The Importance of Handwriting
Speed in Adult Writing
Stephen T. Peverly
Handwriting speed is important to the quantity and quality of children’s essays. This
article reviews research on adult essay writing and lecture note taking that extends
this finding to adult writers. For both children and adults, research suggests that
greater transcription speed increases automaticity of word production, which in turn
lessens the burden on working memory (WM) and enables writers to use the limited
capacity of WM for the metacognitive processes needed to create good
reader-friendly prose. These findings suggest that models of writing, which empha-
size the metacognitive components of writing primarily, should be expanded to in-
clude transcription (handwriting automaticity and spelling). The article also evalu-
ates the implications of fluent handwriting to WM, given that even the most fluent
handwriting can consume some WM resources and recent research and theory has
highlighted the importance of WM to quality writing. Finally, the implications of
handwriting and WM to assessment and instruction are discussed.
The goal of this article is to argue that transcription (handwriting) speed is impor-
tant to the quality of adults’ writing. Toward this end, I first review changes in mod-
els of writing over the last 15 to 20 years, from an almost exclusive reliance on
metacognition as the variable that differentiates between expert–novice or chil-
dren–adult writers to a model of writing competence that stresses fluency in “basic
skills,” such as transcription speed, as well as metacognitive skill. Then I review
the literature on the relationship of handwriting speed to writing quality and quan-
tity for children (elementary and middle school age) and adults. Next, I examine
the relationship of handwriting speed to working memory (WM), both of which
contribute to skilled writing. Four models of WM are considered, each of which
has a different explanation for these relationships. Finally, I present an illustrative
DEVELOPMENTAL NEUROPSYCHOLOGY, 29(1), 197–216
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case study and discuss the implications of each of the four models of WM for as-
sessing and teaching handwriting speed.
MODELS OF WRITING COMPETENCE
Until recently, there have been two perspectives on the nature of the cognitive pro-
cesses that underlie the development of writing (Bereiter & Scardamalia, 1982,
1987; Flower & Hayes, 1980; Hayes, 1996). The first perspective was introduced
by Flower and Hayes and portrays skilled writing in adults as primarily a
metacognitive act. A cognitive “monitor” controls the iterative interactions of
three metacognitive processes: planning (goal setting, generating and organizing
content), translating (turning ideas into text), and revising (changes made in text
produced so far). The writer continually evaluates the relationship between the au-
thor’s message (the author’s “semantic intention” as it is represented in the text)
and the goal of written communication, that is, how well the author has used writ-
ten language conventions to guide the reader to the writer’s semantic intent
(Perfetti & McCutchen, 1982). The successful writer is one who has used the
metacognitive processes of planning, translating, and revising to create
“reader-based prose”—prose that clearly specifies the author’s intended meaning.
In contrast, young and inexperienced writers create “writer-based prose.” Because
of their lack of expertise in the application of metacognitive processes, their writ-
ing is typically somewhat idiosyncratic, with unclearly specified referents and lack
Bereiter and Scardamalia, in contrast to Flower and Hays, proposed a develop-
mental model of writing. Young and/or inexperienced writers engage in “knowledge
telling,” that is, they search their memory for information based on the cues in text
and/or what they know about the topic (e.g., baseball, summer vacations) or genre
(e.g., narratives have characters, settings, episodes, etc.) and write down what they
have retrieved. They make few if any attempts to evaluate or revise what they have
written to ensure that they have adequately communicated their semantic intent for
the reader. Experts, on the other hand, use “knowledge-transforming” strategies to
create text that is readily understandable by the reader.Thus, both models character-
ize expertise in writing largely in reference to the application of metacognitive pro-
cesses such as planning, translating, and revising (McCutchen, 1995).
There is substantial evidence to support the importance of metacognition to the
development of writing. For children, the processes of planning and revising de-
velop slowly over time until they are mastered later in writing development
(Bereiter & Scardamalia, 1987; Brown, Bransford, Ferrara, & Campione, 1983;
McCutchen, 1995; 1996). Research shows that: (a) young children do little explicit
planning or revision (Alexander, Graham, & Harris, 1998; Bereiter &
Scardamalia, 1987; Graham & Harris, 2003; McCutchen, 1995, 1996) and (b) the
amount, quality, and skill in the strategic application of planning and revising in-
creases with age and experience, although the developmental progression of online
planning and preplanning and online revising and posttranslating revising are dif-
ferent (Alexander et al., 1998; Beal, 1990; Beal, Garrod, & Bonitatibus, 1990;
Berninger & Swanson, 1994; Burtis, Bereiter, Scardamalia, & Tetroe, 1983;
Butterfield, Hacker, & Albertson, 1996; Butterfield, Hacker & Plumb, 1994;
Flower & Hayes, 1980; Levy & Ransdell, 1995; McCutchen, 1995, 1996;
McCutchen, Kerr, & Francis, 1994, as cited in McCutchen, 1995).
Before leaving this section, I should point out that I am not purposefully ignor-
ing the role of WM in writing. Although none of the earlier models of writing
(Bereiter & Scardamalia, 1987; Hayes & Flower, 1980) paid much explicit atten-
tion to WM, a volume of edited empirical studies at the University of Washington
(Carlson & Butterfield, 1994) reported a number of studies documenting the im-
portance of working memory in children’s and adults’writing. This collection and
Hayes’s (1996) revised model of skilled writing suggest that WM plays a substan-
tial role in the writing process. Briefly, Hayes, McCutchen (1996), and Berninger
and colleagues (Berninger, Cartwright, Yates, Swanson, & Abbott, 1994;
Berninger & Swanson, 1994) argued that individual variations in WM resources
are related to individual differences in writing skill. Those with more WM re-
sources are better able to apply their metacognitive skills to writing than those with
fewer resources. Later in this article, the relationship of transcription to working
memory is considered.
Expanding the Metacognitive Models
Unlike Flower and Hayes and Bereiter and Scardamalia, who emphasized the
metacognitive processes in writing, others view those processes as necessary but
not sufficient for the development of writing expertise (Berninger et al., 1994;
Berninger et al., 1992; McCutchen, 2000; Ransdell & Levy, 1996). According to
this expanded view, execution of higher level metacognitive processes is based in
the “efficient” or “fluent” execution of lower level processes, processes not ade-
quately considered in earlier models. Writers must (a) be fluent in generating ideas
that can be written down and (b) write these ideas down quickly before they are
forgotten. If writers are efficient in executing (a) and (b), they will be able to use
the metacognitive processes discussed earlier and other cognitive resources (e.g.,
genre and content knowledge; McCutchen, 2000) to create reader-based prose. To
incorporate these ideas, Berninger and Swanson (1994) suggested that the transla-
tion component of both the Flower and Hayes and Bereiter and Scardamalia mod-
els be changed to include two processes: transcription and text generation. Tran-
scription draws on processes involved in retrieving letter forms and familiar word
HANDWRITING SPEED 199
spellings from long-term memory, strategically spelling novel words, and motor
planning to produce the letters by hand. Text generation involves translating gener-
ated ideas into language in WM and then translating those temporary mental repre-
sentations into more permanent external representations using the symbols of the
writing system (Berninger et al., 1992).
RELATION OF HANDWRITING SPEED
TO CHILDREN’S WRITING
Given the focus of this article, I review the research on only one of the basic skills
discussed by Berninger, McCutchen, and their colleagues—transcription speed.
For elementary and middle school students, (a) motor processes are more capacity
(time) consuming than for adults (Bourdin & Fayol, 1994; Olive & Kellogg, 2002;
see Dixon, Kurzman, & Friesen, 1993); however, for data that shows that transcrip-
tion speed slows in old age, (b) writers’ speed of transcription is related to the
length and quality of their written compositions (Berninger, Whitaker, Feng,
Swanson, & Abbott, 1996; Graham, Berninger, Abbott, Abbott, & Whitaker, 1997;
Jones & Christensen, 1999), (c) students’ skill in spelling is uniquely related to
compositional fluency (amount written within time limits) for younger elementary
grade students (Graham et al., 1997), (d) the capacity of WM is related to composi-
tion skill (Kellogg, 2001, 2004; McCutchen, 1996; McCutchen, Covill, Hoyne, &
Mildes, 1994; Ransdell, Levy, & Kellogg, 2002), suggesting that lower level trans-
lation skills must be fluent for students to optimally use their WM capacity, (e) in-
struction in handwriting legibility and automaticity for those who are at risk for
writing problems transfers to improved compositional fluency (the amount that is
written; Berninger et al., 1997; Graham, Harris, & Fink, 2000; Jones &
Christensen, 1999) and quality (Jones & Christensen, 1999), and (f) instruction in
spelling transfers to improved compositional fluency (Berninger et al., 2002;
Berninger et al., 1998; Graham, Harris, & Chorzempa, 2002). Although Berninger
and Swanson (1994) proposed that the association between handwriting speed and
composition length and quality is stronger for younger writers (Grades 1–3) than
older writers (Grades 4–9), subsequent research indicates that this association may
be very robust throughout the elementary and middle school years (Jones, 2004)
and even the high school and college years (see Connelly et al., 2005, this issue).
RELATION OF HANDWRITING SPEED
TO ADULTS’ WRITING
There is no reason to think that the principles that underlie competent writing in
children are different than those that underlie competent writing among adults. For
both, the capacity of WM is limited, writing is very cognitively effortful and can
easily strain WM, and an inefficiency in one component can lead to inefficiencies
in others (Bourdin & Fayol, 1994; McCutchen, 1995). However, there is much less
data on the relation of handwriting speed to the quality/quantity of writing of
adults (college students) than children. The research that exists is divided between
two types of writing tasks: quantity/quality of essays (as in the children’s litera-
ture) and lecture note taking.
Some adult studies have investigated experimental manipulations of handwriting
speed on essay quality. Brown, McDonald, Brown, and Carr (1988), for example,
divided writing tasks into three iterative stages: generation of ideas/translation to
words, motor execution, and monitoring of output. Their basic strategy was to ma-
nipulate the ease or difficulty of executing each of these and to observe their effects
on the other components. In the first of two experiments, Brown et al. manipulated
the ease of copying a short paragraph of approximately 95 words (copying only,
copying while performing an easy concurrent listening task, copying while per-
forming a difficult concurrent listening task) and the speed/accuracy of transcrip-
tion (normal style, stress for speed, stress for accuracy). In the concurrent listening
tasks, participants heard a series of isolated words. At semirandom intervals they
heard a signal that cued them to respond with a word in WM. Participants were to
respond with either the word that immediately preceded the signal (easy concur-
rent listening task) or the next to the last word (hard concurrent task).
The results of interest were a main effect for task concurrency on writing speed:
Single-task participants wrote faster than the easy concurrent task group, who
wrote faster than the hard concurrent task group. As attention load increased, writ-
ing speed decreased. There was also a main effect of task concurrency on recall
from WM: The easy concurrent task group had an error rate of about 4%, whereas
the hard concurrent task group had an error rate of 24%. The authors extended
these results in Experiment 2 by demonstrating that writing speed increased as the
“availability” of information needed for writing increased. In the high available
condition, participants copied text; in the low available condition, participants
wrote text they had memorized. Thus, transcription speed and the processes in-
volved in monitoring recall (an executive function) compete for the resources of a
Olive and Kellogg (2002) evaluated children’s and college students’ ability to
coordinate higher level (e.g., planning, reviewing) and lower level (transcription
speed) writing processes. Both groups were asked to write (transcription + compo-
sition) and then copy (transcription only) an essay on a topic provided by the ex-
perimenters. Half the undergraduates were asked to write and copy their essay us-
ing their usual handwriting, and the others were asked to use an uppercase cursive
HANDWRITING SPEED 201
script to write and copy. All participants performed the writing and copying tasks
using a digitized tablet with an electronic pen with ink. In both the writing and
copying tasks, participants were asked to respond to randomized auditory probes
as quickly as possible by using their nondominant hand to press the space bar of a
keyboard. When the probes occurred, the experimenters measured the pauses in
handwriting. A pause greater than 250 msec was assumed to reflect planning,
translating, or reviewing. A pause under that criterion was assumed to reflect tran-
scription. The results indicated that writing fluency (words per minute) for the
adults using standard script was greater than for adults using uppercase script or
children, who did not differ significantly. Also there was greater interference in the
transcription + composition condition than in the transcription-only condition for
adults using their typical handwriting than for adults using uppercase cursive or for
children. Thus, when asked to use a nonautomatic transcription system, children
and adults showed less concurrent activation of high- and low-level processes.
These results imply that adults with naturally slow handwriting may be hampered
in their ability to execute higher level processes because of the strain placed on
WM by nonautomatic transcription.
Connelly and colleagues have begun a line of research on the relationship be-
tween handwriting speed and quality of essay writing in adults. Connelly,
Dockrell, and Barnett (2005) evaluated the relation of handwriting speed to the
quality of undergraduate students’ essays under two conditions—unpressurized
and pressurized. In both conditions, all the students in a 2nd-year psychology class
(N= 22) had 1 hr to write an essay. In the former condition, all of the students wrote
a practice essay in preparation for a final exam. In the latter, the students wrote an
essay as part of an end of semester examination. It was hypothesized that the pres-
sure of a real examination would increase students’cognitive load and thus create a
stronger relationship between handwriting speed and exam performance. Hand-
writing speed was measured by a modification of the alphabet task, a measure of
handwriting automaticity (Berninger, Mizokawa, & Bragg, 1991). In this modifi-
cation, students are told to write the alphabet in lowercase letters as many times as
they could in 1 min. Students’ essays were scored in three different ways: scores
given to the essays by course tutors, number of words written (for the entire essay
as well as for the introduction, main body, and conclusion), and rubric assessment
scores (the rubric “assessed students’ skill at sectioning the essay clearly, ordering
ideas, linking ideas, [and] showing sufficient support and expansion of ideas and
showing a sufficient sense of audience”; SOURCE, p. 9). Results indicated that
there were no significant correlations between the handwriting automaticity mea-
sure and any of the essay scores in the unpressurized condition. In the pressurized
condition, however, handwriting speed correlated positively and significantly with
tutors’ marks, overall number of words written, and the overall rubric score. These
data suggest that handwriting speed is related to writing quantity and quality in sit-
uations where there is a substantial degree of cognitive load.
In summary, adults’handwriting speed is more automatic than children’s hand-
writing speed; handwriting competes for WM resources in adults and children, but
more so in children; and individual differences in adults’handwriting speed are re-
lated to the length and quality of essays, just as they are in children.
Lecture Note Taking
Other than writing essays, another writing task that undergraduates engage in a
great deal is taking notes in lecture. Almost all college students take lecture notes
(approximately 98%; see Brobst, 1996; Palmatier & Bennett, 1974). Research has
shown that recording (encoding) and reviewing notes from classes is related to
good test performance (e.g., Armbruster, 2000; Bretzing & Kulhavy, 1981;
Bretzing, Kulhavy, & Caterino, 1987; Fisher & Harris, 1973; Kiewra, 1985;
Kiewra et al., 1991; Kiewra & Fletcher, 1984; Rickards & Friedman, 1978;
Titsworth & Kiewra, 2004). Nonetheless, little research has addressed the cogni-
tive skills that underlie expertise in taking lecture notes.
Our analysis of the cognitive skills that underlie lecture notetaking (Peverly,
Ramaswamy, Brown, Sumowski & Alidoost, in preparation) is based on the as-
sumption that taking lecture notes places a tremendous strain on individuals’ lim-
ited information-processing ability (Anderson, 1995; Baddeley, 2001). Spe-
cifically, notetakers must (a) hold lecture information in verbal WM (the capacity
of WM is limited, and the duration of time that information can be held in WM is
short), (b) analyze the contents of WM to select or construct important thematic
units quickly and efficiently before the information in WM is forgotten, and (c)
transcribe the selected information quickly on paper (slow and inefficient tran-
scription may cause information held in WM to be forgotten or detract from the
cognitive resources needed to analyze incoming information). All these activities
must take place more or less in synchrony if students are to maintain the continuity
of the lecture. Although some have found a relation between WM ability and note
taking (see Kiewra, 1989, for a review), we hypothesized that handwriting speed
would also affect quality of notes, which would in turn be related to test perfor-
In an initial test of our assumptions (Peverly et al., in preparation; Ramaswamy,
Brown, & Peverly, 2003), we asked college undergraduates (N= 83) to listen to a
20-min lecture on the psychology of problem solving. We told them to take notes
and that they would later have the opportunity to review their notes in preparation
for a recall test. We also administered measures of transcription speed, WM, spell-
ing, and an independent measure of students’ skill in identifying important infor-
mation. We used three tasks to measure speed of transcription. The first was the al-
phabet copying task (Berninger et al., 1992). For elementary and middle school
students, performance on this task correlated well with writing quality (e.g.,
Berninger et al., 1994; Berninger & Swanson, 1994; Berninger et al., 1996;
HANDWRITING SPEED 203
Berninger et al., 1992). We also administered a test of spelling (on the assumption
that if some students are poor spellers and take time to spell words correctly, it will
have a negative impact on maintaining and processing information in WM), and
the Writing Fluency subtest of the Woodcock–Johnson III (Woodcock, McGrew,
& Mather, 2001), a test of composing speed. Students are asked to write as many
semantically and grammatically coherent sentences as they can in 7 min; each sen-
tence must contain the three words given to the student in the test booklet.
Our measure of verbal WM was the listening span task used by Daneman and
Carpenter (1983), which requires students to hold and process information simul-
taneously. Students are required to listen to sets of sentences, which can range in
size from two to six. After students hear each sentence in a set, they have 2 sec to
determine whether the sentence makes sense (some do and some do not); after all
of the sentences in the set have been presented, they must write down the last word
of each sentence that they heard. We used this task because it measures what stu-
dents are required to do when they listen to a lecture— hold information in WM
and process it semantically. We also obtained an independent measure of students’
ability to identify important information. Participants read a four-page history pas-
sage, which consisted of 10 macropropositions (see Peverly, Brobst, Graham, &
Shaw, 2003, for information on the structure of the text and the reliability of the
identification of the macropropositions). They answered 20 forced-choice items,
without looking back at the text, which required deciding whether a statement
from text was an important part of the overall theme of the text. Identifying impor-
tant information is a critical part of note taking, and research indicates that the cog-
nitive processes that underlie the ability to identify important information while
reading or listening are the same (Kintsch, 1998).
In our analyses of the data, we first evaluated which of the aforementioned vari-
ables, plus our ratings of the quality of students notes, was related to test perfor-
mance. Only quality of notes was related to test performance. This finding con-
firms previous research (Bretzing & Kulhavy, 1981; Fisher & Harris, 1973;
Kiewra, 1985; Kiewra et al., 1991; Kiewra & Fletcher, 1984; Rickards & Fried-
man, 1978; Titsworth & Kiewra, 2004). Next we evaluated which of the other vari-
ables was related to quality of notes. Given the intercorrelations among the vari-
ables and our limited sample size, we tested two simple models using multiple
regression: (a) letter automaticity (alphabet task) + verbal WM, and (b) composing
fluency (writing fluency subtest) + verbal WM. For the first model, only the alpha-
bet task uniquely predicted quality of notes. For the second model, both variables
uniquely predicted quality of notes. These results suggest that speed of transcrip-
tion (as measured either with the alphabet task or with writing fluency) is related to
quality of notes taking while listening to a lecture.
We attempted to replicate and extend our results with a larger sample of stu-
dents (N= 151). First, we added another measure of fluency, the Symbol Coding
subtest of the Weschler Adult Intelligence Test–Third Edition (WAIS–III;
Wechsler, 1997), which is a speeded test that requires individuals to copy
nonlinguistic shapes (e.g., ⊥) as fast as they can. We added this measure be-
cause the alphabet task was significantly correlated with WM (r= .44, p= .000).
We hypothesized that both are verbally loaded, that is, phonological codes may
be accessed automatically in both. Thus, we added a measure of transcription
speed that did not seem to have a phonological component. We also added a
measure of verbal fluency as a measure of the speed of semantic access (partici-
pants were given tasks loosely modeled on those in the NEPSY [Korkman, Kirk,
& Kemp, 1998]—the letters Fand Sand two semantic categories: animals and
foods; they had 1 min to write as many words as they could think of for each let-
ter or word), because McCutchen, Covill, Hoyne, and Mildes (1994) found that
better writers have faster and more accurate access to words in their mental lexi-
cons and thus may generate more ideas than poorer writers. Thus, in the second
experiment we evaluated whether transcription speed, verbal fluency, and verbal
WM were related to quality of notes and again whether quality of notes would
be related to test performance. Our results replicated the findings of the first ex-
periment. Quality of notes was the only significant predictor of test perfor-
mance; and handwriting speed, as measured by the alphabet task, was the only
significant predictor of quality of notes. Neither verbal fluency nor WM contrib-
uted a significant amount of variance above that contributed by transcription
speed. However, there was a ceiling effect with the Symbol-Coding subtest of
the WAIS–III; thus, there was not enough variance to test whether (a) a task that
measures fine motor speed for symbols that are not verbally coded would signif-
icantly predict quality of notes, and (b) verbal WM would then become a signifi-
cant predictor of quality of notes.
In summary, for both children and adults, faster handwriting speed is related to
higher quality essay writing; for adults handwriting speed is also related to higher
quality lecture notes. If we combine the data that support the metacognitive models
presented earlier with the data that support the importance of the modified transla-
tion component proposed by Berninger and McCutcheon, especially transcription
speed, the overall “interactive” model of writing suggests that components of the
model must work in synchrony, within a limited capacity WM, if writers are to
achieve their desired outcomes—reader-based prose and/or high-quality notes.
There is one last issue to consider. Given that writing is a very effortful cogni-
tive activity that can easily strain a limited-capacity processing system, how much
of an impact can handwriting have on the execution of the other cognitive re-
sources (e.g., planning and revising) needed to achieve high-quality writing out-
comes? The answer seems to depend on the theory of WM used to represent the
limited-capacity processing system. Some theories allow for more of an impact of
instruction and practice on expanding the WM system than others. In the next sec-
tion I review four different views of WM and their implications for the impact of
handwriting on writing skill.
HANDWRITING SPEED 205
All models of WM characterize it as (a) space where environmental information
interacts with information from long-term memory for the purposes of temporary
storage and processing for current goals, (b) a capacity-limited mechanism that is
capable of holding a small amount of information for brief periods of time unless
strategies are used to actively maintain the information in WM, and (c) a form of
memory associated with consciousness (as opposed to memory representations
that are implicitly represented rather than explicitly available in conscious aware-
ness). Despite these commonalities, there are important differences in explana-
tions of individual differences made by what I labeled as capacity-based, knowl-
edge-based, central executive, and combined models (see also Miyake, 2001;
Miyake & Shah, 1999). These are discussed in the following sections.
Just and Carpenter’s (1992) model of WM suggests that individual differences in
capacity are structural, and therefore some individuals have more WM space than
others (see also the work of Meredyth Daneman for a similar point of view; e.g.,
Daneman & Carpenter, 1983; Daneman & Hannon, 2001). Because there are
interindividual differences in capacity, there are interindividual differences in the
amount of material that can remain active in WM at any one time. Every element
(word, phrase, etc.) has an activation level. If the amount of activation in WM is
less than the individual’s capacity, storage and processing proceed relatively
smoothly, but all processes become more effortful as activation levels approach ca-
pacity levels. If the demands of the task(s) exceed capacity, some of the informa-
tion in WM will deactivate and forgetting will occur.
In this view, some of the variation in writing skill among individuals is due to
limitations in the amount of WM space available to activate processes needed to
achieve goals. It follows that if handwriting speed is fluent and requires less activa-
tion of low-level transcription processes, more capacity-limited resources can be
devoted to the activation of high-level writing processes. However, even if all the
processes that underlie writing are highly efficient, individual differences in the
structural capacity of WM will continue to explain some of the variance related to
differences among writers.
Ericsson and Kintsch (1995; see also Chase & Ericsson, 1982) hypothesized that
WM, as it is typically conceived in structural, capacity-based explanations, cannot
account for individuals’ ability to remember amounts of information far in excess
of their typical capacity, such as readers’ability to recall large amounts of informa-
tion from text. The reason is that many previous models of WM have not given
LTM a sufficiently prominent role in processing information in WM, and they have
not specified the mechanisms needed to account for the impact of LTM on WM.
To compensate for the aforementioned problems, Ericsson and Kintsch (1995)
hypothesized that all individuals have a limited-capacity short-term WM, which is
supplemented by a portion of their long-term memory that is not limited in capac-
ity. They refer to the latter as long-term working memory (LT-WM). For example,
when people read, propositions are encoded in WM. In turn, these propositions
contain cues, for example, linguistic devices like anaphora (e.g., he, she, there,
then, etc.) that are actively related to (i.e., act as “retrieval structures” for) informa-
tion in LTM. Representations in LT-WM may be based on (a) what the reader has
read already (this information is arranged hierarchically with the most important
ideas or macropropositions at the top of the hierarchy; higher order information is
retrieved more quickly than lower order information) and (b) other information
such as schemas and scripts that are relevant to the interpretation of the text. Thus,
according to this model, the capacity of WM is determined primarily by LTM re-
sources, including content knowledge, events schemas and scripts, and language
ability, rather than structural limitations in the amount of space in WM. Within this
perspective, limitations in writing skill attributed to WM are not structural but are
due to insufficient resources in LTM. Instruction is strongly related to the develop-
ment of these resources (Anderson, 1995).
Cowan’s (1999) view of WM, as represented in his Embedded Processes
Model, and the representation of WM in ACT–R (e.g., Lovett, Reder, & Lebiere,
2001), are similar to Ericsson’s and Kintsch’s LT-WM. However, rather than in-
voking a structurally separate WM, Cowan (1999) and Lovett, et al. (2001) argued
that WM is the activated portion of LTM. Thus, for all three models, individual dif-
ferences in WM capacity are related directly to the quantity and quality of the cog-
nitive representations in LTM.
Engle (2001, 2002; Engle, Kane, & Tuholski, 1999; Kane, Bleckley, Conway, &
Engle, 2001) argued that variations in capacity are not due to differences in the size
of memory stores (i.e., STM); the processes traditionally associated with STM
such as grouping, chunking, and rehearsal; or LTM expertise/resources (e.g., skill
in reading comprehension), but to differences in executive attention, that is “ability
to control attention [and avoid distraction] to maintain information in an active,
quickly retrievable state” (p. 20). For evidence, Engle and his colleagues reported
that the correlation between WM and reading comprehension remains even when
expertise in reading is statistically removed (Engle, Cantor, & Carullo, 1992), and
high-span participants are better able to ignore potentially distracting stimuli in
HANDWRITING SPEED 207
Stroop and dichotic-listening tasks than low-span participants (Conway, Cowan, &
Bunting, 2001; Kane & Engle, 2003).
In this view, individual differences in writing skill are due in part to writers’
ability to attend to the writing task. Klein (2001, 2002), for example, found that
disclosing emotions in expressive writing helps lessen their impact on students’
ability to store and process information in WM. Although there are many potential
reasons for this, one is that expressive writing helps students construct more orga-
nized representations of their emotions so that they are less scattered and intrusive.
This in turn helps students to inhibit unwanted thoughts and focus on the storage
and processing of information in WM needed to construct reader-based prose.
There are at least two models that consist of a central executive and specialized pe-
ripheral processors, or slave systems: Baddeley’s (1998, 2000, 2001; Baddeley &
Hitch, 1974; Baddeley & Logie, 1999) multicomponent model of WM and the
model of WM embedded in Schneider’s Connectionist Control Architecture
(Schneider, 1999). I focus on Baddeley’s model because it is the more developed
and well known of the two. Baddeley’s multicomponent model has four parts:
three “slave” systems and a central executive. The slave systems consist of a pho-
nological loop (a verbal WM), a visual-spatial sketch pad (a WM for visual-spatial
information), and an episodic store (a backup store that is capable of integrating in-
formation from the other stores and information from long-term memory; in some
ways its purpose and function is similar to Ericsson and Kintsch’s LT-WM). The
purpose of the central executive is to determine how the slave systems are used and
to control attention by maintaining information in an active, quickly retrievable
state (similar to Engle’s view of attention). Each component of the model has its
own storage capacity, with the exception of the central executive, and the phono-
logical loop and visual spatial sketch pad have their own rehearsal systems. In this
model individual differences in writing seem to be due to structural differences in
the capacity of each of the slave systems, how efficiently capacity of the slave sys-
tems is used and the efficiency of the central executive (Baddeley, personal com-
munication, December 9, 2004; Bayliss, Jarrold, Gunn, & Baddeley, 2003).
Research suggests, and all models of WM seem to imply, that the best way to en-
hance the efficiency of a limited-capacity processing system is through instruction
and practice, especially of basic skills, so that the capacity of WM can be devoted
to the higher order skills necessary to achieve academic goals (e.g., Anderson,
1995). Within the context of writing, research suggests that instruction focused on
making transcription speed as fluent as possible is especially important (Berninger
et al., 1997). As discussed previously, transcription fluency can substantially re-
duce the burdens placed on WM so that the capacity of WM can be used by the
conscious and effortful processes of planning, monitoring, and revising.
The question of the effect of instruction on capacity is a more difficult question
to answer. None of the WM models discussed previously have specifically ad-
dressed this issue. Ericsson and Kintsch’s (1995) model, however, seems to em-
phasize the importance of instruction and schools much more than other models,
as they suggest that (a) LTM plays a much larger role in the functioning of WM in
their theory than any other; (b) LTM is the repository for a tremendous variety of
cognitive resources, which schools help to develop (verbal ability, scripts,
schemes, domain knowledge, reading ability, etc.); and (c) many of the resources
in LTM are central to skilled writing (Hayes, 1996). In other words, Ericsson and
Kintsch’s model (and to a lesser extent Baddeley’s because of the episodic store)
seems to suggest that instruction can have an impact on the capacity of WM. Other
models seem to suggest that, past the point of the efficient execution of basic-level
writing processes, differences among learners in capacity are due to structural, en-
dogenous differences (e.g., capacity, attention) that may or may not be amenable to
instructional intervention (see Hooper et al., 1994, for a discussion of the impor-
tance of taking into account both exogenous and endogenous factors in explaining
writing disability). Indeed, if what has been found for children (e.g., Berninger et
al., 1997; Berninger et al., 1998; Chenault et al., this issue; Graham et al., 2000;
Graham et al., 2002) generalizes to adults, effective writing intervention may well
draw on all four WM models: training letter writing to automaticity to free up
available working memory capacity (all models), developing LTM resources for
correspondences between phonemes and letters and precise orthographic word
forms (second model), teaching explicit strategies for regulating attention during
writing (third model), and integrating the storage and executive processes with a
focus on quality and efficiency of representation (fourth model).
ASSESSMENT OF WRITING PROBLEMS
IN ADULTS: A CASE STUDY
The information in previous sections suggests that difficulties in writing can be re-
lated to any of a variety of variables. In this section, a case is presented to illustrate
the assessment of many (but not all) of these as a way of highlighting weaknesses
that can be the focus of instruction.
A woman in her late 20s (Ms. D) was referred by her psychiatrist to me to deter-
mine if she had a writing disability. She was a master’s student at a local university
and was having difficulty keeping up with the amount of work required in the pro-
gram. She had a history of fine motor, writing, and spelling problems but had not
received specialized instruction or accommodations. She reported that she had
HANDWRITING SPEED 209
taken few essay or short-answer tests (mostly multiple choice) but that when she
did she wrote compactly and succinctly, using relatively few words. She also
seemed to have excellent study skills, which she used to help overcome her diffi-
culties with writing. For example, because her writing problems precluded exten-
sive note taking, she focused on recording key words. She used the key words to
access relevant information from the text. If she encountered something she did not
know, she engaged in extensive research using outside sources. Once she had gath-
ered all of the information relevant to the key words, she constructed flash cards
containing the words and the information relevant to the them. She studied the
flash cards until she overlearned the information. For her, overlearning saved writ-
ing time; she reported that she did not have to plan and revise as much as she might
have had to otherwise. All in all, her ability to compensate for her writing difficul-
ties suggests that executive functioning was not a cause of her writing difficulties.
As measured by the Wechsler Adult Intelligence Scale (WAIS-R; Wechsler,
1981), Ms. D’s overall level of intellectual functioning was in the average range
(Full Scale Intelligence Quotient of 107, 68th percentile). She placed in the high
average range on the Verbal component of the WAIS–R (Verbal Intelligence Quo-
tient of 113, 81st percentile) and in the average range on the Performance compo-
nent (Performance Intelligence Quotient of 99, 47th percentile). Notably, she per-
formed very poorly on the Digit Symbol subtest (standard score of 3, 1st
percentile). Although she held the pencil with an appropriate grip in her right hand,
she held it very tightly, pressed very hard, and took a great deal of time to form
each symbol. She proceeded very slowly (but accurately) through the task. In con-
trast, Ms. D’s performance on Digit Span indicated that her verbal STM (digits for-
ward) and WM (digits backward) were in the average range of functioning. Thus,
her performance on the Digit Symbol subtest suggested that she might have prob-
lems with transcription speed but that her WM span per se was not impeding her
The writing subtests of the Woodcock–Johnson Psychoeducational Bat-
tery–Revised (Woodcock & Johnson, 1989) were used to assess Ms. D’s quality
and efficiency (speed) of composing and handwriting further. Overall, Ms. D’s
quality of expression, based on semantics, syntax, and discourse coherence, was in
the average range as measured by the Writing Samples subtest (standard score of
104). However, when completing this subtest, she proceeded very slowly and
pressed the pencil so hard to the paper that she broke at least a half-dozen pencil
points. Her slow rate of writing was compounded by the poor quality of her hand-
writing. As judged by two raters, the quality of her handwriting was below age ex-
pectations (standard score of 78, 7th percentile). Ms. D’s sentence-writing skills
were very also inefficient, as observed on the Writing Fluency subtest, which times
written production of grammatically acceptable sentences generated from three
given words. On this subtest, her performance was well below the average range
for someone her age (standard score of 70, 2nd percentile). Her spelling skills were
also below the average range (standard score of 74, 4th percentile), which may
have contributed to her problems with speed (if she was overly concerned with
spelling words correctly). Finally, her rate of writing may have been slow enough
to cause problems in WM while composing. At one point she remarked spontane-
ously that she was occasionally experiencing difficulty in remembering what she
wanted to write.
The writing cluster scores (cluster scores are combinations of subtests that are
more reliable than the individual subtests) confirmed that overall her basic writing
skills were significantly below average for a person her age: Broad Written Lan-
guage (standard score of 78, 7th percentile), Basic Writing Skills (standard score
of 76, 6th percentile), and Written Expression (standard score of 83, 13th percen-
Ms. D’s Digit Span, an index of STM-WM, fell in the average range, and her Ver-
bal IQ, fell in the high average range. Thus, these data suggest that neither
STM-WM capacity nor verbal knowledge were causes of her writing difficulty.
The most striking feature of her writing disability was the large and substantial
discrepancy between the quality (average range) and fluency (well below average)
of her composing. Given the documented poor quality and speed of Ms. D’s hand-
writing and the clear research evidence that handwriting speed contributes to
compositional fluency, it is likely that poorly developed transcription skills that are
nonautomatic for alphanumeric stimuli, contributed greatly to Ms. D’s writing dis-
ability. An evaluation of the neuropsychological processes shown in research to
constrain handwriting speed (e.g., orthographic coding, grapho-motor planning,
and executive function related to supervisory attention) would be the next phase in
an assessment of this sort (see Berninger, 2004; Berninger & O’Donnell, 2004).
SUMMARY AND IMPLICATIONS
FOR FUTURE RESEARCH
In summary, theory and data on the development of writing and the models of WM
suggest that skill in writing depends on performing a hierarchy of skills simulta-
neously (in parallel). In the execution of these skills, two conditions must hold.
First, translation skills, which include transcription speed and the generation of
ideas, must be executed with an acceptable degree of fluency so that most if not all
of the space in WM can be used for the application of the higher level cognitive
skills (e.g., metacognitive processing) needed to produce good reader-based prose.
If basic skills are not fluent the application of higher level cognitive skills can be
attenuated and prevent students from achieving their goal, even if students’ cogni-
HANDWRITING SPEED 211
tive (e.g., metacognitive) resources are substantial. Second, as implied in the previ-
ous sentence, individuals must have the cognitive resources (e.g., content knowl-
edge, verbal skills) necessary to enable them to attend, interpret, and process the
information in WM once basic skills become fluent.
There are three implications for research. First, to what extent can researchers
establish the relative contributions of inherent limitations in capacity (e.g., Just &
Carpenter, 1992) versus cognitive resources (Ericsson & Kintsch, 1995) as an ex-
planation for individual differences in writing skill? Second, to what extent can re-
search identify ways that instruction can reduce WM capacity limitations? Which
cognitive resources are most useful in reducing WM capacity or execu-
tive/attentional limitations? Finally, to what extent can instruction increase adults’
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