Benefits of Assistive Reading Software for Students with Attention
Disorders
Linda Hecker
Liza Burns
Landmark College
Putney, Vermont
Jerome Elkind
Lexia Institute and Kurzweil Educational Systems
Portola Valley, California
Kenneth Elkind
Kurzweil Educational Systems
Bedford, Massachusetts
Lynda Katz
Landmark College
Putney, Vermont
To Appear December 2002 in Volume 52
Annals of Dyslexia
Preprinted with Permission of the International Dyslexia Association
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
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Benefits of Assistive Reading Software for Students with Attention
Disorders
ABSTRACT
This study investigated how assistive reading software affected the reading performance of a
group of 20 post-secondary students who had a primary diagnosis of attention disorder. These
students used assistive reading software for most of a semester to read assignments for an
English class and in testing sessions in which comparisons were made between normal,
unassisted reading and reading assisted by the software. This software provides a synchronized
visual and auditory presentation of text and incorporates study skills tools for highlighting and
note taking. Attention measures, reading speed, comprehension scores, and attitude questionnaire
responses were obtained during these sessions. The principal findings were that the assistive
software allowed the students to attend better to their reading, to reduce their distractibility, to
read with less stress and fatigue, and to read for longer periods of time. It helped them to read
faster and thereby to complete reading assignments in less time. It did not have a significant effect
on comprehension, but it helped some students whose comprehension was very poor. The study
results indicate that assistive reading software should be considered as a significant intervention
to assist students who have attention disorders and as an accommodation to help them
compensate for their disabilities.
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INTRODUCTION
Over the last several years software that helps people with reading disabilities
compensate for their poor reading skills has become widely available. This software scans printed
documents, recognizes the characters on the page, speaks the text to the user through a
loudspeaker or earphones using a speech synthesizer, and simultaneously displays the printed
page on the computer monitor. As the computer speaks a word, it is highlighted on the computer
monitor, thereby providing a synchronized auditory and visual presentation of the text. The
phrase, sentence, or paragraph containing the spoken word is also highlighted, but in a different
color, to call attention to the context in which the word is used. The characteristics of the speech
synthesizer (male or female voice and pitch, for example), speed of speech, and magnification of
the text on the monitor is controlled by the user. The user can also decide to have the reading
pause after each phrase, sentence, or paragraph, which is often useful when difficult material is
being read. In addition, the software integrates electronic dictionaries and study skills tools that
facilitate active reading strategies such as: previewing section headings; highlighting main ideas,
supporting details, and other important segments of text in distinctive colors; taking notes by
typing, dictating, or copying; automatically creating study and writing outlines; and building
glossaries of important terms. This software also works with electronic documents from word
processors, web pages, and other sources. In this paper, we use the term assistive reading
software to refer to software with these capabilities; other terms used in the field include
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HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
reading machines and literacy software. Assistive reading software is available from several
companies.1
Students with reading disabilities who have good receptive oral language have found that
assistive reading software can enhance their reading speed and comprehension. Elkind (1998) and
Elkind, Black, & Murray (1996), working with post-secondary students, found that the changes
in reading rate and comprehension test scores observed when students used assistive reading
software were inversely related to the students unassisted performance: that is, students who
read slowest or with poorest comprehension benefited the most. Higgins & Raskind (1997)
obtained a similar result. In an earlier study, Elkind, Cohen, & Murray (1993) found an
enhancement of comprehension in a study of middle school students. Leong (1992) studied the
effects of text-to-speech systems on reading comprehension of elementary school students in a
task in which the students were given word knowledge training. His results were equivocal in that
the text-to-speech system improved comprehension of only a few of the passages that his
students read. Students with reading disabilities also have reported that reading was less tiring
and less stressful when they used assistive reading software and that they could double or triple
the time that they could sustain reading (Elkind, Black, & Murray, 1996).
This paper reports on an exploratory study of the effect of assistive reading software on
students who have a primary diagnosis of attention disorder2 rather than a reading disability.
This study was carried out at Landmark College in Putney, Vermont, a small (enrollment is 360
students), private college in a rural setting that offers a two-year Associates Degree in Liberal
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Studies. It is a fully accredited post-secondary institution exclusively serving students with
learning disabilities and attention disorders.
Our interest in students with attention disorders grew out of two observations: (1) In
their 1993 study Elkind, Cohen & Murray observed that several of their middle school students
who had difficulty maintaining attention to their reading were able to read for longer periods of
time when they used assistive reading software. They reported that the combination of the
computer display of the text and the auditory input seemed to allow these students to focus their
attention better and to block out distractions. (2) More recently, after Landmark College installed
assistive reading software to support a new curriculum that addresses the needs of students with
very poor decoding skills (Hecker, 2000), students with attention disorders reported that they
were able to read for longer periods of time when they used the software and that it improved
their reading. Although there is a large literature on accommodations and interventions for
students with attention disorders (see Katz, Goldstein, & Beers, 2001 and DuPaul & Eckert,
1998 for summaries), we were not able to find studies relevant to our interest in the use of
assistive reading software by students with this disability. As a result, we decided to undertake a
formal study at Landmark College to determine if the reported benefits could be substantiated
and quantified.
Attention disorders and reading disabilities are among the most common and widely
studied of problems that negatively affect educational outcomes. The prevalence of attention
disorders is estimated conservatively to be 3% to 5% as compared to conservative estimates of
5% to 10% for reading disabilities (Schulte, Conners, & Osborne, 1999). These two disorders
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tend to co-occur at levels greater than would be expected by chance; in particular, children who
have attention disorders show a greater tendency to have reading disability than do children
without attention problems, and the converse is also true (Schulte, et al.,1999; Shaywitz,
Fletcher, & Shaywitz, 1994). Estimates of the overlap range from 10% to 20% (Hinshaw, 1992)
to 20% to 40% (DuPaul & Stoner, 1994). Although it can be argued that attention disorders and
reading disabilities result from separate sets of cognitive deficits -- phonological processing for
reading disabilities and executive functioning deficits for attention disorders (Pennington, 1991) -attention disorders may make it more difficult for a child to learn to read, thereby causing the
child to exhibit poor reading performance (Fergusson & Horwood, 1992; Rabiner, & Coie, 2000).
Reading disabilities are typically diagnosed based on an individual s performance on a
variable battery of psycho-educational tests measuring academic potential and achievement and
performance in relevant underlying processes, such as phonemic awareness and phonological
processing (Lyon, 1996). Although federal and most state laws in the United States define
reading disabilities in terms of an achievement-ability discrepancy, the validity and usefulness of
this discrepancy has become controversial (Katz, Goldstein, & Beers, 2001; Lyon, 1996). A
more recent practice among researchers is to define reading disabilities in terms of reading
achievement on standardized tests, rather than as an achievement-ability discrepancy (Schulte et
al., 1999).
A diagnosis of attention disorder is made on the basis of meeting specific criteria in the
Diagnostic and Statistical Manual (DSM-IV) of the American Psychiatric Association (1994).
The DSM-IV criteria for a positive diagnosis requires that Six or more symptoms of inattention
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and/or hyperactivity/impulsivity have persisted for at least 6 months to a degree that is
maladaptive and inconsistent with developmental level. A psychiatrist, physician or
psychologist generally makes this diagnosis through structured interviews, self-reporting,
observations, and, in some instances, neuropsychological testing. Attention disorders not only
have negative impacts in social and behavioral areas, but also on educational outcomes. Indeed,
the DSM-IV criteria further stipulate the need to document clinically significant impairment in
social, academic (emphasis added), or occupational functioning.
Students with attention disorders experience a variety of difficulties that can interfere
with reading. They have difficulty sustaining attention, which leads to more off-task behavior
when compared to same-age peers, thereby minimizing the acquisition of academic information.
(Barry, DeShazo, Klinger, Lyman, Bush, & Hawkins, 2001, p.201). They may lose their place,
have trouble keeping what they read in short-term memory, and have to read the same paragraph
repeatedly to get any meaning out of it. As a result, reading becomes mentally fatiguing. (Robin,
1998, p. 284). Furthermore, their distractibility leads to a tendency to focus on unimportant
stimuli or non-salient detail, to be impulsive, to daydream, and to free associate. Superficial
attention to detail may obscure salient visual features of letters or words and lead to difficulty
with accurate word recognition. Cognitive impulsiveness may result in guessing entire words.
Distractibility may lead to difficulty with understanding sentences, because of the tendency to
tune in and out, missing important pieces of information. Passage recall is also affected when
reading is impulsive and unfocused. The student may read an entire chapter while thinking about
other things, and have no idea about its content (Levine, 1987).
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There is some dispute as to whether attention disorder leads to word decoding problems.
Wood and Felton (1994) found that attention disorder had no measurable impact on word
decoding; Fergusson and Horwood (1992) reported a contrary result. It is widely believed that
attention difficulties have their primary impact on comprehension (Schulte, et al., 1999).
However, children who have both attention disorder and reading disability are expected to exhibit
problems with phonological processing and accurate, fluent single word decoding that are typical
of individuals with reading disability (Lyon, 1996) in addition to the problems resulting from
their attention disorder.
From observations of and discussions with students with attention disorders at Landmark
College who had used the assistive reading software, we thought it likely that the software would
improve attention to reading in a number of ways. Because the software presents the text through
both visual and auditory channels, external and internal distractions are more likely to be masked
and their impact lessened. The movement of the color highlighting across the text from left to
right should help keep attention on the page, rather than wandering elsewhere. The fact that this
sequential visual presentation of text is reinforced by the sequential auditory presentation should
reduce the tendency to jump impulsively around in the text and to skipping over important
information. Reductions in the effort required to maintain attention should lower the stress and
fatigue associated with reading and thereby increase the time that students can sustain reading.
Reading speed should also improve since the need to re-read would be reduced. Finally,
comprehension should improve because less information is lost through inattention and because
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the multimodal presentation of information may increase the likelihood that the material is
assimilated into memory and recalled (Shany & Biemiller, 1995).
The objectives of our study emerged from these observations. We wanted to determine
how the use of assistive reading software would affect four aspects of the reading performance of
students with attention disorders: (1) would attention improve because the tendency to be
distracted, to wander, or to skip impulsively over portions of the text is reduced? (2) would
reading be less stressful, less tiring, and sustainable for longer durations? (3) would reading speed
increase and assignments be completed in less time? (4) would reading comprehension increase?
METHOD
PARTICIPANTS
The participants were students at Landmark College who had an attention disorder as their
primary diagnosis. Participants were recruited from the group of students taking a particular
course, English 102: Exposition and Analysis, which is required for graduation. Students
volunteered in response to personal visits by the researchers to their English classes early in the
semester and were offered a free copy of the assistive reading software if they completed the
study. Students self-reported their diagnosis, which we confirmed by examining their admissions
files. Landmark College requires a recent diagnostic evaluation as part of the admissions process.
We examined the students files for a primary diagnosis of attention disorder. All students also
had results from Wechsler Adult Intelligence Scale-III (WAIS-III) (Wechsler, 1997) and reading
scores from either the Gray Oral Reading Test-3 (GORT-3) (Wiederholt & Bryant, 1994) or
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from the Woodcock Johnson Psycho-Educational Battery-Revised (WJ-R) (Woodcock &
Johnson, 1989) in their files.
In this manner we recruited 20 students (12 male and 8 female) in two cohorts (eleven in
Fall 2000; nine in Spring 2001). All but two students were between 19 and 25 years old; the
others were 51 and 57. Median age was 23. Nineteen students were European-American; one was
African-American. Three students were educated outside of the United States (Venezuela,
Germany, Canada) before attending college, but English was their primary language. Socioeconomic data were not available, but since the cost of attending Landmark is comparable to that
of prominent national private universities, it is likely that 65% to 70% of the students were from
families in the upper two quartiles of socio-economic status. (Owings, Madigan, & Daniel,
1998).l. WAIS-III Verbal scores ranged from 80 to 139 (mean 109), Performance scores ranged
from 84 to 134 (mean 103), and Full Scale scores ranged from 84 to 139 (mean 107). Thus the
scores of all students but one were in the Average to Superior ranges. The Verbal (80) and Full
Scale (84) scores of this participant were in the Low Average range, while his Performance score
(92) was Average.
All of the students had a formal diagnosis of attention disorder in their records. These
diagnoses were made in accordance with the DSM-IV criteria (American Psychiatric Association,
1994) by the students own psychiatrists, physicians, and psychologists who used a variety of
methods, as discussed in the Introduction. Thirteen of the students were prescribed medications
(Ritalin, Adderal, Wellbutrin, or Dexedrine) for management of their attention disorder and
reported that they took their medications regularly. Medication use was noted at each session to
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confirm that students were taking their medication on the day of the testing. Three students also
had a formal diagnosis of reading disability. The reading scores of two other participants also
suggested a combination of attention disorder and reading disability. Although there was no
formal diagnosis of reading disability in their files, their GORT-3 Accuracy grade equivalent
scores (4.1 and 6.5) were lower than one standard deviation below the mean score for high school
seniors. These two students may not have been diagnosed with reading disability because they
were educated and evaluated outside the United States where different standards and protocols
for educational testing often exist. We classified all five of these students as having reading
disability, giving us a co-occurrence rate of attention and reading disorders of 25% for our sample,
well within the co-occurrence rates of 10% to 40% reported in the literature (Hinshaw 1992;
DuPaul & Stoner, 1994).
The other 15 participants had GORT-3 accuracy scores or WJ-R Word Identification
standard scores higher than one Standard Deviation below the mean for their age group. The
average GORT-3 Accuracy score of the entire group was a 9.9 grade equivalent, and the average
WJ-R Word Identification standard score was 111. In summary, all 20 of the students had a
diagnosis of attention disorder, and five of the students additionally had either a formal diagnosis
of reading disability or had standardized reading test scores consistent with that diagnosis.
ASSISTIVE READING SOFTWARE
The Kurzweil 3000 assistive reading software (Kurzweil, 2002) was used in this study.
This software was widely available at Landmark College in the library, computer laboratory, and
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other common study locations. It has all of the characteristics of assistive reading software that
are described at the beginning of this paper. Copies of the Kurzweil 3000 software were installed
on the personal computers of the participants so that they could use the software for reading in
their normal study locations.
MEASUREMENTS
The study required measurements of: reading speed and time to complete reading
assignments; reading comprehension; distractions; stress, fatigue, and duration of reading. We
used a combination of objective and subjective measurement instruments. Reading rate over a
brief period and comprehension were measured using the Nelson-Denny Reading Test (Brown,
Fishco, & Hanna, 1993), a passage comprehension and reading rate test standardized for
secondary and post secondary students. Time to complete assignments was determined by
having the students record in logs the number of pages read and the time spent during periods of
sustained reading of assignments for their English course.
Physiological indicators are often used to gauge attention, stress, and fatigue, an example
being the use of task-evoked pupillary response as an indicator of attention (Beatty, 1982).
These indicators, however, are difficult to measure, not easy to interpret, and require equipment
that was not available to us. As a result, we were forced to rely upon subjective measures of
these factors. Measuring when a student s attention was distracted and his/her mind had
wandered away from the reading task presented a special challenge. We used two approaches to
address this issue. One was a technique suggested by Levine (1994). He asked students to detect
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when their thoughts had wandered from their reading and to record such lapses of attention.
Levine called these notations of distractions canceled mind trips. This measure of distractibility
depends upon the student s ability to detect when he/she has been distracted, and, to our
knowledge, it has not been calibrated against more direct indicators of distraction. Nonetheless, it
is a measure that has attractive face validity and is easy to implement. We supplemented this
measure with structured attitude questionnaires based on a Likert scale (Likert, 1970) to obtain
self-assessments from the students about whether their distractibility, stress, fatigue, and
duration of sustained reading were affected by the assistive reading software. We also used
questionnaires to elicit the students impressions about the effect of the software on their reading
speed and comprehension. The self-assessments of reading speed and comprehension were
compared with the Nelson-Denny test results, and they showed that the students had a realistic
view of these skills. We could not verify the self-assessments of fatigue and stress since we did
not have objective measures of these factors.
EXPERIMENTAL DESIGN
Throughout this study we used an experimental design in which each participant served
as his/her own control. The performance of each participant was measured under two conditions:
without the Kurzweil software (the unassisted condition) and with the Kurzweil software (the
assisted condition). This design allowed direct comparison of the effects of the software upon the
performance of each participant and the use of more sensitive paired-comparison statistical tests.
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PROCEDURE
The study had five components that spread over a semester. The following summarizes
the procedures followed in each component.
1. Self-assessment. During the second week of the semester, the students were given a
Self-Assessment Questionnaire in which they were asked to assess their reading skills and to
report the problems they encountered when reading and the strategies they use to cope with
these problems.
2. Independent reading. Students were asked to keep reading logs during five typical
study sessions in which they independently (unobserved by the experimenters) read assignments
for their English 102 course at their normal study location. The reading assignments were given to
the students in electronic format ready for use on the Kurzweil 3000. Unassisted independent
reading took place during the third and fourth weeks of the semester. Assisted independent
reading took place after the Nelson-Denny tests were administered (see below). The students
were asked to record: the time spent reading, the pages read, and all canceled mind trips. We
obtained reading logs from 17 students for the unassisted condition, but from only 10 of the 20
students for the assisted condition, despite intense prompting from the experimenters.
3. Nelson-Denny tests. Early in the second month of the semester, after being trained to
use the Kurzweil software, the students were given the Nelson-Denny test to measure reading
rate and comprehension. For the unassisted condition, the students read the test from the
standard paper format. For the assisted condition, they used the Kurzweil software to read the
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passages and questions. Answers to questions were marked using the study skills highlighter of
the software. Different test forms were used for the two conditions, and care was exercised to
balance the order in which the unassisted and assisted versions of the test were administrated and
the forms used for each condition.
The Nelson-Denny test is a timed test that is normally completed in twenty minutes. We
recorded the number of questions answered in the standard 20-minute test period, but then
allowed the students to complete the test regardless of how long it took. Thus, we obtained a
timed comprehension score corresponding to the standard administration and an untimed score.
This second score provided a measure of comprehension when students compensate for their
poor reading or attention skills by spending as much additional time as they choose. After the
Nelson-Denny tests were completed, we administered a second questionnaire in which we asked
the students to compare their unassisted and assisted reading with respect to reading speed,
comprehension, stress, fatigue, and distractibility.
4. Extended reading. Toward the beginning of the third month the students participated in
an observed extended reading session. They read selections from their English 102 assignments
for 35 minutes unassisted and for 35 minutes assisted by the Kurzweil software. While reading,
they recorded canceled mind trips. The order of administration was balanced between assisted
and unassisted reading. When the extended reading was completed, a third questionnaire was
administered in which the students were asked once again to assess how their reading skills,
concentration, and reading strategies were affected by the Kurzweil software. Nineteen students
participated in this part of the study; one had dropped out of the study.
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5. End of semester questionnaire. In the last two weeks of the semester a final
questionnaire was administered. The questionnaire asked the students about their continued and
voluntary use of the software during the last part of the semester, and once again they were asked
to judge whether the reading software changed their speed, comprehension, duration of reading,
distractibility, fatigue, and reading strategies. Questionnaires were obtained from 16 of the 20
students who began the study.
TRAINING
During the self-assessment session at the beginning of the semester, the students were
trained to use the reading logs to record canceled mind trips, the number of pages read, and the
time spent reading. Before the Nelson-Denny tests were administered, the students were trained
to use the Kurzweil reading software. First they were trained to use it for basic reading and then
to use the study skills tools that are incorporated in the software for highlighting and note taking.
Training was conducted in small groups of two to four students or individually as required by
student schedules. Students received from one to three hours of training. The amount of training
each student received depended upon the student s prior experience with the Kurzweil software
and their skill in using computers. Additional training was given to students who requested it or
who did not appear to have become proficient in the use of the software. The students were also
given fifteen to twenty minutes of refresher training at the beginning of the Nelson-Denny test
sessions during which time they practiced taking the test with the reading software. The test
form used for this training was different from those used for actual measurement.
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RESULTS
UNASSISTED READING PERFORMANCE
The median reading rate of students at the beginning of their second year of community
college, as measured by the Nelson-Denny test, is approximately 250 words per minute (wpm)
(Brown, et al., 1993). The median unassisted reading rate of the students in our study was 162
wpm. Ninety-five percent of our students had reading rate scores less than the community
college median, and the median reading rate of our students was at the 13th percentile of their
community college peers. Slow reading affected the students timed comprehension scores.
Seventy five percent of our students had Nelson-Denny timed comprehension standard scores
below the community college median; the median of our students was at the 28th percentile of
their community college peers. However, when the students were not limited in time, 80% scored
above the community college median. The median untimed comprehension score for our students
was at the 80th percentile of their community college peer group. By spending extra time, our
students achieved excellent comprehension.
The students self-assessments of their reading performance obtained at the beginning of
the study are in reasonable agreement with the results from Nelson-Denny tests. Seventy-four
percent of the students reported that they read more slowly than the average student, as
compared with the 95% who actually had reading rate test scores below the community college
median. Twenty-one percent reported that their comprehension was lower than average, as
compared with the 20% who actually had untimed comprehension test scores less than the
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community college median. Their positive self-assessment of comprehension was evidently a
report on their ability in the absence of time limits.
The self-reports of the students principal reading problems mirror many of the reading
problems typical of students with attention disorders discussed earlier. Seventy percent of the
students reported that they miss or skip important parts of the text, from elements of words
through whole sentences. Almost half reported that the sentences that they read do not make
sense, 70% percent reported that they do not remember what they read, and 35% make frequent
use of context and probability to infer meaning.
The self-reported coping strategies employed by the students reflect these problems. To
reduce external distractions, 75% of the students seek to read in a quiet place. To maintain
attention and keep focused on their reading, the same percent use active reading techniques
(underlining, note taking, etc.), and more than half take a stimulant such as coffee. To deal with
the fatigue and stress of reading 60% take frequent breaks. Three quarters of the students find
that they have to re-read words, phrases, sentences, and even paragraphs in order to make sense
of the material and to assimilate it into memory. We do not know whether our students practice
these strategies more intensively than do students without attention disorders when reading
material of equivalent difficulty, but it is notable that our students are aware that they employ
these strategies to cope with their reading problems.
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ATTENTION
The results related to attention are summarized in Table I. The principal measure of
distractibility was canceled mind trips per hour, which we refer to as distractions per hour.
During the Extended Reading sessions, the median number of distractions per hour in the assisted
reading condition was 50% of that in the unassisted condition, a significant difference (p < .01).
This result is supported by the data from unobserved Independent Reading in which the
distractions per hour were 65% less in the assisted condition than it was in the unassisted
condition. Only 10 students provided logs for both conditions, and the difference in distractions
was not significant (p = 0.25). A large fraction of the students (significant at the .05 and .01
levels as shown in Table I) agreed with questionnaire statements that they could concentrate
longer, their thoughts wandered less, they were distracted less, and they skipped less material
when they used the reading software, thereby confirming the canceled mind trip results.
Insert Table I about here
The scatter plot of Figure 1 shows that there are substantial differences among the
students in how they responded to the assistive reading software. The change in distractions per
hour (∆Distractions/hr) experienced by the students depended strongly upon their distractions
per hour in unassisted reading (Unassisted Distractions/hr). (Note: positive values of
∆Distractions/hr correspond to increases in distractions when the Kurzweil software is used).
The linear regression model shown in the figure accounts for 79% of the variance of
∆Distractions/hr (r = - 0.89), and it summarizes quite well the nature of these individual
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differences. The model predicts that students whose Unassisted Distractions/hr are less than 5.2,
the x-intercept of the regression line, will experience an increase in distractions, and those whose
Unassisted Distractions/hr are greater than this x-intercept will experience decreases. Sixteen of
the 20 students conformed to this model. Furthermore, the regression model indicates that the
reduction in distractions is approximately proportional to the distractions in unassisted reading.
Students who are distracted most will tend to experience the greatest reduction in distractions
when they use the Kurzweil software. Striking departures from the model occurred for just two
of the students who had a small number of distractions in unassisted reading but showed large
values of ∆Distractions/hr.
Insert Figure 1 about here
STRESS, FATIGUE, AND DURATION OF READING
The questionnaire results related to stress, fatigue and duration of reading are summarized
in Table II. In the questionnaires administered after the Nelson-Denny testing, 80% of the
students, agreed that reading with the Kurzweil software was less stressful (significant, p < .05);
at the end of the semester, seventy-five percent of the students agreed that reading was less tiring
(not significant, p = 0.08). In the Extended Reading Questionnaire, the students were asked to
estimate how long they could concentrate on their reading before needing to take a break. The
mean of these duration estimates was 53 minutes for assisted reading versus 32 minutes for
unassisted reading, a 66% increase (significant at the .05 level). Results from the End of Semester
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Questionnaire gave a similar result; duration of sustained reading was estimated to increase by
60% (significant at the .05 level).
Insert Table II about here
READING SPEED AND TIME TO COMPLETE ASSIGNMENTS
The Nelson-Denny test measures the reading rate in the first one minute of the test, thus
providing a reading speed estimate over a brief time early in a reading session. There was no
significant difference between the average one-minute reading rates for assisted and unassisted
reading (Table III). However, there were substantial individual differences among the students,
which are evident in the Figure 2, a scatter plot of the change in rate (∆Reading Rate) for each
student plotted against that student s Unassisted Reading Rate. (Note: positive values of
∆Reading Rate correspond to improvements in rate when the Kurzweil software was used.) The
linear regression model shown in Figure 2 accounts for 82% of the variance of ∆Reading Rate (r =
- .91) and is therefore a good model for the data. There is a strong linear relationship between
∆Reading Rate and Unassisted Reading Rate. The model predicts that students whose unassisted
reading rate is less than 187 wpm, the x-intercept of the regression line, will experience increases
in rate when they use the assistive reading software, and those whose unassisted reading rate is
greater than 187 wpm will experience decreases. Seventeen of the 20 students conformed to this
model. Students whose unassisted reading rate is slowest obtain the greatest increases from the
assistive reading software, as is evident from the inverse relationship between ∆Reading Rate and
Unassisted Reading Rate. The fact that the regression line has a negative slope of — 1.1 means
19
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
that the changes in reading rate will be of a magnitude and direction that cause the assisted reading
rates of all the students to cluster around the x-intercept rate of 187 wpm. Seventeen of the 20
students had assisted reading rates that were 187 wpm + 20%.
Insert Figure 2 about here
The reading speed during periods of sustained reading of course assignments, such as the
Extended Reading and Independent Reading Sessions (Table III), provides a better estimate of
how long it takes to complete reading assignments than does the one-minute reading rate from the
Nelson-Denny test. The effects of fatigue, distractions, and other problems have more
opportunity to exert their influence. In the Extended Reading Sessions the page rate (number of
pages read per minute) in the assisted condition was 54% higher rate than the unassisted page
rate, a significant difference (p < .05). This result is supported by the data from the small sample
of 10 students who returned logs for Independent Reading and whose assisted page rate was 44%
greater than their unassisted rate (not significant, p = .23). Further support is provided by
additional data obtained from the Nelson-Denny tests. The Nelson-Denny test required the
student to sustain reading for periods that averaged about 30 minutes. During the Nelson-Denny
tests, we recorded the time the students spent reading the test passages as well as the total time
to complete the test. The average time spent reading the passages decreased 29% when reading
was assisted (significant, p < .01). Student questionnaire responses, also in Table III, are
consistent with these sustained reading results. In the Extended Reading Questionnaire, 92% of
the students agreed that they could read faster with the assistive reading software (significant, p
20
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
< .01); in the End of Semester Questionnaire, 82% of the students supported this statement
(significant, p < .05).
Insert Table III about here
COMPREHENSION
There was no significant difference between the assisted timed comprehension test scores from
the Nelson-Denny test and the unassisted scores (not shown in the tables). However, as shown
by the scatter plot of Figure 3, there are substantial individual differences among students. (Note:
positive values of ∆Timed Comprehension correspond to improvements in comprehension score
when the Kurzweil software is used.) The timed comprehension data are more scattered than the
reading rate data, but a simple linear regression model still accounts for 49% of the variance of
∆Timed Comprehension (r = — 0.7). This linear model, represented by the regression line shown
in Figure 3, has a negative slope and crosses the x-axis at a standard score of 295.3 The model
indicates that students whose unassisted timed comprehension standard scores are less than the
x-intercept of 295 are likely to benefit from the Kurzweil software, while those whose unassisted
comprehension scores are greater than 295 are likely to see a reduction in score. It should be
noted that only 40% of the students experienced increases in timed comprehension scores when
they used the software.
Insert Figure 3 about here
21
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
As was the case for timed comprehension, there was no significant difference between the
assisted and unassisted untimed comprehension standard scores. The simple linear regression
model (not shown) relating ∆Untimed Comprehension to Unassisted Untimed Comprehension
accounted for only 28% of the variance of ∆Untimed Comprehension (r = - 0.53). However, a
stepwise multiple linear regression model incorporating both the Unassisted Untimed
Comprehension scores and the scores from the Arithmetic component of the WAIS-III accounted
for 44% of the variance of ∆Untimed Comprehension, equivalent to a correlation, r = 0.66. This
model is shown in Figure 4, which is a scatter plot of the actual changes in untimed
comprehension and the changes predicted by this multiple regression model. According to this
model, the change in a student s untimed comprehension score is influenced by two factors: the
unassisted untimed comprehension score and the score on the WAIS-III Arithmetic test. Students
who have the poorest untimed comprehension and the best results on the WAIS-III Arithmetic
test are likely to see the greatest increases in untimed comprehension scores when they use the
assistive reading software.
Insert Figure 4 about here
In the questionnaires administered after the Nelson-Denny test, only 50% of the students
agreed with questionnaire statements that their comprehension improved when they used the
reading software. In the End of Semester Questionnaire, 65% agreed that their comprehension
improved. Neither of these results was significantly different from the null hypothesis that
comprehension did not change. These responses are consistent with the Nelson-Denny results
that fewer than half the students showed higher timed or untimed comprehension scores when
22
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
they used the assistive reading software, and that the differences between assisted and unassisted
mean comprehension scores were not significantly different from zero for both timed and
untimed comprehension.
CONTINUED USE
After the Extended Reading sessions were completed, the use of the Kurzweil software
was no longer required by the study, and the students were free to use it or not. If they used the
software for courses other than English 102, they had to scan the material that they read, which
took extra effort on their part. Continued use for English 102 did not require scanning since all the
material for that course already had been provided to them in electronic format. In the End of
Semester Questionnaire we asked about their continued use of the software. Of the16 students
who answered this questionnaire, 11 continued to use the software for English 102, 4 had
stopped using it, and one did not have access to her own computer. Eight students used the
software for courses in addition to their English course. Thus, 73% of those who had easy access
to a computer, continued to use the software voluntarily for their English course, and 53% used it
additionally for other courses where scanning was required.
DISCUSSION
EFFECTS OF ASSISTIVE READING SOFTWARE
In this study we assessed the effects of the Kurzweil assistive reading software on a
group of 20 college students who had a recent diagnosis of attention disorder. Twenty five
23
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
percent of the students also had a diagnosis of reading disability. The students reading
performance when assisted by the Kurzweil software was compared with their unassisted
performance in three different settings: Nelson-Denny testing, reading course assignments
independently, and reading course assignments under observation. We were interested in
determining the effects of the assistive reading software on: attention; stress, fatigue, and duration
of reading; reading speed; and comprehension.
When reading normally, without the benefit of the assistive reading software, the students
in our study read slowly but were able to achieve excellent comprehension by spending more
time than students without disabilities. They reported having many of the reading difficulties that
are characteristic of students with attention disorders: missing or skipping parts of words, whole
words, phrases, sentences; sentences not making sense; not remembering what was read; and
relying on context to infer meaning.
We obtained consistent evidence that attention improved when the students used the
assistive reading software. The median number of distractions per hour, as measured by canceled
mind trips, was cut in half in observed extended reading sessions. This result was statistically
significant and was supported by limited data from independent reading by 10 students that
showed a 65% reduction in distractions per hour (not significant and possibly not representative
of the whole student group). The decrease in distractions was proportional to the number of
distractions per hour in unassisted reading. More than 80% of the students reported in a
questionnaire administered at the end of the semester that they could concentrate longer, that
24
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
their thoughts wandered less, and that they were less distractible. All of these results were
statistically significant.
We obtained support for our expectation that the improvement in attention would reduce
fatigue and stress and increase duration of sustained reading. Eighty percent of the students
reported that reading was less stressful (significant) and 75% that it was less tiring (not
significant). They also estimated in responses to two questionnaires administered at different
times that the duration of sustained reading increased by more than 60% with the assistive
reading software (significant).
We also obtained consistent evidence that reading speed during periods of sustained
reading increased with the assistive reading software, thus enabling assignments to be completed
in less time. Data supporting this result were obtained from four sources: Extended Reading
Sessions, Independent Reading, the passage reading times from the Nelson-Denny tests, and
questionnaire responses. In the Extended Reading sessions 75% of the students increased their
page rate during observed reading of course assignments; the increase in the mean rate was 54%
(significant). Results from the small sample (n =10) of logs of independent reading of course
assignments showed an increase in page rate of 44% (not significant). The average time spent
reading the passages in the Nelson-Denny tests decreased by 29% (significant), corresponding to
an increase in reading speed. More than 80% (significant) of the students reported in
questionnaire responses that they could improve their reading speed by using the assistive
reading software, which is in very close agreement with the 75% who actually increased their
page rate in Extended Reading.
25
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
We infer, given the close correspondence between the questionnaire responses and the
percent of students who increased their sustained reading speed, that the students interpreted the
questions to be about their speed during periods of sustained reading like those required for
reading course assignments rather than about their one-minute reading rate from the NelsonDenny test. There are important differences in the two reading speed measures. The reading
speeds in sustained reading, especially the page rates derived from the reading of course materials
during Extended Reading and Independent Reading, are more realistic estimates of the time to
complete assignments than the rate derived from the Nelson-Denny test during which long term
effects such as fatigue and loss of concentration do not have much chance to affect performance.
We saw a small (13%) and not significant increase in the average Nelson-Denny oneminute reading rate. The scatter plot of the Nelson-Denny reading rate results (Figure 2),
however, reveals important differences among students; the associated linear regression model,
given that it accounted for such a large fraction of the variance of the ∆Reading Rate data (82%),
provides an excellent model for predicting the change in reading rate that an individual is likely to
experience when using the assistive reading software. It predicts that those students whose
unassisted reading rate was slower than the x-intercept of the regression line, 187 wpm, should
have an increase in reading rate while those who read faster should have a decrease, as the assisted
reading rates converge toward 187 wpm. This x-intercept value is approximately the average
speech speed setting of the Kurzweil reading software. All of the students chose speech speeds
that were within the range of conversational speech, normal oral reading, and that were
comfortable to listen to and understand.
26
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Our expectation that comprehension would improve with the assistive reading software,
especially given the improvements in attention, fatigue, stress, and speed, was not supported by
the results of the study. The improvements in reading speed for sustained reading should have led
to improvements in timed comprehension scores. However, only 40% of the students improved
either their timed or untimed comprehension test scores when using the assistive reading
software. The average comprehension scores of the group were essentially unchanged. This is
consistent with the questionnaire results that only 50% to 65% of the students agreed with
statements that the software improved comprehension, results that were not significantly
different from the null hypothesis that there was no change in comprehension.
The lack of improvement in timed comprehension, in spite of the improvements in
reading speed, can be understood by examining the average time to read the Nelson-Denny
passages (Table III) and the average total time to complete the Nelson-Denny test. Although the
time to read the passages declined substantially, as we would expect from the improvement in
reading speed, the average total time to complete the test did not change. Enough additional time
was consumed in answering the test questions with the Kurzweil software and in operating the
software so as to cancel out the benefits to the timed comprehension scores of faster reading.
Marking answers with the study skills highlighter of the Kurzweil software was slower than
marking answers on the paper form used in the unassisted condition, and time was lost in starting
and stopping the Kurzweil software after each passage and each of the questions relating to the
passages. These factors may account for the greater time spent on activities other than passage
reading. The lack of improvement in untimed comprehension scores may be due to a ceiling
27
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
effect. The median unassisted untimed comprehension standard score was high, almost one
standard deviation above the median of the community college peer group of our students. There
was not much room for improvement.
The best regression model for timed comprehension was a simple regression model based
only on unassisted timed comprehension (Figure 3). It accounted for half the variance of the
changes in timed comprehension scores (∆Timed Comprehension). No other factors, such as the
WAIS-III component test results, improved the model. The best regression model for untimed
comprehension was a multiple regression model incorporating both the WAIS Arithmetic score
and unassisted untimed comprehension (Figure 4). It accounted for 44% of the variance of
∆Untimed Comprehension, and it was considerably better than the simple regression model based
only on unassisted untimed comprehension scores, which accounted for only 28% of the
variance. This multiple regression model indicates that students who have better scores on the
WAIS-III Arithmetic test will get the greater benefit from the assistive reading software. We have
only a tentative explanation for this result. One of us (Katz) has noted from her work with
students who have attention disorders that those students who benefit from repetition also
benefit from having information presented through two modalities. The assistive reading software
presents text through both auditory and visual modalities; the WAIS-III Arithmetic test, which is
an oral test, permits the student to request a repetition of the arithmetic problem. It is the only
component of the WAIS-III in which repetition is permitted and important. We speculate that
those students who did well on the Arithmetic test benefited from repetition and, therefore, also
28
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
benefited from the multimodal presentation of the software, but this obviously needs further
investigation..
Finally, the fact that more than 70% of the students who had easy access to a computer
continued to use the software after it was no longer required indicates that the students
considered the software to be of significant benefit to them for reading and studying for their
courses.
COMPARISON WITH STUDENTS HAVING READING DISABILITIES
It is interesting to compare our results with those obtained by Elkind (1998) with community
college students who were diagnosed as having reading disabilities. The Nelson-Denny reading
rate results from the two studies are in very close agreement, with very similar regression models
for the data. The comprehension results are different. In the reading disabilities study, the best
regression models for both timed and untimed comprehension incorporated the results of the
WAIS Vocabulary test as well as the unassisted comprehension scores. In the current attention
disorders study, the best regression model for timed comprehension incorporated only unassisted
comprehension; the best model for untimed comprehension incorporated the WAIS-III
Arithmetic score as well as the unassisted untimed comprehension. The WAIS-III Vocabulary
test measures understanding of spoken words. Students with reading disability typically have
difficulty decoding words, and those with good oral language find that the auditory presentation
provides an alternative access to words that they cannot decode easily. For students with
attention disorders, decoding of words is not so much of a problem; their difficulty is assimilating
29
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
the information received. As mentioned earlier, the WAIS-III Arithmetic test results may provide
an indication of the extent to which the multimodal presentation enhanced assimilation of
information.
LIMITATIONS
This study was done with a small group of post secondary students from a specialized
private college that attracts students who are aware of their disabilities and who have made a
multi-year commitment to learn strategies for addressing them. The response of this group to the
assistive reading software may not be similar to the response of the general population of post
secondary students with attention disorders.
In this study we relied on subjective measures of distractions -- canceled mind trips and
questionnaire responses -- to gauge the effect of the software on attention, fatigue, stress, and
duration of sustained reading. We do not have independent verification that students are
accurately able to detect when their thoughts have wandered and that they can do so equally well
in unassisted reading and when using the software. Similarly, we do not have verification that
student questionnaire reports on issues like their ability to concentrate, the amount of skipping
through the text, and the stress and fatigue of reading actually reflects their functioning or
behavior. Although questionnaire responses were consistent with measurements of reading speed,
comprehension, and canceled mind, we must interpret them as reflecting how the students feel
about these issues, which may be different from what is occurring physiologically or
behaviorally.
30
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
This study was done with particular assistive reading software, the Kurzweil 3000. We
believe that the effects observed depended upon the general features and qualities of the software,
such as: good quality speech, text that is highlighted as it is read, good synchronization between
the highlighted and spoken words, and convenient study skills tools. Similar software that has
these qualities should provide similar results.
CONCLUSION
Assistive reading software can benefit many people who have attention disorders, just as it
has benefited people who have reading disabilities. It allowed our students to attend better to their
reading, to reduce their distractibility, to read with less stress and fatigue, and to read for longer
periods of time. It helped most of them to read faster so they could complete reading assignments in
less time. The software did not have a significant effect on comprehension of the entire group of
students, but it did benefit those who had very low comprehension test scores. This software should
be considered as a significant intervention to assist students who have attention disorders and as an
accommodation to help them compensate for their disabilities.
ACKNOWLEDGMENTS
The authors wish to thank their colleagues at Landmark College, for their support and
encouragement of this study. In particular, we thank Sirkka Kauffman, former Director of
Evaluation at Landmark College, for her active participation in the study and her generous and
perceptive insights. We appreciate the assistance of Arne Anderson with the data analysis. We
especially want to acknowledge the students of Landmark who participated in the study for their
time, good cheer, and insights. This study was supported in part by a Federal Title III grant to
31
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Landmark College, and by Kurzweil Educational Systems. We thank them for their financial
support.
Please address correspondence to: Linda Hecker, Landmark College, River Road, Putney, VT,
05346. Telephone: (802) 387 6718; Fax: (802) 387-6880; Email: [email protected].
32
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
FOOTNOTES
1
The principal products currently available with all the capabilities described are the Kurzweil
3000 from Kurzweil Educational Systems and WYNN from Freedom Scientific 2002. Similar
software with a subset of these capabilities includes WordSmith from TextHelp and e-Reader
from CAST. Information about these products can be obtained from their websites:
www.Kurzweiledu.com; www.FreedomScientific.com; www.texthelp.com; and www.cast.org.
2
We follow the practice of Schulte, Conners, and Osborne (1999) in using the term attention
disorders to refer to the diagnosis of ADD or ADHD, due to the variability of how those
diagnostic terms have been applied.
3
The Nelson-Denny standard scores were normalized by setting the mean for the group of
students used to standardize the test at 300 and the standard deviation at 15. The standardizing
group spanned the grades from high school through college. The mean comprehension standard
score for community college students at the beginning of the second year is 305. The x-intercept
of 295 is 2/3 of a standard deviation below this and corresponds to the 26th percentile of this peer
group. Students who have scores below 295 have poor comprehension relative to most of their
peers.
33
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
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FIGURE LEGENDS
Figure 1. The effect of the Kurzweil 3000 on distractions. The reduction in the number of
distractions per hour (∆Distractions/hr) in the Extended Reading sessions when the Kurzweil
3000 is used is approximately proportional to the number of distractions per hour in unassisted
reading (Unassisted Reading Distractions/hr). The linear regression model indicates that students
who have the highest rate of distractions in unassisted reading are likely to have the greatest
reduction in distractions.
Figure 2. Effect of the Kurzweil 3000 on reading rate. The change in Nelson-Denny
reading rate that result from the use of the Kurzweil 3000 depends inversely upon the students
unassisted reading rate. The linear regression model indicates that students who have unassisted
rates less than the x-intercept of 187 wpm are likely to experience increases in rate; those with
unassisted rates greater than187 wpm will tend to have reductions in rate.
Figure 3. Effect of the Kurzweil 3000 on Nelson-Denny timed comprehension scores.
Students with poor unassisted comprehension tend to benefit the most from the assistive reading
software.
Figure 4. Comparison between the actual changes in untimed comprehension scores
resulting from the use of the Kurzweil 3000 and the predicted changes in untimed comprehension
obtained from a multiple regression model that incorporates the unassisted untimed
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HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
comprehension scores and the WAIS-III Arithmetic test scores. Students who have poor
unassisted comprehension and good WAIS-III Arithmetic scores will tend to benefit the most.
7
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Figure 1
40
∆Dist ract ions/ hr = -0.78(Unassist ed Dist ract - 5.2)
Correlat ion, r= - 0.89
∆ Distractions/hr
20
0
-20
-40
-60
0
20
40
60
Unassist ed Dist ract ions/ hr
8
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Figure 2
∆ Reading Rate (wpm)
150
75
0
-75
∆Rat e = -1.1(Unassist ed Rat e - 187)
Correlat ion, r = - 0.91
-150
50
125
200
275
350
Unassist ed Reading Rat e (wpm)
9
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Figure 3
∆ Timed Comprehension (std score)
30
∆Timed Comp = -0.4 8(Unassist ed Comp - 295)
Correlat ion, r = - 0.7
15
0
-15
-30
270
290
310
330
350
Unassist ed Timed Comprehension (st d score)
10
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Figure 4
Actual ∆ Untimed Comprehension (std score)
20
Pred ∆Unt imed Comp = -0.40(Unassist ed Comp - 320)
+ 1.41(Arit h - 10)
Correlat ion, Act ual vs. Predict ed, r= 0.66
10
0
-10
-20
-20
-10
0
10
20
Predict ed ∆Unt imed Comprehension (st d score)
11
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Table I
Improvements in Attention
Measure
Source
Sample
Size, n
Unassisted
Reading
Median
Assisted
Reading
Median
Finding
Distractions (canceled mind trips)
Extended
Reading logs
19
8/hr.
4/hr.
50%reduction** 1
Distractions (canceled mind trips)
Independent
Reading logs
10
6/hr.
2/hr.
65% reduction ns 1
Concentrate longer End of Semester
with Kurzweil
Questionnaire
16
81% agreed* 2
Wander less with
Kurzweil
End of Semester
Questionnaire
16
81%* agreed* 2
Distracted less
with Kurzweil
End of Semester
Questionnaire
16
88% agreed** 2
Skip less with
Kurzweil
End of Semester
Questionnaire
16
81%* agreed* 2
** Significant at .01 level. * Significant at .05 level. ns Not significant.
1
Wilcoxon Signed Rank Test. 2One Sample Sign Test, ties included.
12
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Table II
Effect on Stress, Fatigue, and Duration of Reading
Measure
Source
Sample
Size, n
Unassisted
Reading
Mean
Assisted
Reading
Mean
Finding
Duration of
sustained reading
Extended
Reading
Questionnaire
19
32 min.
53 min.
66% increase* 1
Duration of
sustained reading
End of
Semester
Questionnaire
16
40 min.
64 min.
60% increase* 1
Reading less
stressful with
Kurzweil
NelsonDenny
Questionnaire
20
80% agreed* 2
Reading less tiring End of
with Kurzweil
Semester
Questionnaire
16
75% agreed ns 2
** Significant at .01 level. * Significant at .05 level. ns Not significant.
1
Paired t-Test. 2One Sample Sign Test, ties included.
13
HECKER, ET. AL.: ASSISTIVE READING SOFTWARE FOR STUDENTS WITH ATTENTION DISORDERS
Table III
Effects on Reading Speed
Measure
Source
Sample
Size, n
Unassisted
Reading
Mean
Assisted
Reading
Mean
Finding
13% increase ns 1
Reading rate
Nelson-Denny
testing
20
167 wpm
189 wpm
Time to read
passages
Nelson-Denny
testing
20
18 min.
14 min.
Page rate
Extended Reading
logs
173
0.25
pages/min.
0.38
54% increase* 1
pages/min.
Page rate
Independent
Reading logs
10
0.16
pages/min.
0 .23
44% increase ns 1
pages/min.
Read faster
with Kurzweil
Extended Reading
Questionnaire
19
92% agreed** 2
Read faster
with Kurzweil
End of Semester
Questionnaire
16
81% agreed* 2
29% decrease** 1
** Significant at .01 level. * Significant at .05 level. ns Not significant.
1
Paired t-Test. 2 One Sample Sign Test, ties included.
3
Data from two subjects were omitted because some of the pages they read had very sparse
content, and the page rates measured under the two conditions were not comparable.
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