The Literacy
Challenge. Reading
is fundamental. If children don't learn
to read independently by the fourth grade, they fall further and further behind
in school. One-on-one instruction by
trained human tutors succeeds in helping kids learn to read, but is expensive
and sometimes unavailable. Efforts to
duplicate the effects of one-on-one tutoring in large group settings have
typically not matched the performance of human tutors.
The TechnologyOpportunity.
Advances in speech recognition and spoken dialog technology have made
possible computer-based reading tutoring.
Intelligent tutoring systems based on cognitive principles have
previously proven successful in such varied domains as algebra and computer
programming. By combining spoken
dialogue and intelligent tutoring systems we hope to come closer to the goal of
a "two-sigma" computer tutor for reading -- one that duplicates the
two-standard-deviation gain in reading skill observed for human-human tutoring.
Research Strategy. One methodology
in reading research is to study successful human tutors and identify what makes
tutorial dialog effective.
Unfortunately, human-human tutorial dialog cannot be directly imitated
by the computer. Errors in speech
recognition, combined with the broad range of discourse, domain and world
knowledge used by human tutors, require an indirect approach. Therefore, we intend to identify a few
critical reading skills and explore which features of human-human dialog
effectively train those skills. For each
skill, by combining a cognitive model of the skill with the dialog features
that effectively train components of that skill, we will design human computer
multimodal dialogues that successfully train the desired skill.
Organization of thisProposal. In
this proposal, we briefly describe related research in beginning reading and in
educational software for reading. We
then describe Project LISTEN’s Reading Tutor, including some of the technological
and design considerations that will guide our dialog design for reading
tutoring. Next, we suggest several examples of reading skills we might focus
on: word attack, word comprehension, and passage comprehension. For each example skill, we describe how that
skill is learned and taught, discuss hypothetical computer human dialogs
designed to train that skill, propose methods for evaluating the effectiveness
of such dialogs, and consider the expected contributions of developing
successful dialogs. (As implemented in
the thesis, these dialogs may be newly designed, or they may be modifications
of existing dialogs within the Reading Tutor.)
We claim that skill specific human computer multimodal dialogs, based on
cognitive skill models and successful human tutoring strategies, can improve
elementary students' reading abilities.
Related
Research
BeginningReading. Many
factors involved in achieving competence in early reading. For poor readers,
word recognition skills are critical (Ehrlich 1993, Stanovich 1991). For good readers, other factors including
metacognitive skills and motivation are also important:
“Basic word decoding and perceptual skills
are necessary in order to read; if a child lacks these cognitive skills, even
the most adaptive attribution and self-efficacy beliefs will not magically
reveal the meaning behind the text. Thus for poor readers, word decoding skill
is highly related to comprehension ability.
In contrast, for good readers who
possess adequate decoding skills, motivational variables such as perceived
competence emerge as influential factors determining reading performance.”
(Ehrlich 1993). In addition to
predicting immediate ability, poor word decoding skills are a good predictor of
long-term reading difficulties (Ehrlich 1993).
Beyond word recognition, fluent reading
relies on lower-level cognitive skills such as symbol-naming ability (Bowers
1993). Phonological awareness is also a
factor, but it is not clear whether this is an result of word-recognition
skills or an independent contribution to reading success (Bowers 1993).
Individual differences also play a role in
achieving reading fluency. For example, while some poor readers learn better
with instruction including “Listening Previewing”, or hearing a passage read
aloud while following along in the text (Daly and Martens 1994), some learn
better without previewing (Tingstrom 1995).
What role do segmentation skills play in
beginning reading? Nation and Hulme
(1997) found that phonemic segmentation predicts early reading and spelling
skills more than onset-rime segmentation. Peterson and Haines (1992) found that
training kindergarten children to construct rhyming words from onsets and rimes
improved children's segmentation ability, letter-sound knowledge, and ability
to read words by analogy.
What about selection of material? Rosenhouse et al. (1997) found that
interactive reading aloud to first-graders led to increases in decoding,
passage comprehension, and picture storytelling. Rosenhouse et al. also found that reading serial
stories (stories with the same characters and moderately predictable plots or
conflicts) had a positive impact on the number of books bought for pleasure
reading.
A review of the literature by Roller (1994)
reveals that teachers interrupt poor readers more frequently than good readers,
but which comes first (poor reading or interruption) is not clear. Also, with good readers, more emphasis is
placed on meaning. Again, the reasons
and causal relationships are unclear. Because of the importance of word
recognition in learning to read, reading software should encourage the
development of word decoding skills and aim to increase the sight vocabulary of
the student. However, since motivational
variables become important for good readers, and the goal of the software is to
enable poor readers to become good readers, reading software should ideally
also encourage the development of positive motivational attitudes towards
reading and provide experiences that let the student experience the joy of
reading.
Educationalsoftware for reading. While the literature on
intelligent tutoring systems is quite substantial, we will focus here on
automated reading tutors. Commercial reading systems provide help on demand,
and some (Vanderbilt 1996) even provide the opportunity for students to record
their own readings of the material. Use
of speech recognition in reading systems is much more rare and is at the
research stage.
Some reading software systems provide
spoken assistance on demand (Discis 1991), (Edmark 1995), (Learning Company
1995), and the use of synthesized speech has been explored in a research
context (Lundberg and Olofsson1993). The Little Planet Literacy Series
(Vanderbilt 1996) provides help on demand and allows children to record their
own voices, but it does not use speech recognition and therefore cannot judge
the quality of the child's reading or offer help based on such a judgement.
Previous research has demonstrated that mouse- or keyboard-oriented
computer-assisted instruction can improve reading skills such as phonological
awareness and word identification (e.g. Barker and Torgesen 1995).
Lewin (1998) in a study of “talking book” software, found that such
software was typically used with students in pairs (82%) or as individuals
(29%), or occasionally with students in larger groups (11%). Pairs or groups were more commonly normally
progressing readers, perhaps to ensure that all students were able to use the
software while allowing poorer readers more individual time. Teachers requested additional feedback from
the software, such as onset and rime, and “hints”. Teachers also requested more
"reinforcement activities" for particular skills . Most teachers, however, made little use of
records of which words kids clicked on.
As a cautionary note, in software filled
with animated or talking characters, children may spend large amounts of time
clicking on the characters to see the animation (Underwood and Underwood 1998),
to the detriment of time spent reading.
What is it that distinguishes human
tutoring from most reading software? One
fundamental difference is that in human tutoring, to one extent or another, the
student produces the desired sounds (e.g. pronouncing an unfamiliar word),
instead of recognizing them. Not only
does the student make the sound; the student makes the effort to make the
sound. By contrast, in reading software
that does not listen, students may be restricted to receptive activities such
as matching up rhyming words, or to constructive activities that redirect what
would normally be speech into some other medium, such as putting blocks
together to make words.
Automatic speech recognition (Huang 1993),
while it has appeared in other language-related educational systems (such as
single-word foreign language pronunciation training, and speech pathology
software), is still a rarity in reading software (but see Edmark 1997). DRA Malvern has developed a system called
STAR (Speech Training Aid) that listens to isolated words without context
(Russell 1996). Russell et al. (1996)
also describe an ongoing research effort with aims similar to those of Project
LISTEN, the Talking and Listening Book project, but they use word spotting
techniques to listen for a single word at a time. They also require the child to decide when to
move on to the next word (fully user-initiated) or completely reserve that
choice to the system (fully system-initiated).
For other systems using speech recognition with reading tutoring, see
(Edmark 1997, IBM 1998).
Research Context: Project LISTEN
ProjectLISTEN: A Reading Tutor that Listens. Project
LISTEN’s Reading Tutor (Mostow and Aist AAAI 1997, Mostow et al. 1995, Mostow
et al. 1994, Mostow et al. 1993) adapts the Sphinx-II speech recognizer (Huang
et al. 1993) to listen to children read aloud.
The Reading Tutor runs on a single stand-alone Pentium™. The child uses a noise-cancelling headset or
handset microphone and a mouse, but not a keyboard. Roughly speaking, the Reading Tutor displays
a sentence, listens to the child read it, provides help in response to requests
or on its own initiative based on student performance. (Aist 1997) describes how the Reading Tutor
decides when to go on to the next sentence.
The student can read a word aloud, read a
sentence aloud, or read part of a sentence aloud. The student can click on a word to get help
on it. The student can click on Back to
move to the previous sentence, Help to request help on the sentence, or Go to
move to the next sentence (Figure 1).
The student can click on Story to pick a different story, or on Goodbye
to log out.
The Reading Tutor can choose from several
communicative actions, involving digitized and synthesized speech, graphics,
and navigation (Aist and Mostow 1997).
The Reading Tutor can provide help on a word (e.g. by speaking the
word), provide help on a sentence (e.g. by reading it aloud), backchannel
(“mm-hmm”), provide just-in-time help on using the system, and navigate (e.g.
go on to the next sentence). With speech
awareness central to its design, interaction can be natural, compelling, and
effective (Mostow and Aist WPUI 1997).
Writing. The Reading Tutor has the capability, so far used mostly in the
laboratory, to allow new stories to be typed in and then recorded with
automatic quality control on the recordings using automatic speech recognition.
Taking
turns. People
in general exhibit a rich variety of turn-taking behavior: interruption,
backchanneling, and multiple turns (Ayres et al. 1994, Duncan 1972, Sacks,
Schegloff, and Jefferson 1974, Tannen 1984, Uljin 1995). Turn-taking is important in tutorial dialog
as well (Fox 1993). Humans use different
conversational styles when speaking to computers than when speaking to humans.
Shorter sentences, a smaller vocabulary, fewer exchanges, fewer interruptions,
and fewer justifications of requests are all characteristic of human
conversational style during human-computer spoken dialogue, but it is not clear
whether the difference is due to the (supposed or actual) identity of the
interlocutor, or the interlocutor's conversational style (Johnstone 1994). Apparently when computers behave similarly to
humans, human-computer interaction more closely resembles human-human
interaction (Johnstone 1994).
In general, spoken language systems follow
strict turn-taking behavior, and even Wizard of Oz studies tend to use a simple
“my turn”—“your turn” approach to low-level discourse behavior in terms of
their generation abilities (Johnstone 1994).
The Reading Tutor, however, employs a conversational architecture (Aist
1998) that allows interruption by either the Tutor or the student, overlap,
backchanneling, and multiple turn-taking (cf. Donaldson and Cohen 1997, Keim,
Fulkerson, and Biermann 1997, Ward 1996, Ball 1997).
The
nature of feedback. In prior work, the Reading
Tutor has been designed to never tell the student she was right, and to never
tell the student she was wrong. Because
of error in the speech recognition,
explicit right/wrong feedback would be incorrect sometimes, which might
confuse the student. Therefore, the
Reading Tutor generally simply gives the correct answer and leaves the
right/wrong judgment partially as an exercise for the reader. This turns out to match well with the
observation of Weber and Shake (1988) that teachers’ rejoinders to student
responses in comprehension discussions were most frequently null or involved
repetition of the student’s answer.
Giving the correct answer may seem like corrective feedback if the
student was right, and may seem like confirmation if the student was
wrong. This interpretation depends on
the ability of the student to contrast his or her answer with the answer given
by the system.
Visual
design. Throughout the visual design of the Reading
Tutor, we will continue to use buttons labeled with both text and pictures, for
maximum clarity and ease of use (King et al. 1996).
Skill:
Word Attack
Description.
For the first example skill, let us consider “word
attack”, or decoding skills. Here the
goal is to learn how to take unknown words and turn them into sound. There
are many stages in taking a word from printed symbol to speech:
1. Visual stimulus "cat"
2. Visual stimulus "c",
"a", "t"
3. Orthographic symbols 'c', 'a', 't'
4. Phonological representation: phonemes
/k/, /a/, /t/
5. Phoneme string /kat/
6. Sound of the word 'cat'
This skill concentrates on that
part of the reading process that transforms letters into sounds – the
orthographic to phonological mapping (#4 above). One reasonable skill model is thus a probabilistic
context-sensitive unidirectional grapheme-to-phoneme mappings, at various
levels of subword detail. There are several sources for
deriving the subword units: inferring them from English text, inferring them
from kids’ performance, or from the literature on subword components in
reading.
Human
tutoring strategies. What constitutes a Ssuccessful
human-human dialogs for teaching word
attack? In a study of 30 college student-elementary student tutoring dyads,
Juel (1996) analyzed videotaped interactions for successful tutoring
strategies.
Two activities were
found to be particularly important in successful dyads: (a) the use of texts
that gradually and repetitively introduced both high-frequency vocabulary and
words with common spelling patterns, and (b) activities in which children were
engaged in direct letter-sound instruction. Two forms of verbal interactions
were found to be particularly important: (a) scaffolding of reading and
writing, and (b) modeling of how to read and spell unknown words. A synergistic
relationship was found to exist between the form and content of instruction.
(Juel
1996).
In this study, direct
letter sound instruction included making index cards with words on them
together. Most of the children’s responses in cited dialogue are words that are
answers to tutor's questions. Juel also
notes that successful pairs share "affection, bonding, and
reinforcement.Bby
sounding out real words, kids get to practice decoding rules in the contexts in
which they are used. Perhaps, Bby
providing scaffolding, human tutors keep kids from mislearning rules and
provide correct examples of decoding.
Computer-human dialog. What would a computer-human
dialog that successfully taught word attack skills look like? One possibility is to adapt the current
Reading Tutor sentence-reading dialog to a word-list reading dialog. Using information
about what rules are used to pronounce
words, and using its records of student performance, the Reading Tutor would
select a grapheme-to-phoneme rule for the student to
practice. The Reading Tutor
would present a list of words that involved a
specified rule. The student would then read each of these
words, with the Reading Tutor focusing on modeling correct sounding-out of the
words to reinforce the particular rule. By placing the correct stimulus (the words)
on the screen, we would hope to reduce off-task or
non-reading speech, and thus make the speech recognition task feasible. What kind of scaffolding might the Reading
Tutor provide during this task? The Reading Tutor already provides help such as
sounding out words and providing rhyming words.
In the future, the Reading Tutor might employ other help, such as
orthographic units slightly apart as a subtle aid to visually grouping letters
together: “team” might get redisplayed briefly as “t ea m”. Another possibility is for the Reading Tutor
to dynamically construct short bits of text for the student to read that, when
read, get the student to “practice” sounding out a word: “t ea
m. t ea m. team.”
Evaluation.
How would such a
dialog be evaluated? We could test the
effectiveness of practice on an individual rule (say, d ® /d/) by looking
for improvements on real words or pseudo-words with that mapping. For example, students could be presented with
two pseudo-words to read, where
the items are
identical except that one contains the rule used during
training and the other contains a different rule. Besides pedagogical effectiveness, the dialog
must also be understandable to students and fun enough to
get them to participate.
ExpectedContribution.
Successful design of a spoken dialog to teach word attack skills would
be an important achievement in the field of reading education. In addition, such a dialog is expected to
raise important questions about integrating intelligent tutoring systems with
speech recognition, two fields that are ripe for combination.
Skill:
Word Comprehension
Description. Once a word has been decoded (or recognized, if it is a familiar
word), a student must be able to access the meaning of the word in order to understand
the sentence the word is in. What is the
goal of training word comprehension skills? Essentially, vocabulary growth –
kids should learn the meaning, spelling, pronunciation, and usage of new words.
Human
tutoring strategies. What are techniques that work
when human tutors help students learn new words? For beginning readers, many
words may be learned through written context, by having stories read and
re-read to them (Eller et al. 1988). In order for children to encounter many new
words, however, they may need to read material hard enough to traditionally be
considered at their frustration level (Carver 1994) – and children may not
choose material this difficult on their own, either in traditional free reading
time or with computerized instruction.
Human tutors also introduce and explain
new vocabulary, and help students practice spellings of words. When should definitions or other word
specific comprehension assistance be presented?
For high school readers, Memory (1990) suggests that the time of
instruction (before, during, or after the reading passage) for teaching
technical vocabulary may not matter as much as the manner of instruction. The implication is that the Reading Tutor may
be able to choose when to present a definition or other word comprehension help
at several different times without substantially harming the student’s ability
to learn from the assistance.
What kind of word-specific comprehension
should be given? Definitions, in particular context-specific definitions, are
one obvious candidate. What are some
other options? Example sentences may be
of some help (Scott and Nagy 1997), but learning new words from definitions is
still very hard even with example sentences.
Which words should be explicitly
taught? Zechmeister et al. (1995)
suggests that explicit vocabulary instruction be focused on functionally
important words, which they operationalize as main entries in a medium-sized
dictionary.
Human-computer
dialog. Here a direct approach to generating a human-computer
dialog that captures the essence of human tutorial strategies – such as
constructing a dialog where the Reading Tutor interactively explains the
meaning of new words, or augmenting stories with specially written,
context-sensitive definitions – results in excessive requirements for
curriculum design or is beyond the state of the art in spoken dialog.
How can we design an alternate interaction
that places fewer requirements on instructional content and speech
recognition? One possibility is to adapt
the Writing capability of the Reading Tutor to allow kids to build up a “My
Words” portfolio that serves as an
explicit record of the student’s growing vocabulary. The trick here is to try
to get the classroom teacher involved in the process by commenting on, and
indirectly supervising, the student’s production of this
portfolio. Words could be selected for
inclusion in the My Words portfolio when the student encountered them in
stories read with the Reading Tutor. In order to get students to encounter new
words, the Tutor
could influence or partly control the student’s choice of stories to read in
order to steer the student towards harder material.
Part of adding a word
to “My Words” might include practice sessions for spelling, , or
reading sentences that contained that word drawn from the stories used by the
Reading Tutor.
Should the computer or the student take the
initiative in selecting word specific comprehension assistance? A study by
Reinking and Rickman (1990) indicated that mandatory computer presentation of
context specific definitions was better than offline access. Students selected computer presentation was
also better than offline access, and mandatory computer presentation was better
than student selected computer presentation but not significantly. Perhaps a mixed-initiative strategy, where
the Reading Tutor selects extra comprehension activities for some
"hard" words but students can choose to pursue them for other words
as well, would allow students to feel in control while ensuring some additional
comprehension assistance.
Evaluation. How would such a
portfolio-based interaction be evaluated?
s
As kids read stories, the Reading Tutor
would seek to identify new, unusual, or difficult words. Some percentage of those words are might
be selected for "my words"; the rest
arewould
be used as control condition.
BStudents would build
up a "m“My
wWords"”
list by reading words & and drawing
pictures of them. (UseThe
resulting objects could be used later in illustrating storiesions).
Kids For
each word in “My Words”, students would write definitions and
sample sentences for the words - expository writing, to harness kids'
communicative intent in order to "get words right". The underlying message here is that
learning new words is important because they help you read and write
interesting stories – and students should learn the importance of new words
through experience.
How could such an interaction be
evaluated? One method would be to
determine if students are better at recognizing and understanding the words
they've put into “My Words”, as compared to other words that were in the
stories they read, but were not selected for “my words”. Several factors make evaluation of this sort
of interaction difficult, however. Even
if spending time on “My Words” led to improvement in recognizing and
understanding the words it contained, it might still be the case that that time
might have been better spent reading (different, and challenging) stories. The evaluation for this interaction will have
to address these issues.
Expected
Contribution.
A successful human-computer dialog for teaching word comprehension as
described above would “close the gap” between reading, writing, and
illustrating in the Reading Tutor, allowing students to participate in several
literacy activities in a coherent instructional situation. One expected technical contribution is the
development of automated techniques for the Reading Tutor to pick out difficult
words – from any material -- for students to spend more time on, thus extending
comprehension assistance to student-authored material. In addition, the
presence of “My Words” as a writing (and therefore narrating) exercise would
bring the “authoring” capability of the Reading Tutor to use in real classrooms
by students. Another contribution would
be the use of speech recognition – constrained to be less difficult than
full-scale dictation – in a constructive learning activity, another bridge
between spoken dialog systems and instructional tutoring systems.
Skill:
Passage Comprehension
Description. Being
able to read and understand individual words is not by itself sufficient. More is needed to be a skilled reader. What is the goal of teaching passage
comprehension? Reading with understanding
– making meaning out of print.
Human tutoring strategies. While a student
is reading, human tutors supply words in response to pauses, interrupt students
to engage in phonologically motivated interventions, complete students' words,
and provide backchannel feedback (e.g. Oops!) (Roller 1994). One of the
simplest strategies for improved comprehension is rereading. Reading a text twice increases retention of
facts (Barnett and Seefeldt 1989).
Other skills play a role in passage
comprehension. For example, word
recognition is a good predictor of reading comprehension, especially for poor
readers (Ehrlich et al. 1993). One skill of good readers is adjusting reading
rate to passage difficulty (Freese 1997). Providing prior information about
relevant subject matter may improve comprehension of text about unfamiliar
topics (Stahl and Jacobson 1986).
However, improving comprehension on a particular passage is not
necessarily the same as helping the student learn how to comprehend other
passages better.
What about pictures as an aid to
comprehension? Pictures have appeal for
young students, but may not teach skills that are transferable to text without
pictures. For pictures to improve
passage comprehension, O’Keefe and Solman (1987) suggest that pictures must be
displayed simultaneously with the text they depict, rather than prior to
reading the text or after reading the text.
Human-computerdialog. Human tutors teach comprehension in a
variety of ways, but the current Reading Tutor interaction is based on
one-on-one oral reading tutoring. The
resulting dialog is called “shared reading”, where the student reads wherever
possible and the computer reads wherever necessary. How can shared reading be improved? The current Reading Tutor allows kids free
choice of all material on the system.
Some kids make what appear to be poor choices -- reading material that's
too easy, or reading the same story over and over again. During a 1996-1997 pilot study, six
third-grade children who started out below grade level gained almost two years
in fluency in only eight months of Reading Tutor use (Mostow and Aist WPUI
1997). The aide (Ms. Brooks) who helped
kids pick stories most likely played a role in the observed two-year fluency
gain. In order to improve the shared
reading process, perhaps the Reading Tutor should do better at helping kids
pick appropriate material. One possibility here are "curriculum
adjustment" tools, where the teacher or some administrator gets to adjust
an individual kid's reading list.
However, we have found it difficult to involve teachers in directly using
the Reading Tutor for such things as entering student data; they seem to prefer
interacting with the Reading Tutor indirectly.
For example, one teacher put a list of stories to read on an 3x5 index
card and set the card on top of the Reading Tutor monitor. So, an alternate approach is to have the Reading
Tutor “filter” or “sort” the list of stories that students can access to
restrict or guide students to appropriate material. This approach would have to be balanced to
allow the teacher to “indirectly” use the computer through influencing her
students’ choices, if desired.
Evaluation.
How should a revised story selection scheme be
evaluated? Perhaps the most basic
question is whether kids will tolerate the interaction design that lets the
Tutor affect story choice. In terms of
pedagogical evaluation, the question of appropriate material has at least two
subcomponents: is the story of the right difficulty? and, is the story
interesting for that kid at that moment?)
In addition, since teachers may affect student choice of stories,
teacher choices may affect the results of evaluation of Tutor-mediated story
selection. One possibility is to
separate out good story choices from poor choices, based on records of kids'
performance. Note that this is a tricky
proposition. What should we assume makes
a “good” choice or a “poor” choice? An acceptable range of fluency on that
story? Should we seek an expert
consensus on which stories are obviously too hard or obviously too easy? If so, by watching videotapes of the student
reading the story, or by prediction from the material? One exciting alternate
possibility is to test the goodness of the story choice based on the actual
learning from that story. It is an open
question as to whether or not we can detect learning at that fine of a level.
ExpectedContribution. By modifying the Reading Tutor to
better guide the story choices kids make, we would hope to improve the overall
effectiveness of the Reading Tutor. In
addition, using the results of speech recognition – in the form of a student
model – to guide students’ selection of stories from an open-ended set of
material forges an important link between automatic speech recognition and
intelligent tutoring systems.
Summary
We propose to enhance Project LISTEN’s
Reading Tutor by developing skill-specific human-computer spoken dialogs that
train elementary students in fundamental reading skills. Reading is fundamental, and the time is ripe
in intelligent tutoring systems and in spoken dialog systems to address
beginning reading using techniques from both fields and using the issues that
arise to build bridges between the two fields.
The major expected scientific contributions are an improved Reading
Tutor and a better understanding of how spoken language technology can be used
with intelligent tutoring systems. We
have briefly described relevant research both in reading and in automated
reading tutoring. We have described the
work to date on Project LISTEN’s Reading Tutor, which will also serve as the
platform for the proposed work. We have
described three example skills to train: word attack, word comprehension, and
passage comprehension. For each skill we
have discussed how human tutors train that skill, how a human-computer spoken
dialog might train that skill, and how such a dialog might be evaluated. Finally we will conclude with a projected
timeline for completion.
Proposed
Schedule
August 1998: Proposal
(September 1998 - December 1998: visiting
Macquarie University, Sydney, Australia)
January 1999 - May 1999: Collect and
analyze videotapes and transcripts of successful human-human dialogs, perhaps
by courtesy of other researchers; collect examples and descriptions of
successful human tutoring strategies from the literature
June 1999 - December 1999: Design and
implement computer-human dialogs that capture the important characteristics of
successful human-human tutoring dialogs
January 2000 - May 2000: Usability testing
and code hardening
June 2000 - May 2001: Evaluation and
writeup
May 2001: Defense.
Acknowledgments
This material is based upon work supported in part by the National Science Foundation under Grant No. IRI-9505156 and CDA-9616546 and by the author's National Science Foundation Graduate Fellowship and Harvey Fellowship. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the official policies, either expressed or implied, of the sponsors or of the United States Government.
We thank first our committee members; the Principal of Fort Pitt
Elementary School, Dr. Gayle Griffin, and the teachers at Fort Pitt for their
assistance; Drs. Rollanda O'Connor and Leslie Thyberg for their expertise on
reading; Raj Reddy and the CMU Speech Group (especially Ravi Mosur) for the
Sphinx-II speech recognizer; and the many present and past members of Project
LISTEN whose work contributed to current and previous versions of the Reading
Tutor; and many students, educators, and parents for tests of the Reading Tutor
tests in our lab and at Fort Pitt Elementary School.
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