and larger letters (in the alphabet and name-surname task) on the tablet screen than on paper. Further, the ninth graders showed faster writing speed and higher pen pressure while the second graders exhibited more pauses during writing on the tablet computer than on paper. Alamargot and Morin (2015) suggested that the two surfaces differently influenced the writing of younger and older pupils. More specifically, the ninth graders compensated for the smoother surface by producing larger letters and by increasing their pen pressure and pen speed which is similar to the behavior observed in adults (Wann & Nimmo-Smith, 1991). However, previous research has shown that children differ in handwriting performance compared to adults. For instance, the study by Bourdin and Fayol (1994) directly compared spoken and written language production of children and adults in French. Participants had to recall word lists in oral or written mode. Their results show that children, compared to experienced writers such as most adults, performed better for oral recall than written recall while there was no difference in recall mode for adults. Bourdin and Fayol argued that children are not fully automatized in low-level skills of writing (e.g., spelling and graphomotor skills) which reduces the working memory capacity available for the memory task. Recently, Grabowski (2010) replicated the results of Bourdin and Fayol (1994) for German, even though the orthography of German is more predictable than the French orthography and would therefore pose fewer demands on the cognitive system of young writers. Similar to Bourdin and Fayol (1994), Grabowski (2010) concluded that the lack of graphomotor automization (not spelling differences) is responsible for a slower performance of children compared to adults. Since children’s movement execution during handwriting is not yet automatized, we hypothesize that modifications in writing conditions, for instance a smoother writing surface such as a tablet computer, might influence children’s handwriting performance in a different way compared to adults’ handwriting movements. Therefore we decided to investigate handwriting performance on different writing surfaces of experienced writers rather than children. 1.4. Handwriting measures Tablet computers and digitizers provide handwriting measures such as writing and pause duration, writing velocity and the number of inversions in velocity (NIV) that capture the dynamic processes during writing (Adi-Japha & Freeman, 2001; Kandel & Perret, 2015; Sumner, Connelly, & Barnett, 2014; Tucha et al., 2008; Wicki, Hurschler Lichtsteiner, Saxer Geiger, &
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Müller, 2014). The NIVs describe the number of directional changes in velocity during the handwriting performance and reflect how smooth and fluent such movements are. These process measures of handwriting, especially the NIVs, are considered to reflect the degree of automaticity of handwriting, i.e., the lower the NIVs the more fluent and automatized the movement. Therefore, NIVs provide a more fine-grained and more direct measure for the degree of automaticity of handwriting than, for instance, the handwriting speed measured via the number of letters in a certain amount of time (Marquardt, Gentz, & Mai, 1996; Tucha et al., 2008). Studies have shown that even when the final writing products look similar, the NIVs and the velocity profiles during writing can be quite different (Marquardt et al., 1996; Tucha & Lange, 2005; Tucha et al., 2008). The latter can depict the directed attention of the writer to the handwriting process that leads to an interference with automatic writing. For instance, Marquardt and colleagues asked participants to repeatedly write a sentence in four different conditions: (1) during normal handwriting, (2) while foveally tracking the tip of the pen, (3) without vision – the sight of the digitizer and the writing hand were blocked by a screen – and (4) without vision while participants were instructed to mentally track the top position of each up-stroke. Interestingly, the writing products showed very little variation between conditions. The style of letters, the number of strokes and the position of pen lifts were almost the same for all conditions. Only the velocity profiles and the NIVs showed interesting differences between conditions: The velocity profiles of conditions (1) and (3) were similar, participants produced smooth and single-peaked velocity profiles. Probably visual feedback was only used to monitor the stroke size, the form and the position of letters during writing (Tucha & Lange, 2005). However, the velocity profiles of conditions (2) and (4) were associated with frequent inversions of velocity, suggesting that focussing on the writing process – visually or mentally – slowed down writing and increased the NIVs. Marquardt et al. (1996) concluded that directing attention to the writing process, for example the visual feedback or the ongoing movements during writing, interferes with automatized writing movements. These findings are in line with studies by Tucha et al. (2008; see also Tucha & Lange, 2005) showing that directing attention to the direction of writing (mirror writing), the lexical status (writing of non-words), the writing movement itself (visual and mental control of writing) and the style of writing (neat handwriting) increased the NIVs, thereby indicating a non-automatized handwriting performance in adults, whereas normal writing and writing without vision typically showed no such increase in NIVs. Tucha et al. (2008) concluded that automatized handwriting is independent from visual feedback, but conscious attention to the process of writing hampers the automaticity of handwriting movements. The findings of these studies indicate that NIVs are an adequate handwriting measure to quantify the level of automaticity in graphomotor execution and the amount of directed attention to the writing process. 1.5. The present study Nowadays, tablet computers are used widely for writing (Mangen & Velay, 2010). Even though they are employed in research with increasing frequency (e.g., to evaluate the handwriting speed in comparison to the handwriting product [Rosenblum et al., 2003] or to distinguish proficient and non-proficient handwriters [Smits-Engelsman & Van Galen, 1997]), they represent a challenge for the writer when a pen is used on the rather smooth surface. Until now there is only one study (Alamargot & Morin, 2015) that systematically investigated whether the process of handwriting is different when using tablet computers in contrast to writing on paper. However, this study tested children and not adults. Since previous research has shown that children are less automatized in graphomotor execution compared to adults (Bourdin & Fayol, 1994; Grabowski, 2010), we decided to investigate the handwriting performance of experienced writers rather than children. Additionally, we suspected that students are currently still more familiar with typing or writing on paper than writing with a pen on a tablet. Therefore, the research questions of our study are: Are there differences between handwriting on paper and on a tablet computer? Can adults adapt their graphomotor control during writing on a rather unfamiliar surface such as a tablet computer? This study directly compares the handwriting performance of adults on a tablet computer and on paper attached to a digitizer. This way, we are able to obtain the same handwriting measures in both conditions. 2. Methods 2.1. Participants Twenty-five students (21 female, mean age 21.8 years (SD: 2.64)) recruited at the University of Potsdam participated in this study. They were all right-handed German native speakers, naïve to the purpose of the experiment and had normal or corrected-to-normal vision. All participants gave written informed consent before the experiment. The study was approved by the ethics commission of the University of Potsdam (Reference number 41/2014) and tests were administered in accordance with the ethical standards specified in the Declaration of Helsinki. 2.2. Apparatus The study was conducted in two conditions: (1) writing with a Lenovo Pen on a ThinkPad X61 and (2) writing on paper with an Intuos Inking Pen. The paper in condition (2) was placed on a digitizer (Intuos4 XL DTP) to obtain the same measures
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as in condition (1) and the digitizer was connected via USB cable to a ThinkPad X61. This ThinkPad X61 was the same tablet computer that we used in condition (1), hence we recorded exactly the same handwriting measures with the same temporal and spatial resolution in both conditions on the ThinkPad X61 (henceforth referred to as tablet computer). The surfaces of the tablet computer and the digitizer are made of tarnished plastic. The paper has a density of 80 g/m2. The pressure sensitivity of the Lenovo Pen is measured in 512 pressure levels and the Intuos Inking Pen generates up to 2048 pressure levels. Note that we did not run any comparisons for pen pressure between the two media. We used a wooden frame (width: 62 cm, length: 46 cm, height: 3 cm) to level the height of the ThinkPad and the forearm of the participant. The digitizer was wide enough so that the forearm could be positioned comfortably on it during writing. The operating system of the tablet computer is Microsoft Windows 7 Professional Service Pack 1. The sampling frequency capability of the ThinkPad X61 is 133 Hz and of the digitizer 200 Hz. Since we used the ‘‘standard mode” for the transmission rate of the Wacom software, the sampling frequency was set to 133 Hz for both media. As a development environment to record the handwriting data we used Visual Studio Community 2013 Update 4 and the Windows Presentation Foundation runtime libraries which are part of the Microsoft .Net Framework 4.5 Ó Microsoft. The software was programmed in C# and XAML. 2.3. Friction assessment To quantify the difference in friction between the two surfaces we used an experimental set-up (Fig. 1) in which the pen was attached to a swivel arm. The tractive force was varied by different loads (20–50 g) connected to the swivel arm with a string running over a cable run at the end of the table. The height of the pen measured from the surface was the same for both media. The pressure on the pen was kept constant for all trials using a counterweight at the other end of the swivel arm. Table 1 shows the writing velocity in mm/s for each of the conditions. The results clearly show a higher velocity for the plastic tipped pen on the tablet computer compared to a ballpoint pen on paper in all conditions. We conclude that the ballpoint pen on the paper has a higher friction compared to the plastic pen on the surface of the tablet computer screen. 2.4. Procedure Participants were seated comfortably in front of a table in a silent laboratory room. Half of the participants began with the tablet computer condition, the other half with the paper condition. Each condition started with a short warm-up, i.e. writing their first name and drawing circles around a dot, to become familiarized with the medium. In the beginning of the experiment, the pen was placed centrally in front of the participant to prevent any bias on handedness. One session took approximately 20 min. 2.5. Materials We examined the handwriting performance in three tasks with different complexity: (a) graphomotor abilities, (b) visuomotor abilities and (c) handwriting (copying the phrase ‘‘Sonne und Wellen” [German for ‘‘sun and waves”]). Each participant performed each task twice, on a tablet computer with a pen in condition (1) and in a separate session on paper attached to a digitizer in condition (2). The writing space and the order of tasks were kept parallel for both media. There were no time constraints for any of the tasks. In the following we will explain each task in more detail. 2.5.1. Graphomotor abilities We investigated the prerequisites of handwriting using 4 different continuous movement patterns because they constitute basic components of handwriting. In the first task, participants were asked to generate loop patterns twice. First the experimenter drew the loop pattern to illustrate the task (Fig. 2a) and then the participant had to repeat it on the next screen. For the other three tasks the picture to copy was presented in the upper half of the screen (Fig. 2b–d) and the experimenter did not show how to draw the pattern. The tasks required the participant to copy a loop pattern around dots (Fig. 2b), a zigzag line around dots (Fig. 2c) and a given staircase pattern around dots (Fig. 2d). Each task was conducted twice. We chose these three tasks to capture three basic handwriting patterns: round, diagonal and horizontal-vertical lines. The writing space for all 4 tasks had a size of 24.7 8.5 cm. 2.5.2. Visuomotor abilities In our study we took also into account the visuomotor abilities underlying handwriting measures. For that purpose, we selected two parts of the Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI) 6th Edition (Beery & Beery, 2010) because this test assesses the degree to which visual perception and finger-hand movements are coordinated in children and adults (e.g., during handwriting) (Volman, van Schendel, & Jongmans, 2006). We used the first nine forms of the Visual-Motor Integration (VMI) and the Motor Coordination (MC) tasks in our study. We chose only the first nine forms because, according to Weil and Amundson (1994), these are the typical forms that a child can master even before knowing how to write. For adults, they are well-learned and easy to perform. Moreover, the first nine items of the VMI are exactly the same geometric forms as those in the MC. Our first task, the VMI, requires the participants simply to copy the geometric forms (Fig. 3a). Usually the evaluation of the VMI score is achieved through the analysis of the product. A rater quantifies the drawings according to a rubric and sums
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Fig. 1. Experimental set-up to determine the difference in friction on pen movement speed between the two media using 4 different loads (20, 30, 40 and 50 g) to move the pen across the paper on the digitizer (or across the tablet computer in the tablet computer condition).
Table 1 Writing velocity (mm/s) on the two writing surfaces (tablet computer and paper) for four loads attached to the pen. Load on pen
Paper/ballpoint
Tablet computer/plastic
20 g 30 g 40 g 50 g Mean
2.67 5.48 24.36 39.14 17.91
2.94 25.57 49.65 62.43 35.15
Fig. 2. (a) Instruction: ‘‘Copy the loop pattern on the next screen.”, (b–d) Instruction: ‘‘Copy the pattern above in the space below.”
up a total score. However, we created a digital version of the first nine geometric forms of the VMI to conduct the task on a tablet computer and track the process of handwriting. In our version of the task, three forms were given in the upper half of the screen and the participants had to copy each form into a square directly below (Fig. 3c). Our second visuomotor task, the MC (Fig. 3b), requires the participants to trace a geometric form with a pencil without crossing a double-lined path. In our digital version, three figures were shown in the upper half of the screen and participants were asked to connect the dots in the form below with a pen, starting at the black dot. The figures in the upper half of the
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Fig. 3. The first nine geometric forms of the Visual-Motor Integration task (a) and the Motor Coordination task (b) (Beery & Beery, 2010). Writing space of the VMI (c) and MC (d).
page were given in a smaller scale than the writing space, in accordance with guidelines of the standardized test (Fig. 3d). Each square of writing space for both tasks had a size of 7.5 7.5 cm. 2.5.3. Handwriting Finally, we were interested in the underlying handwriting measures during the process of writing the phrase ‘‘Sonne und Wellen” (German for ‘‘sun and waves”). The phrase was presented in printed letters at the top of the screen (Fig. 4). We asked the participants to copy the phrase 10 times on lines (two screens with five lines each) in their own writing speed. We chose this phrase because it contains the letter combinations nn and elle, since during writing these letters the pen usually remains on the writing surface and these letter combinations constitute a simple continuous handwriting movement (as compared to e.g., ff). There were no constraints on the type of handwriting – printed or cursive. Each line was 15 cm long and the space between the lines was 2.4 cm (100 pixels). 2.6. Data analysis The x- and y-coordinates of the pen were recorded at a frequency of 133 Hz. In a first step we computed the velocity and number of inversions in velocity (NIVs) for the x- and y-coordinates using Perl v5 scripts. The velocity profiles of hand movements were smoothed by an implementation of the non-parametric kernel estimation (Marquardt & Mai, 1994) using the software R version 3.0.1 (R Development Core Team, 2013). The writing velocity was calculated by taking the first derivative of the x- and y-coordinates with respect to time. Even though the NIVs are sometimes calculated as the sum of all NIVs per up-stroke or down-stroke (Tucha et al., 2008), we computed the sum of NIVs occurring in the item because we did not exclusively test the writing of words. 2.7. Handwriting measures 2.7.1. Writing duration Writing duration is the time that the pen is on the screen surface of the tablet computer or paper (pressure > 0). We calculated the total duration it took participants to execute each task in milliseconds (ms). This gives an indication of temporal performance and is linked to average velocity (Rosenblum et al., 2003). 2.7.2. Writing velocity We computed the writing velocity in millimeter per second (mm/s). This measure is used to evaluate the fluidity in handwriting performance (Rosenblum et al., 2003).
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Fig. 4. Writing space for ‘‘Sonne und Wellen”.
2.7.3. In air time The digitizer can record the pen position at a distance of up to 1 cm above the surface. We measured the total in air time of the pen in ms. This measure indicates breaks in writing and might be linked to higher-level processes (Rosenblum et al., 2003; Sumner, Connelly, & Barnett, 2013). 2.7.4. Number of pen lifts The number of pen lifts determines how often the pen is lifted from the surface (pressure = 0) per item, including the final pen lift after finishing the item. We counted the number of pen lifts per pattern or phrase. A high number of pen lifts might reveal irregular and non-automatized writing. 2.7.5. Number of inversions in velocity (NIV) The number of inversions in velocity is related to the number of accelerations and decelerations during writing. The NIVs indicate the level of handwriting automaticity (Marquardt et al., 1996; Tucha et al., 2008). While low NIVs characterize an automatized and smooth movement, higher NIVs are associated with a lower degree of automaticity (Marquardt et al., 1996; Tucha et al., 2008). 2.8. Statistical analysis First, an outlier adjustment of the data was performed. We excluded data that were 3 standard deviations (SDs) above the group mean for all handwriting measures. These were mainly due to technical problems, misunderstandings of instructions or other external factors. For velocity we additionally excluded data 3 SDs below the group mean. Since the item complexity of the VMI and MC differed substantially, we excluded the data based on the mean of each item. We excluded 3.3% for the paper condition and 3.8% for the tablet computer condition. After the data exclusion via SDs for each condition we removed a data point in the data set for tablet computer if it was previously excluded for paper and vice versa, because we were only interested in the direct comparison of the surfaces between the media. This way we were able to use repeated-measures for the statistical analyses without the problem of having missing data. In total we excluded only 6.3% of the data. We analyzed the data using linear mixed-effect models for each task separately. The models were fit by maximizing the log-likelihood function with repeated measures using the software R version 3.0.1 (R Development Core Team, 2013) and the nlme-package (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2014). The five handwriting measures (dependent variables) were analyzed in separate models. The independent variable was the factor Medium (tablet vs. paper). Writing durations and the in air times were log-transformed in order to avoid skewed distributions. In addition to the p-value we will report the R2 for significant effects. This value describes the proportion of variance that is explained by the factor. R2 allows us to compare the effect sizes of different handwriting measures on different scales (e.g., time in ms, velocity in mm/s, etc.; Lakens, 2013). 3. Results 3.1. Analysis of graphomotor abilities Table 2 presents a summary of the descriptive data and statistical effects for the handwriting measures of the graphomotor abilities. The writing duration was longer for loops with dots when writing on paper compared to writing on a tablet computer (p = 0.026). Since the staircase pattern on paper was the only condition where participants used more than one
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Table 2 Means and standard deviations in parentheses for writing measures of the graphomotor abilities. The p-value refers to the comparison between the tablet computer and paper for the factor Medium. The R2 gives the effect size of significant effects. Writing duration (ms)
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
4760.90 (1273.86) 4549.95 (934.84) 0.216
0 0 –
1 1 –
104.55 (36.75) 125.06 (31.97) <0.001⁄ 0.45
34.88 (7.46) 32.3 (5.75) 0.021⁄ 0.20
8168.95 (2420.36) 7636.17 (2165.83) 0.026⁄ 0.19
0 0 –
1 1 –
43.03 (16.07) 53.17 (18.17) <0.001⁄ 0.55
43.36 (12.50) 42.63 (9.90) 0.249
Zigzag lines Paper Tablet computer p-value R2
7938.73 (2511.17) 7552.21 (2467.75) 0.188
0 0 –
1 1 –
40.54 (15.37) 51.83 (18.95) <0.001⁄ 0.64
42.52 (12.70) 40.50 (10.45) 0.211
Staircase pattern Paper Tablet computer p-value R2
8301.65 (2647.80) 7663.69 (2462.25) 0.051
48.86 (288.61) 0 –
1.10 (0.57) 1 –
20.76 (8.14) 26.78 (10.32) <0.001⁄ 0.60
48.88 (18.13) 44.43 (15.60) 0.047⁄ 0.18
Loops without dots Paper Tablet computer p-value R2 Loops with dots Paper Tablet computer p-value R2
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
pen lift, we did not run any analyses comparing in air time and number of pen lifts on tablet versus paper for the graphomotor ability tasks. The writing velocity was higher on the tablet computer compared to paper for all four tasks (all p < 0.001). The participants produced significantly fewer NIVs on the tablet computer than on paper for loops without dots (p = 0.021) and the staircase pattern (p = 0.047). Importantly, the writing velocity and the NIVs for all of the four tasks are correlated (Pearson’s r for loops without dots r = 0.33, p < 0.001; Kendall’s tau coefficient for loops with dots s = 0.62, p < 0.001; zizag lines s = 0.59, p < 0.001; staircase pattern s = 0.68, p < 0.001). This result shows that a smoother writing movement (=higher velocity) produces fewer NIVs (=more automatized and smoother movement) (Meulenbroek & Van Galen, 1990). The effect size (indicated by R2) shows that, depending on the task, writing velocity explains between 45% and 64% of the variance in the data for the comparison between the tablet computer and paper. 3.2. Analysis of visuomotor abilities We evaluated the VMI and MC tasks according to the manual of the Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI) 6th Edition (Beery & Beery, 2010). Two raters visually quantified each drawing according to the rubric of the test manual. The total test score constitutes how accurately the participants copied the forms. The scores of both tasks yielded a performance at ceiling for our participants. The mean score for the VMI performed on paper was 9 (out of 9) and on the tablet computer 8.97 (t(29) = 1, p = 0.33), the mean MC score on paper was 9 (out of 9) and on the tablet computer 8.87 (t (29) = 2.11, p = 0.04). The results of the paired t-tests show that there is no difference between the media for the performance of the VMI but there is for the performance of the MC. Participants performed significantly less well on the tablet computer for this task. Table 3 shows a summary of the descriptive data and statistical effects for the handwriting measures of the tests of visuomotor abilities. The participants wrote longer on the tablet computer than on paper only for the MC (p = 0.028, R2 = 0.19). The velocity was higher on the tablet computer than on paper only for the VMI (p < 0.001). The correlation analysis between the velocity and the NIVs yields Kendall’s tau coefficients for VMI s = 0.40 and MOC s = 0.34. Similar to the results obtained for the graphomotor abilities, these correlations reveal an inverse relationship between the writing velocity and the NIVs. For VMI the velocity explains 38% of the variance in the data for the comparison between the tablet computer and paper. Since both tasks involved the same set of items, we ran another analysis with the additional factor Task (VMI vs. MC) to check whether the tasks differed in graphomotor demands for the handwriting measures (see Table 4). We found one main effect for Medium (tablet computer vs. paper) for writing velocity. The factor Task yielded significant differences between VMI and MC for writing duration, writing velocity and NIVs (all p < 0.001). Compared to VMI, for MC the participants wrote longer (VMI: 1841.86 ms, MC: 2763.59 ms), slower (VMI: 32.01 mm/s, MC: 21.10 mm/s) and produced more NIVs (VMI: 11.32, MC: 16.93). Interestingly, the interaction of Task and Medium was significant for writing velocity (participants wrote faster on the tablet computer than on paper for the VMI, but there was no difference for MC).
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Table 3 Means and standard deviations in parentheses for writing measures of the visuomotor abilities. The p-value refers to the comparison between the tablet computer and paper for the factor Medium. The R2 gives the effect size of significant effects. Writing duration (ms) Visual-Motor Integration Paper Tablet computer p-value R2
(VMI) 1830.15 (1243.23) 1792.31 (1300.92) 0.562
Motor Coordination (MC) Paper 2582.54 (1546.54) Tablet computer 2938.22 (1694.12) p-value 0.028⁄ R2 0.19
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
219.59 (400.72) 200.85 (359.22) 0.586
1.45 (0.80) 1.35 (0.62) 0.104
29.88 (19.19) 37.04 (23.89) <0.001⁄ 0.38
11.79 (7.80) 10.51 (7.78) 0.107
199.66 (380.76) 232.25 (429.96) 0.682
1.40 (0.73) 1.41 (0.71) 0.946
21.53 (11.40) 21.00 (10.29) 0.964
16.77 (9.98) 17.02 (10.55) 0.969
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
Table 4 Summary of statistical effects for the factors Medium (tablet computer vs. paper), Task (VMI vs. MC) and the interaction Medium Task.
Medium Task Interaction
Writing duration (ms)
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
p = 0.230 p < 0.001⁄ p = 0.055
p = 0.932 p = 0.799 p = 0.500
p = 0.233 p = 0.775 p = 0.261
p < 0.002⁄ p < 0.001⁄ p < 0.001⁄
p = 0.312 p < 0.001⁄ p = 0.403
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
3.3. Analysis of graphomotor abilities of handwriting Table 5 presents a summary of the descriptive data and statistical effects for the handwriting measures of writing the phrase ‘‘Sonne und Wellen”. We found significant differences between the tablet computer and paper for all five handwriting measures. The participants wrote longer (p < 0.001), had a longer in air time (p = 0.002), lifted the pen more often (p = 0.031), wrote quicker (p < 0.001) and produced more NIVs (p = 0.010) on the tablet computer compared to paper. The correlation between velocity and NIVs yielded a Kendall’s tau coefficient of s = 0.20, revealing that a faster velocity is associated with fewer NIVs. Notably, the participants showed a longer writing duration and a higher velocity on the tablet computer compared to paper. This apparently paradoxical result, that participants wrote faster but longer on the tablet computer, turned out to be a consequence of letter size. A paired t-test revealed that the participants wrote larger letters on the tablet computer (M: 1.47 cm, SD: 0.29 cm) compared to paper (M: 1.20 cm, SD: 0.32 cm; t(299) = 18.20, p < 0.001). The effect sizes for the different measures reveal that writing duration explains 71% of the data for the comparison of the tablet computer versus paper, velocity explains 59%, the in air time 33%, the NIVs 24% and the number of pen lifts only 18%.1 To test if the participants adapt to the smoother surface of the tablet computer, we ran an additional linear mixed-effects model with the pressure of the pen as dependent variable. The independent repeated measure was item number in increasing order. We hypothesized that if participants adapt to the smoother surface in the course of the task then the pressure should decrease while writing the phrase 10 times, indicating a change in graphomotor control and an adaptation to the surface. Since the tips of the pens differed slightly according to the medium (inked vs. non-inked pen) we ran the analysis for each medium separately. For both media the pressure significantly (Paper: p < 0.001; Tablet computer: p < 0.001) declined from the first (mean pressure for the first item on paper: M: 0.49, SD: 0.11; on the tablet computer: M: 0.76, SD: 0.09) to the last item (Paper: M: 0.46, SD: 0.12; Tablet computer: M: 0.71, SD: 0.08) of writing repetitively the same phrase in accordance with our predictions (Fig. 5). 4. Discussion The present study investigated whether the writing surface influences the handwriting process in skilled writers when they write on a tablet computer or on paper. Furthermore we sought to show if skilled writers can adapt their graphomotor execution during writing to the rather unfamiliar surface such as a tablet computer. Our results demonstrate that it makes a difference if experienced writers write on a tablet computer in comparison to paper. However, the difference in handwriting performance depends on the task and the specific writing demands of the task. Our study also reveals an adaptation of handwriting movements to the smoother surface of the tablet computer during the tasks. Again, the degree of adaptation is dependent on the specific features of the task. We will interpret our findings in more detail in the following sections.
1
The percentages do not add up to 100% because each measure was used as a dependent variable in a separate model.
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Table 5 Means and standard deviations in parentheses for writing measures of writing the phrase ‘‘sun and waves”. The p-value refers to the comparison between the tablet computer and paper for the factor Medium. The R2 gives the effect size of significant effects. Writing duration (ms) Writing ‘‘sun and waves” Paper 5039.54 (1414.62) Tablet computer 5872.78 (1823.93) p-value <0.001⁄ 2 R 0.71
In air time (ms)
Number of pen lifts
Velocity (mm/s)
NIVs
1550.43 (642.99) 1741.86 (617.29) 0.002⁄ 0.33
8.83 (3.31) 8.51 (3.25) 0.031⁄ 0.18
30.46 (7.90) 35.88 (8.93) <0.001⁄ 0.59
56.45 (15.63) 59.68 (15.67) 0.010⁄ 0.24
Note: The asterisk indicates significant effects below an alpha-level of 0.05.
Fig. 5. Mean of pen pressure for each item of ‘‘sun and waves” on paper and on the tablet computer (with 95% confidence intervals).
4.1. Handwriting performance on the tablet computer versus paper We found faster writing velocity for all tasks on the tablet computer compared to paper. These findings show that the pen was apparently sliding more on the smoother surface of the tablet computer. Consequently, the participants needed to adapt their motor execution to achieve fluent and regular handwriting. Additional post-hoc analyses of the effect size for the handwriting measures revealed that writing velocity accounted for nearly half of the variance in the tasks. Therefore, the difference between writing on a tablet computer and on paper is mostly explained by the writing velocity. We used three writing tasks with different complexity. The first task – graphomotor abilities – required copying continuous patterns. For all four patterns we found higher writing velocity on the tablet computer compared to paper. Additionally, the loops with dots showed a longer writing duration for paper. This results from the fact that participants drew loops wider and taller on paper than on the tablet computer. In other words, the pen passed a longer pathway in a shorter time, hence the writing duration was long and the velocity was high. Seemingly, our participants were more careful when copying the loops with dots compared to the other three tasks because they focussed their visual attention on drawing the loops around the dots (longer writing duration for paper) and the pen was sliding more on the smoother surface of the tablet computer (higher velocity on the tablet computer). When comparing the two tasks which probed the tests of visuomotor abilities – VMI and MC – we obtained a main effect of task (see Table 4). Furthermore, we found interactions of task and medium for the writing duration, the writing velocity and the NIVs. These results suggest that the task demands on the tablet computer were higher for MC compared to VMI. This is unsurprising since the MC required participants to stay in a particular writing space. This draws attention to the writing process and is similar to previous findings about mental or visual tracking of the writing process (Marquardt et al., 1996; Tucha & Lange, 2005; Tucha et al., 2008). Consciously attending to the handwriting movements hampers the automaticity of writing and leads to slower execution. Our last task examined participants’ actual handwriting. In this task they were asked to copy a phrase in their own handwriting. This task is different from the two previous tasks because participants did not have to copy a pattern. They only had to plan and execute the necessary motor movements for the letters of the phrase ‘‘Sonne und Wellen” from memory and write the phrase onto five lines twice. In our view this is a very easy task for experienced writers, hence this task probes handwriting automaticity on two different media. The results showed task-dependent differences for all five handwriting measures selectively for the comparison between the tablet computer and paper. We will discuss these findings in more detail in the following paragraph.
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4.2. Adaptation of handwriting performance Our study reveals that skilled writers are able to adapt their handwriting movements to the writing surface. In the handwriting task the results show longer writing duration and higher writing velocity on the tablet computer compared to paper. An analysis of the letter sizes produced a larger writing product on the tablet computer. Therefore, the time of the pen on the surface (=writing duration) was longer but the execution of the writing was faster (=writing velocity). A possible explanation for this finding might be the adaptation of the graphomotor execution to the smoother surface of the tablet computer compared to paper. The smoother surface of the tablet computer presumably needs to be countered with increased attention to the motor execution of the handwriting movements. We suggest that writers who are automatized in handwriting (‘‘who have had regular and long experience in comparison to children”, Chanquoy & Negro, 1996, p. 556) compensate for the decrease in friction between the pen and the writing surface by increasing their writing velocity and enlarging the letter size, which is similar to findings by Alamargot and Morin (2015) and Denier van der Gon and Thuring (1965). Another clue for our participants’ adaptation to the surface is a modulation in pen pressure during the execution of our handwriting task (copying ‘‘Sonne und Wellen”), in line with previous research (Wann & Nimmo-Smith, 1991). At the beginning of the task, execution involved higher pen pressure with the tip on the surface and during the course of the task the pen pressure declined. The handwriting task was the last task in our study, hence the participants were probably already familiar with the writing surface. But this task actually depicts handwriting performance while the previous tasks merely measured handwriting prerequisites. It might have been easier for the participants to use an automatized handwriting routine while executing this last, more natural task because they were less constrained in their performance, since in the previous tasks they either had to copy patterns or to stay in a particular writing space. In other words, the participants did not consciously concentrate on the handwriting movements because writing words is an everyday skill for which they have achieved automatized execution. Therefore, we interpret the decline in pen pressure during this task as an adaptation to the writing surface. Interestingly, these findings are in contrast to some results obtained in the study by Alamargot and Morin (2015). The 9th graders in their study increased the pen pressure to adapt to the smoother surface of the tablet computer. The authors interpret this as a disturbance in the ‘‘online regulation of initial motor commands” (Alamargot & Morin, 2015, p. 38). Nevertheless, the 9th graders’ adaptation was not sufficient since they exhibited less legible letters and longer pauses on the tablet compared to paper. This is probably due to a lower degree of automatization in graphomotor execution of 9th graders compared to the adults in our study. It might be that at this stage in handwriting perceptual feedback still plays an essential role in controlling handwriting movements (Bourdin & Fayol, 1994; Grabowski, 2010). We hypothesize that this may be a reason for a slower adaptation to a smoother writing surface.
5. Conclusions The results of our study provide a first answer to the still open question of whether the writing surface influences skilled writers’ execution of writing movements. We found differences between writing on a tablet computer compared to writing on paper which were partly modulated by the writing task. In general, we found a higher writing velocity when writing on a tablet computer. Apparently even experienced writers, such as most adults, are influenced by the friction of the writing surface. Nevertheless they are able to adapt quite quickly – even within ten items of copying a given word phrase. Our findings corroborate previous research that shows that writers compensate for a smoother writing surface with larger letters and a modulation in pen pressure. It would be interesting to see whether less skilled writers or even students who have not acquired handwriting yet are sensitive to the writing surface in a similar way and whether they are able to adapt their handwriting movements. As mentioned above, our findings were partly task-dependent. When copying basic and continuous graphomotor patterns, we found mixed results depending on having to focus attention on drawing around dots or not. In our second task – testing of visuomotor abilities – we clearly saw that the more demanding task (the MC) hampered the automaticity of the writing movements because participants had to focus their attention on the writing process to stay in the limited writing space. Finally, the results of our actual handwriting task show a clear adaptation to the writing surface. Participants wrote letters quicker and larger on the tablet computer and exhibited more NIVs when writing on the tablet computer. The explanation for this might be higher graphomotor control when writing on a smoother surface. Our observations have important implications for handwriting research. For example, we doubt that it is wise to simply digitize a paper-pencil-version of a test to obtain measures of the handwriting process. The difference in task demands might lead to unexpected and spurious results which are mostly related to the writing surface itself. Nevertheless we recommend using tablet computers in writing research to evaluate the process of handwriting in a more sophisticated manner than by assessing only the writing product.
Acknowledgements This research was funded by the Land Brandenburg, Germany. We thank the students at the University of Potsdam who participated in our study, as well as Elisabeth Fleischhauer, Sophia Czapka and David Ziegert for their help with the data
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