|
Summary
Awards
In the News
Progress
Research Basis
Publications
Photos
Videos
People
|
Abstract
This document summarizes two types of published research
underlying Project LISTEN’s automated Reading
Tutor. Intervention studies measured
the Reading Tutor’s effectiveness.
Other research, others’ as well as our own, served to guide its
development. The cited Project LISTEN
publications can be downloaded from www.cs.cmu.edu/~listen
except where precluded by copyright or not yet in print.
Acknowledgements
Project LISTEN is supported by NSF under ITR/IERI Grant
REC-0326153, and by the Heinz Endowments. Any
opinions, findings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily reflect the views
of the National Science Foundation or the Heinz Endowments.
Special thanks to the co-principal investigators who helped formulate the
IERI proposals from which portions of this document are excerpted, especially
reading expert Professor Rollanda O’Connor.
1. Summary of intervention
studies
Speech-recognition-based,
computer-guided oral reading has demonstrated usability, user acceptance,
assistive effectiveness, and even pre- to post-test gains (Cole et al., 1999;
Mostow, Roth, Hauptmann, & Kane, 1994; Nix, Fairweather,
& Adams, 1998; Russell et al., 1996; Williams, 2002; Williams, Nix, &
Fairweather, 2000) – but the proof of
the pudding is whether it significantly increases learning gains over
gains that children make otherwise.
Even with barely 20 minutes of use per day, successive versions of the
Reading Tutor have produced substantially higher comprehension gains than
current practices in controlled studies lasting several months. To ensure
that results were due to the Reading Tutor intervention, we compared
different treatments within the same classrooms and randomized treatment
assignment, stratifying by pretest scores within class. We used valid and reliable measures (Woodcock, 1998) to measure gains from pre-
to post-test. We computed effect size
as the difference in gains between the Reading Tutor and current
practice, divided by the average standard deviation in gains of the two
groups. Effect sizes for passage
comprehension were substantial compared to other studies (NRP, 2000): 0.60 for 63 students in grades 2, 4, and 5
at a low-income urban school (Mostow & Aist, 2001; J.
Mostow et al., 2003); 0.48 for 66 third graders at a
lower-middle class urban school (Aist et al., 2001; Mostow et al.,
2001; Jack Mostow, Greg Aist et al., 2003); and 0.66 for 52 first graders at
two suburban Blue Ribbon Schools of Excellence (Mostow, Aist, Bey
et al., 2002; Mostow, Beck, & others, under revision).
1.1. Pilot study
(1996-97)
The Reading Tutor achieved
dramatic results in the first pilot study of extended use long enough to
demonstrate significant learning.
During the 1996-97 school year, a pilot group of low-reading third
graders used the Reading Tutor one at a time in a small office under the
individual supervision of a school aide.
According to school-administered pre- and post-tests, six third
graders who started almost three years below grade level averaged two years
of progress in under eight months use (Aist & Mostow,
1997).
1.2. Within-classroom
comparison (1998)
In spring 1998, we did our first controlled study of
the Reading Tutor in classroom settings at Fort Pitt Elementary (J. Mostow et al., 2003).
All 72 students in 3 classrooms (grades 2, 4, and
5) that had not previously used the Reading Tutor were independently
pre-tested on the Word Attack,
Word Identification, and Passage Comprehension subtests of the
Woodcock Reading Mastery Test (Woodcock,
1987). We split each class into 3 matched treatment groups – Reading
Tutor, commercial reading software, or regular classroom activities,
including other software use. We
assigned students to treatments randomly, matched within classroom by pretest
scores. Even though the study lasted only 4 months, and
actual usage was a fraction of the planned daily 20-25 minutes, students who
used the 1998 version of the Reading Tutor significantly outgained
their matched classmates in comprehension (effect size .60, p = .002), progressing
faster than their national cohort. (No
other differences were significant, and commercial software fell in
between.) As the principal said,
“these children were closing the gap.”
In
1999-2000, we evaluated the new, mixed story choice version of the Reading
Tutor at a second school in a lower-middle class community near Pittsburgh. This year-long study of 131 second and
third graders in 12 classrooms compared three daily 20-minute treatments. (a)
58 students in 6 classrooms used the 1999-2000 version of the Reading
Tutor. Students took daily turns using
one shared Reading Tutor in their classroom while the rest of their class
received regular instruction. (b) 34
students in the other 6 classrooms were pulled out daily for one-on-one
tutoring by certified teachers. To
control for materials, the human tutors used the same set of stories as the
Reading Tutor. (c) 39 students served
as in-classroom controls, receiving regular instruction without
tutoring. We pre- and post-tested
students in word identification, word attack, word comprehension, passage
comprehension, and fluency.
To our
surprise, human tutors beat the Reading Tutor only in Word Attack (effect
size .55). Third graders in both the
computer- and human-tutored conditions outgained
the control group in Word Comprehension (effect sizes of .56 and .72,
respectively) and Passage Comprehension (effect sizes of .48 and .55,
respectively) (Aist et al., 2001; Mostow et al., 2001). No other differences in gains were
significant.
1.4. Equal-time comparison
to Sustained Silent Reading
(2000-2001)
According
to the National Reading Panel, “the amount of gain attributable to
reading alone should be the baseline comparison against which the efficacy of
instructional procedures is tested. If an instructional method does better
than reading alone, it would be safe to conclude that method works” (NRP, 2000, Ch. 3, p.
27). A 7-month study of 178 students in grades
1-4 at two Blue Ribbon Schools of Excellence compared two treatments, each
provided in daily 20-minute sessions.
88 students did Sustained Silent Reading (SSR) as already implemented
in their classrooms (including teacher read-aloud in grade 1 until students
were ready for independent reading practice).
90 students in 10-computer labs used the 2000-2001 version of Project LISTEN’s Reading Tutor. The Reading Tutor group significantly outgained their statistically matched SSR classmates in
phonemic awareness, rapid letter naming, word identification, word
comprehension, passage comprehension, fluency, and spelling –
especially in grade 1, where effect sizes for between-treatment differences
in gains ranged from .20 to .72 (Mostow, Aist, Bey et al., 2002).
1.5. Effectiveness
for English language learners (2004) [this section added 6/6/05]
2004 marked
the first independent, third-party, controlled evaluation of the Reading
Tutor (Poulsen,
2004). This two-month pilot study included 34
second through fourth grade Hispanic students from four bilingual education
classrooms. The study compared the efficacy of the 2004 version of the
Project LISTEN Reading Tutor against the standard practice of Sustained
Silent Reading (SSR). This study was undertaken to obtain some initial
indication as to whether the tutor would also be effective within a population
of English language learners.
The study
employed a crossover design where each participant spent one month in each of
the treatment conditions. The experimental treatment consisted of 25
minutes per day using the Reading Tutor within a small pullout lab
setting. Students in the control treatment remained in the classroom
where they participated in established reading instruction activities.
Dependent variables consisted of the school district’s curriculum based
measures for fluency, sight word recognition, and comprehension.
The Reading
Tutor group outgained the control group in every
measure during both halves of the crossover experiment. Within-subject
results from a paired T-Test indicate that these gains were significant for
one sight word measure (p = .056) and both fluency measures (p <
.001). Effect sizes were 0.55 for timed sight words, a robust 1.16 for
total fluency and an even larger 1.27 for fluency controlled for word
accuracy. These dramatic results observed during a one-month treatment
indicate that this technology may have much to offer English language
learners.
Two
additional groups of Canadian researchers conducted independent evaluations
of the Reading Tutor with English language learners and as of June 2005 are
analyzing the data.
A 10-week
study by Kenneth Reeder, Margaret Early, Maureen Kendrick, Jon Shapiro, and
Jane Wakefield at the University of British Columbia (D’Silva
et al., 2005)
involved 77 students from five Vancouver elementary schools, grades 2-6 (ages
7-12 years). Their home languages were
Hindi (14), Mandarin (21), Spanish (21), and English (21: 11 using the
Reading Tutor, and 10 in a human tutoring program). Gains by the Reading Tutor group matched
gains by the human tutoring group on most reading measures, and interviews
showed favorable affect impact by the Reading Tutor. Analysis is continuing.
A 12-week
study by Esther Geva and Todd Cunningham at the
University of Toronto (Cunningham & Geva, 2005) involved 104 ESL students in grades 4-6 at
eight schools. The study compared
three treatments: the Reading Tutor; Kurzweil 3000, which reads aloud to the student and
provides vocabulary support; and regular ESL classroom instruction. Analysis of data from the first 39 students
shows promising trends.
2. Summary of underlying
research
Why does the
Reading Tutor improve comprehension?
Theoretically, students who recognize words effortlessly can devote
more attention to comprehension (LaBerge
& Samuels, 1974),
and the relationship between rate of oral reading and reading comprehension
is strong through the elementary years (Pinnel
et al., 1995). The cognitive load imposed by word
identification before it has become a mentally automatic process consumes
limited mental resources, such as attention and short term memory, needed to
comprehend the sentence and its relationship to the surrounding context (Perfetti,
1992).
However,
decoding practice by itself does not necessarily improve fluency or
comprehension. Some studies found that
teaching children to recognize isolated words quickly gave no advantage in
reading comprehension (Fleischer, Jenkins,
& Pany, 1979), or that comprehension did
not improve unless readers recognized the words nearly as fast in context as
in lists (Levy, Abello, & Lysynchuk, 1997). Thus fluency makes a unique contribution to
comprehension over that made by word identification (Ehri
& McCormick, 1998; O'Connor et al.,
2002; Shankweiler et al., 1999).
Guided oral
reading provides opportunities to practice word identification and
comprehension in context. There is
ample evidence that one of the major differences between good and poor
readers is the amount of time they spend reading. Poor readers are unlikely to practice on
their own. Students who need the most
practice spend the least amount of time actually reading (Allington,
1977). How time is spent reading matters
too (Mostow, Aist, Beck et
al., 2002). Poor readers tend to reread the same easy
stories over and over (Aist, 2002a). Modifying the Reading Tutor to take turns
picking stories exposed students to more new vocabulary than they saw when
they chose the stories (Aist, 2002a, 2002b;
Aist & Mostow, 2003; J. Mostow et al., 2003).
The Reading
Tutor aims for the zone of proximal development (Doolittle, 1997) by dynamically updating its
estimate of the student’s reading level, and picking stories
accordingly – which are somewhat harder than students choose when it is
their turn (Jack Mostow, Greg
Aist et al., 2003).
The Reading Tutor scaffolds
key processes in reading – and tests its own scaffolding. Scaffolding provides information at the
“teachable moments” when it is needed. For example, explicit vocabulary
instruction is important but time-consuming (Beck, McKeown, & Kucan, 2002). Explaining unfamiliar words and concepts in
context can remediate deficits in vocabulary and background knowledge (Elley,
1989),
so we added support for vocabulary acquisition by presenting short
“factoids” – comparisons to other words (Aist, 2001b, 2002a). An automated experiment embedded in the
Reading Tutor tested the effectiveness of reading a factoid just before a new
word in a story, compared to simply encountering the word in context without
a factoid. The outcome variable was
performance on a multiple-choice question, presented the next day the student
used the Reading Tutor. Analysis of
over 3,000 randomized trials showed that factoids helped on rare,
single-sense words, and that they helped third graders more than second
graders (Aist, 2000, 2001a,
2001b).
By acquiring predictive models of the effects of tutorial actions, embedded
experiments can inform a decision-theoretic approach to tutoring (Beck, 2001, 2002;
Beck & Woolf, 2000, 2001; Beck, Woolf, & Beal, 2000; Murray, VanLehn,
& Mostow, revisions under review).
The zone of
proximal development depends on tutorial scaffolding as well as on student
proficiency (Murray & Arroyo,
2002),
so the Reading Tutor lets the student read as much as possible, but helps as
much as necessary. It provides spoken
and graphical assistance when it notices the student click for help,
hesitate, get stuck, skip a word, make a mistake, or encounter a word likely
to be misread (Mostow & Aist,
1999). Its “visual speech” (Massaro,
1998)
uses talking-mouth videoclips of phonemes to
scaffold phonemic awareness. The
Reading Tutor assists word identification by previewing new words (Mostow, to appear) and reading hard words
aloud. Its word attack hints include
rhyming and sounding out. It supports
vocabulary acquisition by explaining new words (Aist, 2001b, 2002a;
Jack Mostow, Joseph Beck et al., 2003). It scaffolds comprehension by reading hard
sentences aloud and by asking questions (NRP, 2000) – “cloze”
items (Mostow, Tobin, &
Cuneo, 2002)
and generic “who-what-where” questions, which at first appeared
to boost comprehension of nearby sentences in an embedded experiment (Beck, Mostow, Cuneo,
& Bey, 2003). The Reading Tutor bolsters motivation by
listening attentively, “backchanneling”
(Aist & Mostow,
1999),
giving encouragement (Aist, Kort, Reilly, Mostow, & Picard, 2002), and praising good or
improved performance (Mostow & Aist,
1999).
By reducing frustration (Betts, 1946) and making a wide range of
authentic, engaging text cognitively accessible to the child, scaffolding
helps address the motivational issues of confidence, challenge, curiosity,
and control pivotal to effective tutoring (Lepper
& Chabay, 1988; Lepper,
Woolverton, Mumme, & Gurtner, 1993). Poor readers’ listening comprehension
is far above their independent reading level (Curtis, 1980; Spache, 1981), so reading hard words and
sentences to them reduces frustration and repairs comprehension failures
caused by lack of automaticity in word
identification.
One approach
to improving automaticity is repeated reading, in
which students read a passage or page of text until their reading rate
increases by a given amount, usually 25% or more (Samuels, 1979). A recent review of the repeated reading
literature (Meyer & Felton,
1999)
recommended that poor readers practice building fluency for 10-20 minutes per
day over a long duration, engage in reading aloud, and use text at their
instructional level. However, improving word recognition accuracy and
comprehension can require assistance to remediate errors (McCoy & Pany, 1986; Young, Bowers, & MacKinnon, 1996) – which requires
listening to the student read aloud.
References (download Project LISTEN publications at www.cs.cmu.edu/~listen)
Aist, G. (2000). Helping Children Learn Vocabulary during Computer-Assisted Oral
Reading. Unpublished Ph.D. dissertation, Carnegie Mellon University,
Pittsburgh, PA.
Aist, G.
(2001a). Factoids: Automatically constructing and administering vocabulary
assistance and assessment. In J. D. Moore & C. L. Redfield & W. L.
Johnson (Eds.), Artificial Intelligence
in Education: AI-ED in the Wired and
Wireless Future (pp. 234-245). San Antonio, Texas: Amsterdam: IOS Press.
Aist, G.
(2001b). Towards automatic glossarization: Automatically constructing and
administering vocabulary assistance factoids and multiple-choice assessment. International Journal of Artificial
Intelligence in Education, 12, 212-231.
Aist, G.
(2002a). Helping Children Learn Vocabulary During Computer-Assisted Oral
Reading. Educational Technology and
Society, 5(2), http://ifets.ieee.org/periodical/vol_2_2002/aist.html.
Aist, G.
(2002b, April 29). Helping Children
Learn Vocabulary during Computer-Assisted Oral Reading: A Dissertation Summary [Poster presented as
a Distinguished Finalist for the Outstanding Dissertation of the Year Award].
47th Annual Convention of the International Reading Association, San
Francisco, CA
Aist, G., Kort, B., Reilly, R., Mostow, J., & Picard, R. (2002,
October 14-16). Experimentally Augmenting
an Intelligent Tutoring System with Human-Supplied Capabilities: Adding Human-Provided Emotional Scaffolding
to an Automated Reading Tutor that Listens. Proceedings of the Fourth
IEEE International Conference on Multimodal Interfaces (ICMI 2002), Pittsburgh,
PA, 483-490.
Aist, G., &
Mostow, J. (1997, October). When Speech
Input is Not an Afterthought: A Reading Tutor that Listens. Workshop on
Perceptual User Interfaces, Banff, Canada
Aist, G., &
Mostow, J. (1999, September). Measuring
the Effects of Backchanneling in Computerized Oral
Reading Tutoring. Proceedings of the ESCA Workshop on Prosody and Dialog,
Eindhoven, Netherlands
Aist, G., &
Mostow, J. (2003). Faster, better task choice in a reading tutor that
listens. In F. N. Fisher (Ed.), Speech
Technology for Language Learning. Lisse, The
Netherlands: Swets & Zeitlinger
Publishers.
Aist, G.,
Mostow, J., Tobin, B., Burkhead, P., Corbett, A.,
Cuneo, A., Junker, B., & Sklar, M. B. (2001).
Computer-assisted oral reading helps third graders learn vocabulary better
than a classroom control – about as well as one-on-one human-assisted
oral reading. In J. D. Moore & C. L. Redfield & W. L. Johnson (Eds.),
Artificial Intelligence in
Education: AI-ED in the Wired and
Wireless Future (pp. 267-277). San Antonio, Texas: Amsterdam: IOS Press.
Allington, R. (1977). If they don't read much, how they
ever gonna get good? Journal of Reading, 21, 57-61.
Beck, I. L.,
McKeown, M. G., & Kucan, L. (2002). Bringing Words to Life: Robust Vocabulary Instruction. NY:
Guilford.
Beck, J. E.
(2001). ADVISOR: A Machine-Learning Architecture for
Intelligent Tutor Construction. Unpublished Ph.D., University of
Massachusetts Amherst, Amherst.
Beck, J. E.
(2002). Directing Development Effort
with Simulated Students. Proceedings of the Sixth International
Conference on Intelligent Tutoring Systems, Biarritz, France, 851-860.
Beck, J. E.,
Mostow, J., Cuneo, A., & Bey, J. (2003, July
20-24). Can automated questioning help
children's reading comprehension? Proceedings of the Tenth International
Conference on Artificial Intelligence in Education (AIED2003), Sydney,
Australia, 380-382.
Beck, J. E.,
& Woolf, B. P. (2000). High-level
Student Modeling with Machine Learning. Fifth International Conference on
Intelligent Tutoring Systems, 584-593.
Beck, J. E.,
& Woolf, B. P. (2001). Using
Rollouts to induce a policy from a user model. Eighth International
Conference on User Modeling, 210-212.
Beck, J. E.,
Woolf, B. P., & Beal, C. R. (2000). ADVISOR:
A machine learning architecture for intelligent tutor construction.
Proceedings of the Seventeenth National Conference on Artificial
Intelligence, Austin, Texas, 552-557.
Betts, E. A.
(1946). Foundations of Reading
Instruction. New York: American Book Company.
Cole, R., Massaro, D. W., Villiers, J. d., Rundle, B., Shobaki, K., Wouters, J.,
Cohen, M., Beskow, J., Stone, P., Connors, P., Tarachow, A., & Solcher, D.
(1999, April). New tools for
interactive speech and language training: Using animated conversational
agents in the classrooms of profoundly deaf children. ESCA/SOCRATES
Workshop on Method and Tool Innovations for Speech Science Education, London,
UK
Cunningham, T.,
& Geva, E. (2005, June 24). The effects of reading technologes
on literacy development of ESL students [poster presentation]. Twelfth
Annual Meeting of the Society for the Scientific Study of Reading, Toronto
Curtis, M. E.
(1980). Development of components of reading skill. Journal of Educational Psychology, 72(5), 656-669.
D’Silva, R., Hong, L., Lenters,
K., Early, M., Kendrick, M., Reeder, K., Shapiro, J., & Wakefield, J.
(2005, May). LISTENing to diverse learners: The effectiveness
and appropriateness of a computer-based reading tutor for young multilingual
learners [poster presentation]. Fifth Faculty of Education Research Day,
The University of British Columbia, Vancouver, BC
Doolittle, P.
E. (1997). Vygotsky's Zone of Proximal Development
as a Theoretical Foundation for Cooperative Learning. Journal on Excellence in College Teaching, 8(1), 83-103.
Ehri, L., & McCormick. (1998). Phases of word
learning: Implications for instruction with delayed and disabled readers. Reading and Writing Quarterly, 14,
135-164.
Elley, W. B. (1989). Vocabulary acquisition from
listening to stories. Reading Research
Quarterly, 24, 174-187.
Fleischer, L.
S., Jenkins, J. R., & Pany, D. (1979). Effects
on poor readers' comprehension of training in rapid decoding. Reading Research Quarterly, 14, 30-48.
LaBerge, D., & Samuels, J. (1974). Toward a theory of
automatic information processing in reading. Cognitive Psychology, 6, 293-323.
Lepper, M. R., & Chabay, R.
W. (1988). Socializing the intelligent tutor: Bringing empathy to computer
tutors. In H. Mandl & A. Lesgold
(Eds.), Learning issues for intelligent
tutoring systems (pp. 242-257). NY: Springer.
Lepper, M. R., Woolverton, M., Mumme, D. L., & Gurtner, J.
(1993). Motivational Techniques of Expert Human Tutors: Lessons for the Design of Computer-Based
Tutors. In S. P. Lajoie & S. J. Derry (Eds.), Computers as Cognitive Tools (pp.
75-105). Hillsdale, NJ: Erlbaum.
Levy, B., Abello, B., & Lysynchuk, L.
(1997). Transfer from word training to reading in context: Gains in reading
fluency and comprehension. Learning
Disabilities Quarterly, 20, 173-188.
Massaro, D. (1998). Perceiving
Talking Faces. Cambridge, MA: MIT Press.
McCoy, K.,
& Pany, D. (1986). Summary and analysis of oral
reading corrective feedback research. The
Reading Teacher, 39, 548-555.
Meyer, M. S.,
& Felton, R. H. (1999). Repeated reading to enhance fluency: Old
approaches and new directions. Annals
of Dyslexia, 49, 283-306.
Mostow, J. (to
appear). Evaluation purposes, excuses, and methods: Experience from a Reading Tutor that
listens. In L. Verhoeven (Ed.), Facilitating Constructivist Literacy
Environments Through Technology. Mahway, NJ:
Erlbaum.
Mostow, J.,
& Aist, G. (1999). Giving help and praise in a reading tutor with
imperfect listening -- because automated speech recognition means never being
able to say you're certain. CALICO
Journal, 16(3), 407-424.
Mostow, J.,
& Aist, G. (2001). Evaluating tutors that listen: An overview of Project
LISTEN. In K. Forbus & P. Feltovich
(Eds.), Smart Machines in Education
(pp. 169-234). Menlo Park, CA: MIT/AAAI Press.
Mostow, J.,
Aist, G., Beck, J., Chalasani, R., Cuneo, A., Jia, P., & Kadaru, K. (2002,
June 5-7). A La Recherche
du Temps Perdu, or As Time Goes By: Where does the
time go in a Reading Tutor that listens? Proceedings of the Sixth
International Conference on Intelligent Tutoring Systems (ITS'2002),
Biarritz, France, 320-329.
Mostow, J.,
Aist, G., Bey, J., Burkhead,
P., Cuneo, A., Junker, B., Rossbach, S., Tobin, B.,
Valeri, J., & Wilson, S. (2002, June 27-30). Independent practice versus computer-guided oral reading: Equal-time
comparison of sustained silent reading to an automated reading tutor that
listens. Ninth Annual Meeting of the Society for the Scientific Study of
Reading, Chicago, Illinois
Mostow, J.,
Aist, G., Burkhead, P., Corbett, A., Cuneo, A., Eitelman, S., Huang, C., Junker, B., Platz,
C., Sklar, M. B., & Tobin, B. (2001). A
controlled evaluation of computer- versus human-assisted oral reading. In J.
D. Moore & C. L. Redfield & W. L. Johnson (Eds.), Artificial Intelligence in Education: AI-ED in the Wired and Wireless Future
(pp. 586-588). San Antonio, Texas: Amsterdam:
IOS Press.
Mostow, J.,
Aist, G., Burkhead, P., Corbett, A., Cuneo, A., Eitelman, S., Huang, C., Junker, B., Sklar,
M. B., & Tobin, B. (2003). Evaluation of an automated Reading Tutor that
listens: Comparison to human tutoring
and classroom instruction. Journal of
Educational Computing Research, 29(1), 61-117.
Mostow, J.,
Aist, G., Huang, C., Junker, B., Kennedy, R., Lan,
H., Latimer, D., O'Connor, R., Tassone, R., Tobin,
B., & Wierman, A. (2003). 4-Month Evaluation of
a Learner-controlled Reading Tutor that Listens. In F. N. Fisher (Ed.), Speech Technology for Language Learning.
Lisse, The Netherlands: Swets
& Zeitlinger Publishers.
Mostow, J.,
Beck, J., Bey, J., Cuneo, A., Sison,
J., & Tobin, B. (2003, June 12-15). An
Embedded Experiment to Evaluate the Effectiveness of Vocabulary Previews in
an Automated Reading Tutor. Tenth Annual Meeting of the Society for
Scientific Studies of Reading, Boulder, CO
Mostow, J.,
Beck, J., & others. (under revision). Computer-Guided Oral Reading versus
Independent Practice: Comparison of
Sustained Silent Reading to an Automated Reading Tutor that Listens. Journal of Educational Psychology.
Mostow, J.,
Roth, S. F., Hauptmann, A. G., & Kane, M. (1994, August). A prototype reading coach that listens
[AAAI-94 Outstanding Paper Award]. Proceedings of the Twelfth National
Conference on Artificial Intelligence, Seattle, WA, 785-792.
Mostow, J.,
Tobin, B., & Cuneo, A. (2002, June 3). Automated comprehension assessment in a reading tutor. Proceedings
of the ITS 2002 Workshop on Creating Valid Diagnostic Assessments, San
Sebastian, Spain, 52-63.
Murray, R. C.,
VanLehn, K., & Mostow, J. (revisions under review). Looking Ahead to
Select Tutorial Actions: A Decision-Theoretic Approach. International Journal of Artificial Intelligence in Education.
Murray, T.,
& Arroyo, I. (2002). Towards
Measuring and Maintaining the Zone of Proximal Development in Adaptive
Instructional Systems. Sixth International Conference on Intelligent
Tutoring Systems, 749-758.
Nix, D., Fairweather, P., & Adams, B. (1998). Speech Recognition, Children, and Reading.
CHI 98, 245-246.
NRP. (2000). Report of the National Reading Panel.
Teaching children to read: An
evidence-based assessment of the scientific research literature on reading
and its implications for reading instruction (00-4769). Washington, DC:
National Institute of Child Health & Human Development.
O'Connor, R.
E., Bell, K. M., Harty, K. R., Larkin, L. K., Sackor, S., & Zigmond, N.
(2002). Teaching Reading to Poor Readers in the Intermediate Grades: A
Comparison of Text Difficulty. Journal
of Educational Psychology, 94, 474-485.
Perfetti, C. A. (1992). The representation problem in
reading acquisition. In P. B. Gough & L. C. Ehri
& R. Treiman (Eds.), Reading Acquisition (pp. 145-174). Hillsdale, NJ: Lawrence
Erlbaum.
Pinnel, G. S., Pikulski, J. J.,
Wixson, K. K., Campbell, J. R., Gough, P. B., &
Beatty, A. S. (1995). Listening to
children read aloud. Washington, DC: Office of Educational Research and
Improvement, U.S. Department of Education.
Poulsen, R. (2004). Tutoring
Bilingual Students With an Automated Reading Tutor That Listens: Results of a Two-Month Pilot Study.
Unpublished Masters Thesis, DePaul University, Chicago, IL.
Russell, M.,
Brown, C., Skilling, A., Series, R., Wallace, J.,
Bonham, B., & Barker, P. (1996). Applications
of automatic speech recognition to speech and language development in young
children. Proceedings of the Fourth International Conference on Spoken
Language Processing, Philadelphia PA
Samuels, S. J.
(1979). The method of repeated readings. The
Reading Teacher, 32, 403-408.
Shankweiler, D., Lundquist, E., Katz, L., Stuebing, K. K., Fletcher, J. M., Brady, S., Fowler, A.,
Dreyer, L., Marchione, K. E., Shaywitz,
S. E., & Shaywitz, B. A. (1999). Comprehension
and decoding: Patterns of association in children with reading difficulties. Scientific Studies of Reading, 3,
69-94.
Spache, G. D. (1981). Diagnostic
Reading Scales. Monterey, CA: McGraw-Hill.
Williams, S. M.
(2002). Speech recognition technology and the assessment of beginning
readers. In NRC (Ed.), Technology and
assessment: Thinking ahead: Proceedings of a workshop (pp. 40-49).
Board on Testing and Assessment, Center for Education, Division of Behavioral
and Social Sciences and Education.
Washington, DC: National Academy Press.
Williams, S.
M., Nix, D., & Fairweather, P. (2000). Using Speech Recognition Technology to
Enhance Literacy Instruction for Emerging Readers. Fourth International
Conference of the Learning Sciences, 115-120.
Woodcock, R. W.
(1987). Woodcock Reading Mastery
Tests-Revised. Circle Pines, MN: American Guidance Service.
Woodcock, R. W.
(1998). Woodcock Reading Mastery Tests
- Revised (WRMT-R/NU). Circle Pines, Minnesota: American Guidance
Service.
Young, A. R.,
Bowers, P. G., & MacKinnon, G. E. (1996). Effects of prosodic modeling
and repeated reading on poor readers' fluency and comprehension. Applied Psycholinguistics, 17, 59-84.
|
