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Research Article
Issue Date: November/December 2016
Published Online: October 10, 2016
Updated: January 01, 2021
Predicting Handwriting Legibility in Taiwanese Elementary School Children
Author Affiliations
  • Tzu-I Lee, MS, is Master’s Student, School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei
  • Tsu-Hsin Howe, PhD, is Associate Professor, Department of Occupational Therapy, Steinhardt School of Culture, Education, and Human Development, New York University, New York
  • Hao-Ling Chen, PhD, is Associate Professor, School of Occupational Therapy, College of Medicine, National Taiwan University, and Occupational Therapist, Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei
  • Tien-Ni Wang, PhD, is Assistant Professor, School of Occupational Therapy, College of Medicine, National Taiwan University, and Occupational Therapist, Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei; tnwang@ntu.edu.tw
Article Information
Pediatric Evaluation and Intervention / School-Based Practice / Children and Youth
Research Article   |   October 10, 2016
Predicting Handwriting Legibility in Taiwanese Elementary School Children
American Journal of Occupational Therapy, October 2016, Vol. 70, 7006220020. https://doi.org/10.5014/ajot.2016.016865
American Journal of Occupational Therapy, October 2016, Vol. 70, 7006220020. https://doi.org/10.5014/ajot.2016.016865
Abstract

This study investigates handwriting characteristics and potential predictors of handwriting legibility among typically developing elementary school children in Taiwan. Predictors of handwriting legibility included visual–motor integration (VMI), visual perception (VP), eye–hand coordination (EHC), and biomechanical characteristics of handwriting. A total of 118 children were recruited from an elementary school in Taipei, Taiwan. A computerized program then assessed their handwriting legibility. The biomechanics of handwriting were assessed using a digitizing writing tablet. The children’s VMI, VP, and EHC were assessed using the Beery–Buktenica Developmental Test of Visual–Motor Integration. Results indicated that predictive factors of handwriting legibility varied in different age groups. VMI predicted handwriting legibility for first-grade students, and EHC and stroke force predicted handwriting legibility for second-grade students. Kinematic factors such as stroke velocity were the only predictor for children in fifth and sixth grades.

Handwriting is considered an essential ingredient for successful participation in school life, because children spend up to half of their classroom time engaged in paper-and-pencil tasks each day (Engel-Yeger, Nagauker-Yanuv, & Rosenblum, 2009). Tasks such as taking notes and tests, doing assignments or homework, and writing papers also require handwriting and are often performed under time constraints and over prolonged periods (Rosenblum, Weiss, & Parush, 2004).
Handwriting problems were reported to be the most common referral for school-related services, the prevalence of which has been estimated to be from 10% to 34% (Karlsdottir & Stefansson, 2002; Rosenblum et al., 2004). Handwriting difficulties significantly influence children’s academic advancement and self-efficacy (Engel-Yeger et al., 2009; Feder & Majnemer, 2007). Studies have reported that handwriting performance, particularly legibility, is significantly correlated with academic success (Cahill, 2009; Feder & Majnemer, 2007; Medwell & Wray, 2008). Several authors have suggested that poor handwriting legibility may influence perceptions of children’s competence as writers because teachers’ judgments on the content of a paper are influenced by legibility (Graham & Weintraub, 1996; Rosenblum et al., 2004). Other authors have suggested that handwriting difficulties in early years may lead to arrested writing development. The production of legible handwriting involves the simultaneous processing of motor and cognitive demands. Developmentally, the motor aspects of handwriting become automated, enabling the child to attend to higher-order cognitive processes related to composition of text. Failure to do so can have negative effects on the fluency and quality of writing (Wallen, Duff, Goyen, & Froude, 2013).
Various factors associated with handwriting performance have been reported in the literature. Visual–motor integration, visual perception, and eye–hand coordination were among the identified contributors to writing performance (Cheung, 2007; Feder & Majnemer, 2007; Kaiser, Albaret, & Doudin, 2009; Klein, Guiltner, Sollereder, & Cui, 2011; Li-Tsang et al., 2012; Rosenblum, Weiss, & Parush, 2003; Volman, van Schendel, & Jongmans, 2006). However, the association between these contributors and writing performance as a function of age is not clear or conclusive.
Writing in Chinese may present a different set of problems in handwriting. Because Chinese characters are characterized by their logographic nature, the ability to write Chinese proficiently requires a person to not only master the multiple stroke sequences and directions (Ng & Wu, 1990) but also meet the demands of visual discrimination of the fine differences in the forms and positions of strokes. Writers must understand spatial organization to write characters legibly with appropriate positioning of strokes and proportioning of radicals (Chang, Yu, & Shie, 2009). Alphabetic languages emphasize smoothness and continuity in their written forms (Rosenblum, Parush, & Weiss, 2003) as opposed to Chinese, which has characters that require sharp turns of stroke and frequent pen lifts (Tseng, 1998).
Most existing handwriting studies have addressed Western alphabetic systems. The findings of these studies may not be transferable or directly applicable to handwriting in Chinese because of the differences not only between form and components of Chinese and alphabetic characters but also in customs and cultural backgrounds (Chang et al., 2009). As a result, studying handwriting performance in Chinese has its own merits. Identifying the related factors associated with legible handwriting may assist clinicians and educators to develop guidelines for intervention.
The purpose of this study was to examine handwriting performance, particularly legibility, in elementary school children from first through sixth grades and to identify predicting factors of handwriting performance for these children. Specifically, we aimed to explore any developmental changes in handwriting legibility and to examine whether predicting factors of legibility were different among students in different grade levels. For this study, participants were categorized into four groups: (1) first grade, (2) second grade, (3) third and fourth grade, and (4) fifth and sixth grade. Instead of grouping the children in first and second grade together, we divided them into two groups, assuming rapid changes in handwriting performance developmentally between the two grades (Chang & Yu, 2013; Overvelde & Hulstijn, 2011).
Method
Participants
We recruited a convenience sample of 118 typically developing children (50 boys and 68 girls) from a local mainstream elementary school in Taipei, Taiwan (Table 1). Children with special needs; who were diagnosed with developmental delay, neurological deficits, physical or mental disability; who presented with behavioral, emotional, and sensory processing problems; or with an IQ of less than 70 were excluded from this study. Permission to conduct the study was provided by the institutional human research ethics committee of the university. Informed consent from parents and assent from participating children were obtained before data collection.
Table 1.
Participant Demographics and Group Outcome Measures
Participant Demographics and Group Outcome Measures×
M (SD) or n
VariableFirst Grade (n = 28)Second Grade (n = 23)Third and Fourth Grades (n = 35)Fifth and Sixth Grades (n = 32)F (3, 114)pPost Hoc Analysis (LSD)a
Characteristic
Male1471514
Female14162018
Age, mo83.79 (3.74)94.65 (3.98)114.91 (6.19)137.35 (7.86)
Measure
CLA (legibility)46.30 (2.17)46.74 (3.03)47.94 (5.25)47.50 (4.08)1.06.369
VMI (visual–motor integration)17.75 (2.22)19.43 (1.67)19.57 (3.01)23.72 (2.19)34.47<.015th & 6th > 3rd & 4th = 2nd > 1st
VP (visual perception)21.11 (2.73)22.78 (2.26)22.93 (2.22)25.16 (1.63)16.87<.015th & 6th > 3rd & 4th = 2nd > 1st
MC (eye–hand coordination)19.04 (3.45)20.52 (4.02)21.66 (3.43)24.56 (2.80)13.98<.015th & 6th > 3rd & 4th > 1st & 5th & 6th > 2nd
Kinetic/kinematic analysis
Task completion time, s164.36 (37.59)89.98 (17.74)77.61 (21.41)56.85 (20.06)96.37<.015th & 6th < 3rd & 4th = 2nd < 1st
Mean stroke velocity, pixels/s65.76 (19.67)101.96 (32.67)104.38 (36.33)154.41 (49.13)28.446<.015th & 6th > 3rd & 4th = 2nd > 1st
Mean stroke force (max = 1024)656.49 (153.68)686.79 (169.35)713.68 (138.08)640.04 (160.29)1.42.240
Pause time per stroke, s0.38 (0.13)0.16 (0.05)0.16 (0.10)0.08 (0.06)55.31<.015th & 6th < 3rd & 4th = 2nd < 1st
Table Footer NoteNote. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Noteap < .05.
p < .05.×
Table 1.
Participant Demographics and Group Outcome Measures
Participant Demographics and Group Outcome Measures×
M (SD) or n
VariableFirst Grade (n = 28)Second Grade (n = 23)Third and Fourth Grades (n = 35)Fifth and Sixth Grades (n = 32)F (3, 114)pPost Hoc Analysis (LSD)a
Characteristic
Male1471514
Female14162018
Age, mo83.79 (3.74)94.65 (3.98)114.91 (6.19)137.35 (7.86)
Measure
CLA (legibility)46.30 (2.17)46.74 (3.03)47.94 (5.25)47.50 (4.08)1.06.369
VMI (visual–motor integration)17.75 (2.22)19.43 (1.67)19.57 (3.01)23.72 (2.19)34.47<.015th & 6th > 3rd & 4th = 2nd > 1st
VP (visual perception)21.11 (2.73)22.78 (2.26)22.93 (2.22)25.16 (1.63)16.87<.015th & 6th > 3rd & 4th = 2nd > 1st
MC (eye–hand coordination)19.04 (3.45)20.52 (4.02)21.66 (3.43)24.56 (2.80)13.98<.015th & 6th > 3rd & 4th > 1st & 5th & 6th > 2nd
Kinetic/kinematic analysis
Task completion time, s164.36 (37.59)89.98 (17.74)77.61 (21.41)56.85 (20.06)96.37<.015th & 6th < 3rd & 4th = 2nd < 1st
Mean stroke velocity, pixels/s65.76 (19.67)101.96 (32.67)104.38 (36.33)154.41 (49.13)28.446<.015th & 6th > 3rd & 4th = 2nd > 1st
Mean stroke force (max = 1024)656.49 (153.68)686.79 (169.35)713.68 (138.08)640.04 (160.29)1.42.240
Pause time per stroke, s0.38 (0.13)0.16 (0.05)0.16 (0.10)0.08 (0.06)55.31<.015th & 6th < 3rd & 4th = 2nd < 1st
Table Footer NoteNote. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Noteap < .05.
p < .05.×
×
Procedure
Participants were asked to copy two preprinted stimulus cards, one with 26 Chinese characters in a grid, copied onto an A4-size paper sheet, and one with 15 Chinese characters in a grid, copied onto a digitizing writing tablet (Intuos5, Wacom Technology Corp., Portland, OR). The respective preprinted stimulus cards were placed at the top of the test sheet and the digitizing writing tablet. The Chinese characters were selected from a pool of 179 characters that were being taught in the first-grade curriculum in Taiwan and were listed as high-frequency words.
Participants copied the set of 26 characters onto a preprinted blank grid (each square, 3.6 × 3.6 cm) on the paper sheet, positioned in landscape orientation, using a black-ink ballpoint pen with a 1.0-mm line size. In addition, a preprinted blank grid on an A4-size paper sheet was affixed to the surface of a digitizing writing tablet, and participants used a wireless electronic pen with a pressure-sensitive tip (Intuos5 Touch Pro Pen; Wacom Technology Corp.) to copy the 15 characters on the tablet. The weight and size of the electronic pen used was similar to those of the mechanical pen that the participants commonly used for their schoolwork.
Participants were seated in an armchair with the sheet of paper or the digitizing writing tablet in front of them on the top of a desk. They could adjust the chair into a comfortable position. The order in which participants wrote on either the sheet of paper or the digitizing writing tablet was randomly preassigned for each participant. The writing tasks were designed for participants to complete within 5 min to minimize possible confounding variables, such as muscle fatigue and poor sustained attention.
All assessments were administered in a quiet room in the Department of Occupational Therapy at the National Taiwan University, Taipei. All assessments, including the writing samples, took approximately 30 min for each participant to complete, and all were completed during the same visit. Data collection was completed within 2 wk to prevent possible exposure and time effects of real change in the handwriting abilities.
Measures
Legibility.
The Computerized Legibility Assessment (CLA), written in the MATLAB language (Version 8.1; MathWorks, Natick, MA), was developed by the authors and designed to quantify legibility by measuring the resemblance between a writing sample and the standard model. The 26 Chinese characters copied by the participants onto the sheet of paper were scanned and analyzed. In writing Chinese multistroke characters, the improper placement of strokes results in a messy script and poor legibility. The resemblance between the handwritten sample and standard model is indicative of correct length, orientation, and placement of every stroke. The components that characterize appropriate letter formation for legible and neat handwriting, including intercharacter space and alignment, were taken into consideration while measuring resemblance. Thus, in this study, we used the resemblance measurement to represent level of legibility.
This legibility assessment program used a template-matching approach to measure the resemblance, similar to other computer-assisted handwriting programs such as the Chinese Handwriting Assessment Program (Chang et al., 2009). Template matching involves first matching a participant’s handwritten character to a standard character (i.e., the template; Figure 1A) and then superimposing the template over the written sample (resizing the template if necessary to match the written sample’s size) and computing the similarity between the two (Figure 1B). The cross-correlation coefficient was used to calculate the resemblance. When the template and the handwritten character are similar, the cross-correlation is high. The average scores of the resemblance obtained from the 26 Chinese characters multiplied by 100 were used in this study to represent a participant’s handwriting legibility.
Figure 1.
Template matching involves (A) matching a handwritten character (right) to a standard character (left) and then (B) superimposing the template over the handwritten character and computing similarities between the two.
Figure 1.
Template matching involves (A) matching a handwritten character (right) to a standard character (left) and then (B) superimposing the template over the handwritten character and computing similarities between the two.
×
The CLA demonstrated moderate to strong convergence. We asked two experts, one elementary school teacher and one experienced pediatric occupational therapist, to rank each participant’s legibility on a 3-point scale, ranging from 1 (not acceptable) to 3 (good legibility). Polyserial correlations were calculated between the experts’ ranking and resemblance scores obtained from the CLA to represent convergent validity. The correlation between resemblance scores and the teacher was .72; the correlation between those scores and the therapist was .56 (Cox, 1974; Drasgow, 1986).
Visual–Motor Integration, Visual Perception, and Eye–Hand Coordination.
The Chinese version of the Beery–Buktenica Developmental Test of Visual–Motor Integration (VMI) and its Supplemental Developmental Tests of Visual Perception (VP) and Motor Coordination (MC; Beery, 1997; Liu & Lu, 1999) were used to assess participants’ visual–motor integration, visual perception, and eye–hand coordination skills. The VMI consists of 27 geometric forms to be copied; forms are in a developmental sequence and increase in complexity as the test is administered. The scores obtained from this test were used to represent participants’ visual–motor integration skills. The supplemental tests contain the same forms but are used to identify visual analysis and motor coordination difficulties (Beery, 1997). Specifically, the VP assesses a child’s visual analysis and visual–spatial skills in a motor-reduced fashion by asking the child to identify each matching form. Scores obtained from this test were used to represent participants’ visual–perception skills. The MC focuses on a child’s motor integration skill by asking the child to “trace” each form by connecting dots within provided paths (Sortor & Kulp, 2003). Scores obtained from this test were used to represent participants’ eye–hand coordination skills.
The Chinese version of the VMI and its supplemental tests have been reported to have sound psychometric properties (Liu & Lu, 1999). The interrater reliabilities of visual–motor integration, visual perception, and motor coordination were .96, .94, and .96, respectively. Test–retest reliabilities of visual–motor integration, visual perception, and motor coordination in an interval of 2 mo were .91, .83, and .85, respectively. The construct validities between age and these three tests were .86, .79, and .81, respectively (Liu & Lu, 1999).
Kinetic and Kinematic Analysis of Handwriting.
Kinetic and kinematic (i.e., biomechanical) handwriting data were collected using the digitizing writing tablet (with a sampling rate maximum of 200 Hz and a 5,080-dpi spatial resolution). The kinetic and kinematic parameters were collected from the tablet and software program in the MATLAB language that was written to analyze children’s performance. The following parameters were collected:
  • Task completion time, which was calculated in seconds from the first stroke to the last. A writing stroke was defined as the movement occurring between two zero crossings of the velocity function.

  • Stroke velocity, which was obtained by dividing the stroke length by the on-paper time during one stroke. The magnitude of stroke velocity indicates the speed of writing per stroke. The mean stroke velocity was derived from the average of all strokes in all of the writing tasks.

  • Stroke force, which was calculated by dividing the sum of the axial pen tip force by the number of sampling points of each stroke. A larger magnitude of stroke force indicates that the child put more pressure on the paper. The mean stroke force was calculated. In this study, we only recorded the axial pen force in the middle four-fifths of a stroke to avoid the large variations found in the starting and ending force of a stroke (Chang & Yu, 2013).

  • Pause time per stroke, which was derived by dividing the cumulative pause time period by the total stroke number.

Statistical Analysis
Descriptive and univariate analyses were conducted for all variables using IBM SPSS Statistics (Version 18; IBM Corp., Armonk, NY). The significant level was set at .05. Pearson correlation coefficients were calculated to test the relation between handwriting legibility and its relevant components, including both biomechanical and neuropsychological factors. One-way analysis of variance (ANOVA) was conducted to test whether the difference in the outcome among the four groups (i.e., first grade, second grade, third and fourth grade, and fifth and sixth grade) was significant, and subsequent post hoc analysis (Fisher’s least significant difference) was conducted when the model was significant. Linear stepwise regression analyses were conducted to examine the predicting factors of handwriting legibility in the four groups.
To ensure the quality of the results, we used the basic model-fitting techniques for variable selection, goodness-of-fit (GOF) assessment, and regression diagnostics in the regression analyses. The GOF measures include coefficients of the determination of R 2. Statistical methods for regression diagnostics, such as residual analysis, detection of influential cases, and check for multicollinearity, were applied to examine problems within the model or the data.
Results
Demographic Characteristics and Outcome Measures
Table 1 summarizes participant demographic data, the scores obtained from the VMI and its supplemental developmental tests, and biomechanical data collected during the writing process. One-way ANOVA was conducted to compare all outcome measures among the four groups. The results show that significant group differences were detected in participants’ visual–motor integration, visual perception, and eye–hand coordination, but not in the mean scores for legibility. Post hoc analysis demonstrates that children in fifth and sixth grades demonstrated better visual–motor integration, visual–perception, and eye–hand coordination skills than children in lower grades. Significant group differences were also found in kinetic and kinematic parameters, including task completion time, stroke velocity, and pause time per stroke. Children in fifth and sixth grades demonstrated significantly more efficient handwriting abilities than children in lower grades (i.e., they took less time to complete a writing task, demonstrated shorter pause time per stroke, and had significantly higher stroke velocity). No gender differences were found for legibility in the different groups.
Relationship Between Handwriting Legibility and Related Factors
Table 2 presents the overall correlations between handwriting legibility (measured by the CLA) and visual–motor integration (measured by the VMI), visual perception (measured by the VP), and eye–hand coordination (measured by the MC) as well as the kinetic and kinematic parameters of task completion time, mean stroke velocity, mean stroke force, and pause time per stroke. Results indicate that handwriting legibility was significantly associated with VMI scores (r = .38, p < .05) in first-grade participants and with eye–hand coordination (r = .48, p < .05) and mean stroke force (r = .49, p < .05) in second-grade participants. No significant correlation was found between handwriting legibility and any measure for the third- and fourth-grade group. For the fifth- and sixth-grade group, handwriting legibility was found to be significantly associated with task completion time (r = .56, p < .01), mean stroke velocity (r = –.68, p < .01), and pause time per stroke (r = .57, p < .01). In other words, when participants wrote with lower stroke velocity and took a longer time to complete their writing task with more pause time per stroke, their handwriting outputs were better.
Table 2.
Correlations Between Legibility and Outcome Measures
Correlations Between Legibility and Outcome Measures×
MeasureFirst GradeSecond GradeThird and Fourth GradesFifth and Sixth Grades
VMI.38*.01.13.33
VP.35.19.04−.04
MC.32.48*−.12.22
Kinetic/kinematic analysis
 Task completion time−.72.19.04.56**
Mean stroke velocity−.24−.34−.13−.68**
Mean stroke force.17.49*.04.31
Pause time per stroke.02.32.08.57**
Table Footer NoteNote. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
Table 2.
Correlations Between Legibility and Outcome Measures
Correlations Between Legibility and Outcome Measures×
MeasureFirst GradeSecond GradeThird and Fourth GradesFifth and Sixth Grades
VMI.38*.01.13.33
VP.35.19.04−.04
MC.32.48*−.12.22
Kinetic/kinematic analysis
 Task completion time−.72.19.04.56**
Mean stroke velocity−.24−.34−.13−.68**
Mean stroke force.17.49*.04.31
Pause time per stroke.02.32.08.57**
Table Footer NoteNote. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
×
Predicting Factors for Handwriting Legibility in Different Groups
Table 3 summarizes the predictive models for handwriting legibility in the four participant groups. Linear stepwise regression analyses were used to identify significant predictors of handwriting performance for each group. Because of the limited sample size for each group, we selected only the predictors that had moderate and high correlations with handwriting legibility (r > .20) for regression analyses for each age group. No multicollinearity was found among predictor variables.
Table 3.
Significant Predictors of Legibility for Each Group
Significant Predictors of Legibility for Each Group×
GroupSignificant Predictor
Intercept BVMIVPMCTimeSVForcePTSFdfsR2
First grade39.724**.370*4.3531, 26.143*
Second grade35.508**.229*.007*6.4622, 20.393*
Third and fourth grades
Fifth and sixth grades56.231**−.057**24.0951, 30.463*
Table Footer NoteNote. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
Table 3.
Significant Predictors of Legibility for Each Group
Significant Predictors of Legibility for Each Group×
GroupSignificant Predictor
Intercept BVMIVPMCTimeSVForcePTSFdfsR2
First grade39.724**.370*4.3531, 26.143*
Second grade35.508**.229*.007*6.4622, 20.393*
Third and fourth grades
Fifth and sixth grades56.231**−.057**24.0951, 30.463*
Table Footer NoteNote. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
×
The results indicate that the VMI was the only significant predictor of handwriting legibility for participants in first grade. The R2 value was .143, indicating that 14.3% of the variance in handwriting legibility could be explained by the VMI scores, F(1, 26) = 4.35, p < .05. Mean stroke force and eye–hand coordination skills were identified as significant predicators of handwriting legibility for participants in second grade, which accounted for 39.3% of the variance in handwriting legibility, F(2, 20) = 6.46, p < .05. No significant predictor was identified for the third- and fourth-grade group. Last, mean stroke velocity was identified as the only significant predictor of handwriting legibility, F(1, 30) = 24.10, p < .01, for the fifth- and sixth-grade group. The R2 value was .463, indicating that 46.3% of the variance in handwriting legibility could be explained by this predictor for participants in the fifth and sixth grades.
Discussion
In this study, we did not find any significant legibility differences as measured by CLA scores among different grade levels. However, previous studies have reported a deterioration in handwriting legibility usually observed in elementary school students after the fourth grade (Graham, Berninger, Weintraub, & Schafer, 1998; Hamstra-Bletz & Blöte, 1993), when teachers start to focus more on the content of students’ work rather than the quality of their handwriting (Lam, Au, Leung, & Li-Tsang, 2011). To meet heavy academic demands, students in fourth, fifth, and sixth grades may stop formally writing in class and pay less attention to their handwriting legibility and instead focus more on completing their work in a limited amount of time (Hamstra-Bletz & Blöte, 1993; Lam et al., 2011).
These different results may be because this study and the previous studies had different outcome measures for handwriting legibility and sample sizes. In addition, our study used Chinese characters and the other studies used the alphabet. Similar to the Western educational system, the class time in Taiwan that is focused on formal writing instruction decreases in Grades 4–6, but students continue to learn new Chinese characters throughout elementary school. Therefore, students are required to continuously follow all writing rules when learning these new characters and are consistently reminded to pay attention to their writing output.
The second purpose of this study was to investigate the handwriting characteristics and potential predictors of handwriting legibility for typically developing elementary school children in Taiwan. Even though we used Chinese characters, our findings were consistent with previous alphabetic studies that indicated that correlations between handwriting legibility and nonkinematic factors decreased with grade (Karlsdottir & Stefansson, 2002) and further demonstrated that correlations between handwriting legibility and kinematic parameters increased with grade. Although visual–motor integration and eye–hand coordination skills made significant contributions to the prediction of handwriting legibility in younger children (first and second grades), kinematic factors showed stronger association in children in fifth and sixth grades.
In this study, VMI score was the only significant predictor of handwriting legibility in children in first grade. Our result aligned with previous studies on alphabetic language, despite different language systems, which indicated that the relationship between handwriting performance and the VMI was only significant among beginner writers (Cornhill & Case-Smith, 1996; Daly, Kelley, & Krauss, 2003; Weil & Amundson, 1994) and that the influence of the VMI declined with age (Karlsdottir & Stefansson, 2002). Eye–hand coordination and mean stroke force were the two significant predictors of handwriting legibility for children in second grade. We found that in second grade, children who exerted greater pressure on the writing surface produced more legible products. This result highlighted the importance of motor control and kinesthetic perception when learning to write. Younger writers who engage in writing tasks develop motor control in their hands and fingers over time. Children who are learning handwriting may also need to rely on tactile and kinesthetic perception to write faster and more legibly. When children learn how to write, they have to grasp the writing tools appropriately and pay close attention to the feeling from their hand and finger positions and touch sense (Yu, Hinojosa, Howe, & Voelbel, 2012).
The biomechanical factors observed during the handwriting process, such as task completion time, mean stroke velocity, and pause time per stroke, were significantly related to handwriting legibility only in fifth- and sixth-grade study participants. The common assumption that speed and quality of handwriting are two inversely related quantities (Karlsdottir & Stefansson, 2002) was supported by the high positive correlation between legibility scores and task completion time (r = .56) and the high negative correlation between legibility scores and mean stroke velocity (r = –.68) in this study.
Our results supported the typical handwriting development as reported by other investigators (Feder & Majnemer, 2007; Overvelde & Hulstijn, 2011). Our study sample demonstrated an increase in writing speed from grade to grade, as evidenced by their increased stroke velocity and decreased time to complete the writing task (Table 1). Previous investigators have proposed that handwriting abilities become automatic, organized, and mature in children in fifth and sixth grades, allowing them to choose whether to write legibly or quickly (Lam et al., 2011; Overvelde & Hulstijn, 2011). Because the academic demands of fifth and sixth grades are higher, children are required to have more efficient handwriting skills, particularly in terms of speed, to complete their homework and examinations in a reasonable amount of time (Lam et al., 2011). This inevitable trade-off between writing with quality and writing with speed occurs in children in fifth and sixth grades.
No factor was identified as a significant contributor to handwriting legibility in children in third and fourth grades. A possible reason for this result might be that children at this age are in a transitional period. Although the predictor of handwriting legibility in junior grades was a nonkinematic-related factor (e.g., the VMI), the predictor of legibility in children in fifth and sixth grades was a kinematic parameter (e.g., stroke velocity). This shift of handwriting focus during the developmental process might explain the lack of predictors for children in third and fourth grades in our study.
Implications for Occupational Therapy Practice
The results of this study have the following implications for occupational therapy practice:
  • Predictors of handwriting legibility vary among different age groups in elementary school children (e.g., integrating visual–perceptual processing, eye–hand coordination, and stroke force predicted legibility in first- and second-grade participants and biomechanical factors were the best predictors of legibility in fifth- and sixth-grade participants).

  • Because predictors vary, when clinicians evaluate a child’s handwriting performance, they must consider the influential factors for legibility for the child’s age.

Study Limitations and Future Research
This study has several limitations, which point to possibilities for future research. First, the Chinese characters selected as stimuli were drawn from the first-grade curriculum. Therefore, the handwriting process for children in higher grades would be automatic because they have learned and have been familiar with these characters since first grade. However, it is questionable whether handwriting abilities would truly become automatic in children in higher grades while writing in the Chinese language because children continue to learn new characters that have increased visual complexity throughout the elementary school years. In general, approximately 2,500 Chinese characters are taught in elementary school. Characters introduced in the first and second grades typically are more commonly used words and contain fewer strokes, whereas characters learned in the fifth and sixth grades tend to be more visually and morphologically complex and contain more strokes (Ku & Anderson, 2003; Shu, Chen, Anderson, Wu, & Xuan, 2003). Thus, future research could investigate writing performance across age groups using Chinese characters presented with varying degrees of complexity and familiarity.
Second, resemblance was the only indicator used for writing legibility in this study, and the analysis was focused on individual characters. Therefore, other parameters that represent handwriting production, such as character size and alignment, writing accuracy, and spatial arrangement in paragraph form (Chang et al., 2009; Li-Tsang et al., 2013), in addition to environmental factors related to the writing process, such as fatigue, should be considered in future studies.
Third, convenience sampling was used in this study, and the sample size of the groups was uneven and relatively small. Therefore, the inferences drawn from the results should be viewed with caution. Longitudinal studies with larger samples recruited from different regions of Taiwan would provide more representation in handwriting performance of the target population.
Last, the outcome measures of writing production and writing process were collected separately in this study. A new device that allows all outcome measures to be collected simultaneously will provide a more ideal condition to improve internal validity.
Conclusion
This study demonstrates that predictors of handwriting legibility vary across age groups. Although perceptual motor–related factors, such as visual–motor integration, eye–hand coordination, and stroke force, demonstrate significant associations in the handwriting performance of younger children (in first and second grades), kinematic factors contribute more to the handwriting performance of children in higher grades (fifth and sixth grades). Future studies should use age-sensitive stimuli of Chinese characters and include more parameters for handwriting performance to make further inferences regarding age-related characteristics.
Acknowledgments
This article is based on the master’s thesis of Tzu-I Lee, completed in the School of Occupational Therapy, College of Medicine, The National Taiwan University in Taipei. This project was supported in part by the following grants from the Ministry of Science and Technology of Taiwan: NSC-101-2314-B-002-001 and 103-2314-B-002-008-MY3 to Tien-Ni Wang and NSC-100-2221-E-002-079-MY3 to Hao-Lin Chen.
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Figure 1.
Template matching involves (A) matching a handwritten character (right) to a standard character (left) and then (B) superimposing the template over the handwritten character and computing similarities between the two.
Figure 1.
Template matching involves (A) matching a handwritten character (right) to a standard character (left) and then (B) superimposing the template over the handwritten character and computing similarities between the two.
×
Table 1.
Participant Demographics and Group Outcome Measures
Participant Demographics and Group Outcome Measures×
M (SD) or n
VariableFirst Grade (n = 28)Second Grade (n = 23)Third and Fourth Grades (n = 35)Fifth and Sixth Grades (n = 32)F (3, 114)pPost Hoc Analysis (LSD)a
Characteristic
Male1471514
Female14162018
Age, mo83.79 (3.74)94.65 (3.98)114.91 (6.19)137.35 (7.86)
Measure
CLA (legibility)46.30 (2.17)46.74 (3.03)47.94 (5.25)47.50 (4.08)1.06.369
VMI (visual–motor integration)17.75 (2.22)19.43 (1.67)19.57 (3.01)23.72 (2.19)34.47<.015th & 6th > 3rd & 4th = 2nd > 1st
VP (visual perception)21.11 (2.73)22.78 (2.26)22.93 (2.22)25.16 (1.63)16.87<.015th & 6th > 3rd & 4th = 2nd > 1st
MC (eye–hand coordination)19.04 (3.45)20.52 (4.02)21.66 (3.43)24.56 (2.80)13.98<.015th & 6th > 3rd & 4th > 1st & 5th & 6th > 2nd
Kinetic/kinematic analysis
Task completion time, s164.36 (37.59)89.98 (17.74)77.61 (21.41)56.85 (20.06)96.37<.015th & 6th < 3rd & 4th = 2nd < 1st
Mean stroke velocity, pixels/s65.76 (19.67)101.96 (32.67)104.38 (36.33)154.41 (49.13)28.446<.015th & 6th > 3rd & 4th = 2nd > 1st
Mean stroke force (max = 1024)656.49 (153.68)686.79 (169.35)713.68 (138.08)640.04 (160.29)1.42.240
Pause time per stroke, s0.38 (0.13)0.16 (0.05)0.16 (0.10)0.08 (0.06)55.31<.015th & 6th < 3rd & 4th = 2nd < 1st
Table Footer NoteNote. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Noteap < .05.
p < .05.×
Table 1.
Participant Demographics and Group Outcome Measures
Participant Demographics and Group Outcome Measures×
M (SD) or n
VariableFirst Grade (n = 28)Second Grade (n = 23)Third and Fourth Grades (n = 35)Fifth and Sixth Grades (n = 32)F (3, 114)pPost Hoc Analysis (LSD)a
Characteristic
Male1471514
Female14162018
Age, mo83.79 (3.74)94.65 (3.98)114.91 (6.19)137.35 (7.86)
Measure
CLA (legibility)46.30 (2.17)46.74 (3.03)47.94 (5.25)47.50 (4.08)1.06.369
VMI (visual–motor integration)17.75 (2.22)19.43 (1.67)19.57 (3.01)23.72 (2.19)34.47<.015th & 6th > 3rd & 4th = 2nd > 1st
VP (visual perception)21.11 (2.73)22.78 (2.26)22.93 (2.22)25.16 (1.63)16.87<.015th & 6th > 3rd & 4th = 2nd > 1st
MC (eye–hand coordination)19.04 (3.45)20.52 (4.02)21.66 (3.43)24.56 (2.80)13.98<.015th & 6th > 3rd & 4th > 1st & 5th & 6th > 2nd
Kinetic/kinematic analysis
Task completion time, s164.36 (37.59)89.98 (17.74)77.61 (21.41)56.85 (20.06)96.37<.015th & 6th < 3rd & 4th = 2nd < 1st
Mean stroke velocity, pixels/s65.76 (19.67)101.96 (32.67)104.38 (36.33)154.41 (49.13)28.446<.015th & 6th > 3rd & 4th = 2nd > 1st
Mean stroke force (max = 1024)656.49 (153.68)686.79 (169.35)713.68 (138.08)640.04 (160.29)1.42.240
Pause time per stroke, s0.38 (0.13)0.16 (0.05)0.16 (0.10)0.08 (0.06)55.31<.015th & 6th < 3rd & 4th = 2nd < 1st
Table Footer NoteNote. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. CLA = Computerized Legibility Assessment; LSD = least significant difference; MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Noteap < .05.
p < .05.×
×
Table 2.
Correlations Between Legibility and Outcome Measures
Correlations Between Legibility and Outcome Measures×
MeasureFirst GradeSecond GradeThird and Fourth GradesFifth and Sixth Grades
VMI.38*.01.13.33
VP.35.19.04−.04
MC.32.48*−.12.22
Kinetic/kinematic analysis
 Task completion time−.72.19.04.56**
Mean stroke velocity−.24−.34−.13−.68**
Mean stroke force.17.49*.04.31
Pause time per stroke.02.32.08.57**
Table Footer NoteNote. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
Table 2.
Correlations Between Legibility and Outcome Measures
Correlations Between Legibility and Outcome Measures×
MeasureFirst GradeSecond GradeThird and Fourth GradesFifth and Sixth Grades
VMI.38*.01.13.33
VP.35.19.04−.04
MC.32.48*−.12.22
Kinetic/kinematic analysis
 Task completion time−.72.19.04.56**
Mean stroke velocity−.24−.34−.13−.68**
Mean stroke force.17.49*.04.31
Pause time per stroke.02.32.08.57**
Table Footer NoteNote. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. MC = Supplemental Developmental Test of Motor Coordination; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
×
Table 3.
Significant Predictors of Legibility for Each Group
Significant Predictors of Legibility for Each Group×
GroupSignificant Predictor
Intercept BVMIVPMCTimeSVForcePTSFdfsR2
First grade39.724**.370*4.3531, 26.143*
Second grade35.508**.229*.007*6.4622, 20.393*
Third and fourth grades
Fifth and sixth grades56.231**−.057**24.0951, 30.463*
Table Footer NoteNote. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
Table 3.
Significant Predictors of Legibility for Each Group
Significant Predictors of Legibility for Each Group×
GroupSignificant Predictor
Intercept BVMIVPMCTimeSVForcePTSFdfsR2
First grade39.724**.370*4.3531, 26.143*
Second grade35.508**.229*.007*6.4622, 20.393*
Third and fourth grades
Fifth and sixth grades56.231**−.057**24.0951, 30.463*
Table Footer NoteNote. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.
Note. — = not a significant predictor or not applicable. dfs = degrees of freedom; Force = mean stroke force; MC = Supplemental Developmental Test of Motor Coordination; PTS = pause time per stroke; SV = mean stroke velocity; Time = task completion time; VMI = Beery–Buktenica Developmental Test of Visual–Motor Integration; VP = Supplemental Developmental Test of Visual Perception.×
Table Footer Note*p < .05.
p < .05.×
Table Footer Note**p < .01.
p < .01.×
×