A Tutor for Teaching English as a Second Language for Deaf Users of
American Sign Language

Kathleen F. McCoy and Lisa N. Masterman CIS Department University of
Delaware Newark, DE 19716 mccoy@cis.udel.edu and masterma@cis.udel.edu
 
Abstract 

In this paper we introduce a computerassisted writing tool for deaf
users of American Sign Language (ASL). The novel aspect of this
system (under development) is that it views the task faced by these
writers as one of second language acquisition. We indicate how this
affects the system design and the system's correction and explanation
strategies, and present our methodology for modeling the second
language acquisition process.
 
1 Introduction
 
This paper briefly overviews a project whose longterm goal is the
development of a "writing tutor" for deaf people who use American
Sign Language (ASL). We wish to address the particular difficulties
faced by the deaf writer learning English and to create a system with
the capabilities of accepting input via an essay written by a user
(possibly several paragraphs in length), analyzing that essay for
errors, and then engaging the user in tutorial dialogue aimed toward
improving his/her overall literacy. The goal is a system designed to
be used over an extended period of time, with the capacity to model
the student's state of language proficiency and changes in that
proficiency. The tutoring provided by the system would then be
handtailored toward the individual user and his/her level of
acquisition of written English.

Such a system must have several components.  First, it must have the
ability to analyze the input texts and determine what errors have
occurred. It must then be able to select which of these errors to
discuss with the learner, and in what order to discuss them. Finally,
it must be able to generate appropriate corrective tutorial messages
concerning the errors, keeping in mind both the goal of correcting
this sample text and the larger objective of improving the overall
literary of the student.  Concurrent with these explicit components,
the system must be capable of constructing and updating a user model
to be consulted in both the selection of errors to be corrected and
the generation of corrective text. This user model would take into
account a theory of second language acquisition which regards the
process as a systematic revision of an internalized concept of the
language to be acquired.  Students would be placed within a model of
climbing literacy, with language concepts rated as above, below, or
within their current realm of acquisition, and the tutorial
interaction tailored to this model.

In this paper, after motivating our specific application, we
introduce the architecture of our eventual system and motivate its
various components.  After describing our current implementation
status, we motivate the need for a model of second language
acquisition. We finish with describing how we propose to model this
process.

2 Literacy Issues for People Who are Deaf 

The problem of deaf literacy has been welldocumented and has far
reaching effects on every aspect of deaf students' education. Though
data on writing skills is difficult to obtain, we note that the
reading comprehension level of deaf students is considerable lower
than that of their hearing counterparts, "...with about half of the
population of deaf 18yearolds reading at or below a fourth grade
level and only about 10% reading above the eighth grade
level..."(Strong, 1988)

Some Deaf people use American Sign Language (ASL). ASL is a
visualgestural language whose grammar is distinct and independent of
the grammar of English or any other spoken language (Stokoe, Jr.,
1960), (Baker and Padden, 1978), (Baker and Cokely, 1980),
(Hoffmeister and Shettle, 1983), (Klima and Bellugi, 1979), (Bellman,
Poizner, and Bellugi, 1983). The structure of ASL is radically
different from that of English, being much more similar to that of
Chinese or the Native American language Navaho. In addition to sign
order rules (which are similar to word order rules of English), ASL
syntax includes systematic modulations to signs as well as nonmanual
behavior (e.g., squinting, raising of eyebrows, body shifts, and
shaking, nodding or tilting the head) for morphological and
grammatical purposes (Baker and Cokely, 1980), (Liddell, 1980),
(Padden, 1981), (Klima and Bellugi, 1979), (Kegl and Gee, 1983),
(Ingram, 1978), (Baker, 1980). The modality of ASL encourages
simultaneous communication of information which is not possible with
the completely sequential nature of written English.
 
In addition to radical differences in the structure of ASL and
English, another obstacle to the ASL user acquiring English is the
unique processing strategies he brings to the task (Anderson, 1993).
The cognitive elements used to store signs in short-term memory are
distinctively different from those used with a spoken/written
language. Also, hearers of spoken language buffer the speech in
order to process it together in words and phrases, but the buffer for
visually observed data has a much quicker decay time than that of
auditory or visual data, which leads to repetition and redundancy in
signed languages that does not occur in the same manner
elsewhere. Moreover, long, involved utterances of a manual language
are parcelled into small parts that are recursively reinforced,
referring back to previous details as each new piece of information is
added, another charactertistic atypical of spoken language.

Adding to these difficulties is the fact that ASL has no accepted
written form, eliminating the opportunity to establish literacy
skills in a fluent native language and then transfer those skills to
the new language being learned. Perhaps the worst difficulty for the
deaf learner is that he has little to no understandable input in the
language he is attempting to acquire. Thus, in addition to providing
feedback on the student's writing, a tutoring system should be capable
of offering sample understandable input using constructions that the
student is currently attempting to master.

We anticipate that our system will address the unique needs of the
deaf population in other ways as well. For instance, this system would
provide the user with feedback on his or her writing without involving
a human teacher. Some students might prefer this mode of feedback
since they would not risk feeling a "loss of face" as they might with
a human tutor. The hope is that this will get the students to write
more.

In explaining the difficulties faced by the deaf learner of English,
we do not propose that ASL natives are fundamentally different from
other learners of English as a Second Language; rather, we want to
stress the view that English is, for ASL natives, a fundamentally
different and challenging language, motivating the need to adopt a
Second Langauge Acquisition strategy toward facilitating the learning
process. There exist many obstacles to this process, some which are
shared with other native language populations and some which are
unique, such as the absence of the opportunity to have English input
tailored to the personal level of acquisition and understanding of
the learner. The system we propose attempts to address these needs as
closely as possible within its own constraints (i.e., without the
ability to converse with the learner in his native language).

We should note that while there are "style checkers" and "grammar
checkers" on the market, these programs do not satisfy the needs of
the deaf. Educators of the deaf (and other people working with deaf
individuals) report that such checkers, geared toward the errors of
hearing writers, frustrate deaf students. Tailored toward the writing
style of fluent, native English speakers, they do not catch many
errors that are common in the writing of people who are deaf, and, at
the same time, they flag many constructions that are not errors. We
ran some of our writing samples from deaf subjects through a few
grammar checkers, and we judged the results to be consistent with
these reports.

3 Overview of System Design 

Figure 1 contains a block diagram of the system under
development. The system, called ICICLE (Interactive Computer
Identification and Correction of Language Errors), is designed to be a
general purpose language learning tutor. While our current focus
is on users of ASL, and thus some of the modules will be specific to
the errors and difficulties of this learner population, our eventual
goal is to have the languagespecific aspects of the system to be
excisable, allowing modules for different native languages to be
inserted, so the system would eventually be usable for any learner
of English as a Second Language.
 

Figure 1: ICICLE Overall System Design 

The input/feedback cycle of ICICLE begins when the user enters a
portion of text into the computer.  The user's text is processed by
the Error Analysis component which is responsible for tagging all
errors. This component first performs a syntactic parse of a
sentence using an English grammar augmented with errorproduction
rules, or malrules (Sleeman, 1982), (Weischedel, Voge, and James,
1978). These malrules allow sentences containing errors to be parsed
with the grammar, and enable the system to flag errors when they
occur. The mal-rules themselves are derived from an error taxonomy
which resulted from our writing sample analysis in conjunction with an
analysis of how ASL knowledge might influence written English and
other ASL information. The initial taxonomy was developed from an
analysis of fortyeight Freshman and Sophomore writing evaluation
samples from Gallaudet University (a liberal arts university for the
deaf), seventeen writing evaluation samples from the National
Technical Institue for the Deaf (NTID, a deaf school in Delaware), and
five letters and essays written by ASL natives and collected through
the Bicultural Center in Washington, DC. In total, the samples
represent about 25,000 words. The errors were hand-counted and
categorized, leading to the development of the malrules which
represent them.

The possible effects of ASL on the errors identified are captured in
the Language Model. The effects from the acquisition of English as a
Second Language are captured in the Acquisition Model (described
later in this paper). These two models affect a scoring mechanism
which is used to identify a single parse (and set of errors) when
multiple possibilities exist (McCoy, Pennington, and Suri, 1996).

The error identification phase must also look for semantic errors
(e.g., mixing of have and be), and for discourse level errors (e.g.,
NP deletions). Some of these errors will be flagged after syntactic
parsing using independent error rules. Finally, the Error
Identification module is responsible for updating any discourse
information tracked by the system (e.g., focus information). Once
this information is recorded, the next sentence will be analyzed.

After all analyses are completed, the text, along with the error
results and annotations from the error rules, will be passed to the
Response Generator.  The Generator component processes this
information (along with data from the User Model and possibly the
History Module) in order to decide which errors to correct in detail
and how each should be corrected (including what language level should
be used in generating any required instruction). The decision as to
which errors to correct in detail will be most influenced by reasoning
on the Acquisition Model.

The second decision that must be made in the Response Generator is
which kind of correction strategy to use in actually generating the
response. This decision is also affected by information stored in the
User Model and History Module. The content of the response itself will
be derived from the annotations on the errors that were passed from
the Error Analysis component; additional content for the responses
may be provided by the ASL/English "Expert" (Language Model) and
influenced by the Acquisition Model. Finally, the responses will be
displayed to the user who then has an opportunity to enter
corrections to the text and have it rechecked.  At the same time,
information from the Response Generator will be used to update the
recent and longterm "history" of the user. This knowledge can then
be utilized to assess the user's secondlanguage ability and other
user characteristics, and to evaluate the success (or failure) of
the correction techniques employed thus far.

4 Implementation 

Our implementation to this point has concentrated most heavily on the
analysis phase of processing. The user interacts with the system
through a windowsbased interface (We thank Robert Jeffrey Morriss for
his work on the interface design and implementation.  ) through which
text may either be entered directly or loaded from a file. Once the
text is loaded, the user may ask that it be analyzed by the system.

The text is analyzed (one sentence at a time) by a bottomup parser
found in (Allen, 1995) using a grammar which has been augmented with
malrules to capture errors uncovered in our analysis of writing
samples. (We thank Xingong Chang and David Schneider for their work on
the grammar and Linda Suri for the writing sample analysis and
development of the error taxonomy.) The malrules are indexed with the
errors that they realize.

The following is an example of a malrule from the grammar currently
in implementation:

((s (inv ) (errorfeature +) (wh ?w)) 
(np (case sub) (wh ?w) (agr p) ) 
(head (vp (vform (? v pres past)) (agr s) (person 3)))) 

This rule would recognize an error at the sentence formation level,
in subjectverb agreement-specifically, an error where the subject
is plural but the verb form is thirdperson singular, such as "We
does..." or "They has..." By tagging the sentence parse with the
feature (errorfeature +), it is identified as containing an error,
and the parse tree can be examined to discover the malrule (in this
case, mv01.2) that was used in the parse.

After all of the sentences have been parsed in this way, the current
system displays the text with colored highlighting over all
errorcontaining sentences (different colors are used for different
classes of error, again as identified from the malrules which were
used). In addition, a colorcoded menu appears which names the
errors and associates them with the colors from the highlighted
display. At this point the user may investigate the individual errors
further. For example, s/he may click on a particular error name to get
a (currently canned) explanation, or s/he may ask the system to mark
the occurrences of a particular error only. In addition, the user may
edit particular sentences, which results in an immediate new
analysis of the text.

5 Accounting for the L2 Acquisition Process

There are several reasons why a model of second language acquisition
is necessary.

5.1 Identifying Errors 

It is common for our system to find multiple possible parses of an
input string, where some parses may contain malrules and others do
not, some may contain different malrules than others, etc. Deciding
between these multiple parses corresponds to deciding which errors
(if any) the student made in the given sentence. One area of our
current work concerns progress toward making an informed choice
about which parse tree best represents the student's input.

Our method is to develop a model of second language acquisition and
use it for this task. For example, if we had a model of what the
student had already acquired, what the student was currently
acquiring, and what the student was most likely to acquire next, this
could be used to select the most likely parse of the sentence in a
principled fashion.  A student is most likely to make errors in
constructions s/he is currently acquiring (Vygotsky, 1986).  Thus,
given a set of parses, the one that is most likely to best describe
the input is the one that contains malrules corresponding to errors
in that realm of constructions (and that does not use constructions
well beyond the student's current acquisition level).

5.2 Focusing the Correction 

Once errors have been detected, the system must determine:

- which errors to focus on in the correction

- what basic content to include in the corrective response

Our model of second language acquisition is crucial for these tasks
as well. Research in second language acquisition and education
indicates that as a learner is mastering a subject, there is a certain
subset of the material that is currently "within his grasp." This
has been called the Zone of Proximal Development (ZPD) by Vygotsky
(Vygotsky, 1986).  This general idea has been applied to assessment
and writing instruction by (Rueda, 1990), and second language
acquisition by (Krashen, 1981). Intuitively the knowledge or
concepts within the ZPD are "currently being acquired".

According to the above literature, instruction and corrective feedback
dealing with aspects within the ZPD may be beneficial; instruction or
corrective feedback dealing with aspects outside of the ZPD will
likely have little effect and may even be harmful to the learning
process, either boring or confusing the student with informtion he is
unable to comprehend or apply. Thus the correction should focus on
features at or slightly above the student's level of acquisition.

Once an error has been identified and chosen for a corrective
response, the system must also decide on the content of that
response. Here again, where the user is in the acquisition process
(and thus, why he made the error) is crucial. Consider the following
example found in one of our writing samples:
 
"My brother like to go..." 

This sentence appears to most of us to have a problem in subjectverb
agreement. Because the subject is thirdperson singular, the present
tense verb should be "likes." Notice that there are several reasons
why this error may be generated:

1. The student doesn't know that such agreement exists in the
language. That is, the student may be unaware that the form of the
subject has anything to do with the form of the verb in such
sentences.

2. The student is mistaken about the syntactic form the agreement
takes. In this case, the student is aware that s/he needs to mark
subject-verb agreement, but does not know how to do so (or believes
that s/he has already done so).

3. The student intended the noun to be in plural form (but mistyped).

4. The student intended the verb to be in singular form (but
mistyped).

Notice that very different kinds of content would be required to
effectively correct the above error depending on the actual reason
for making it. In the first case, some general tutoring should be
given, explaining that agreement exists in the language, the
circumstances in which the agreement needs to be marked, and the form
the agreement should take.  In case 2, only the form of the agreement
needs to be explained. In cases 3 and 4, no tutoring should be given.

Knowing where the student is in acquiring the second language can
help a system distinguish among the cases above. If subjectverb
agreement is something that the student has not acquired and is not
about to acquire, case 1 is most likely. The student's placement in
the model of acquisition can further direct our decisions regarding
actions, because if this agreement is too far above the student's
current level to be intellectually attainable at this time, we do not
want to act on the error at all. If, on the other hand, it is
currently within the ZPD ) i.e., currently being acquired by the
user), then case 2 is the most likely situation. Finally, either case
3 or 4 is likely if subjectverb agreement has already been acquired
by the user.

5.3 Modeling the L2 Acquisition Process 

We are currently developing a computational model that captures the
way that English is acquired (as a second language) and gives us a
framework upon which to project a student's "location" in that
process. There is considerable linguistic evidence that the
acquisition order of English features for second-language learners is
relatively consistent and fixed regardless of the first language
(Ingram, 1989), (Dulay and Burt, 1974), (Bailey, Madden, and Krashen,
1974). In addition to studies concentrating on second language
acquisition, research in language assessment and educational grade
expectations (e.g., (Berent, 1988), (Lee, 1974), (Crystal, 1982)) also
suggests that language features are acquired in a relatively fixed
order. This research outlines sets of syntactic constructions
(language features) that students are generally expected to master by
a certain point in their study of the language. This work can be
interpreted as specifying groups of features that should be acquired
at roughly the same time.

Figure 2: Language Complexity in SLALOM 

We have attempted to account for the preceding results in a language
assessment model called SLALOM ("Steps of Language Acquisition in a
Layered Organization Model") (The initial work on SLALOM was done by
Christopher A. Pennington.  ) . The basic idea of SLALOM is to divide
the English language (the L2 in our case) into a set of feature
hierarchies (e.g., morphology, types of noun phrases, and types of
relative clauses). Within any single hierarchy, the features are
ordered according to their "difficulty" of acquisition, reflecting
their relative linguistic complexity. The ordering within feature
hierarchies has been the subject of investigation in work such as
(Ingram, 1989), (Dulay and Burt, 1974), and (Bailey, Madden, and
Krashen, 1974).

Figure 2 contains an illustration of a piece of SLALOM. We have
depicted parts of four hierarchies in the figure: morphological
syntactic features, noun phrases, verb complements, and various
relative clauses. Within each hierarchy, the intention is to capture
an ordering on the feature acquisition.  So, for example, the model
reflects the fact that the +ing progressive form of verbs is generally
acquired before the +s plural form of nouns, which is generally
acquired before the +s form of possessives, etc.
 
Notice that there are also relationships among the hierarchies. This
is intended to capture sets of features which are acquired at
approximately the same time. These connections may be derived from
work in language assessment and grade expectations such as found in
(Berent, 1988), (Lee, 1974), and (Crystal, 1982). The figure
indicates that while the +s plural ending is being acquired, so too
are both proper and regular nouns, and one and twoword
sentences. While a learner is acquiring these features, we do not
expect to see any relative clauses which are beyond that level of
acquisition.

We anticipate that SLALOM, when fully developed, will initially
outline the typical steps in acquiring English as a second
language. This model will then be tailored to the needs of individual
students via a series of "filters," one for each user characteristic
that might alter the initial generic model.  For instance, it is
possible that the specific features of the student's Native Language
(L1) will affect the rate or order of acquisition of the Second
Language (L2). In particular, one would expect features shared in the
L1 and L2 to be acquired more quickly than those which are not (due to
positive language transfer). Another possible filter might reflect how
various formal writtenEnglish instruction programs might alter the
model, possibly stressing certain features normally acquired after
others which remain unmastered.

We are developing the initial language learning model and its filters
based on acquisition literature.  We expect to further solidify the
model using the writing samples that we have already collected. We are
currently performing statistical analysis on our growing body of
handcorrected samples to see what error classes cooccur with
statistical significance.  We also expect to seek input from English
teachers of deaf students, to see how they rank their students'
abilities based on assignments they correct.
 
Once the SLALOM model is complete, we expect to rely on user
modeling techniques to "place" the user within this model. This
placement must be more sophisticated than simply looking at errors
since some learners will avoid structures they do not know perfectly
well in order to prevent error. Others will make heavy use of
prefabricated patterns, such as the "tourist phrases" found in a
travel book, whose use may precede a complete understanding of meaning
or structure. Thus the placement algorithm must take into account
both of these writing strategies.

6 Generating the Response 

Aside from content, the generated response should have several other
characteristics. In addition to providing examples of constructions
the user is currently acquiring (as discussed earlier) the response
should be organized so as to tie new knowledge into old knowledge thus
facilitating meaningful learning as discussed by (Brown, 1994). When
each new element is tied into alreadylearned data, and is presented
so that pieces of new knowledge indtroduced together are related
conceptually, the learning process gains a more significant meaning
and new material is assimilate more quickly and entirely.
 
In addition, responses should encourage both deductive and inductive
learning (where in the former, a standard practice for many foreign
language classrooms, the student is introduced to the rule and is
expected to use it to construct specific examples; in the latter the
student is not directly told the rule, but is encouraged to generalize
to the rule from specific correct examples). Classrooms benefit from
both forms, but the deaf learner has limited to no exposure to
correct forms, so responses that encourage inductive learning may be
particularly useful. We postulate that this technique may be best
achieved by providing positive examples from the student's own
work. We have investigated the possibility of doing a search on the
parse trees of correct sentences in the writing sample in order to
find those that most closely fit a desired template, perhaps based on
a sentence the learner has written incorrectly elsewhere.

The Response Generator should also take into account that feedback to
a language learner occurs at two levels, affective and cognitive
(Vigil and Oller, 1976). The cognitive level is that which concerns
the content of the feedback, or the part which addresses the intellect
of the learner and either enforces the assimilation of the concepts
involved, or tells the learner to retry his attempt at communication.
The affective level is less explicit, expressed through nonverbal cues
and tone of voice, addressing a less conscious aspect of the
learner. Negative feedback in this area should be avoided, as it may
result in an abortion of his attempts to communicate. Even when the
cognitive content of the response is indicating that an error
occurred, the affective feedback should always encourage the learner.

7 Conclusions 

It seems clear to us that the difficulties faced by deaf learners of
written English require the development of such a tool as the one we
envision. Direct, personalized interaction in a nonthreatening
(nonhuman) package, coupled with constructive input in the form of
specific example utterances that address issues the student is
currently learning, could go a long way toward bringing satisfactory
English literacy within reach of the deaf population. Moreover, its
general-purpose goals, stretching beyond this particular target
audience of users, could make it a very useful tool for any language
classroom.

8 Acknowledgments 

This work has been supported by NSF Grant # IRI-9416916 and by a
Rehabilitation Engineering Research Center Grant from the National
Institute on Disability and Rehabilitation Research of the U.S.
Department of Education (#H133E30010).
 
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