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 computer- 
assisted writing tool for deaf users of Amer- 
ican Sign Language (ASL). The novel as- 
pect 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 explana- 
tion strategies, and present our methodol- 
ogy for modeling the second language ac- 
quisition process. 
1 Introduction 
This paper briefly overviews a project whose long- 
term goal is the development of a "writing tutor" 
for deaf people who use American Sign Language 
(ASL). We wish to address the particular difficul- 
ties faced by the deaf writer learning English and 
to create a system with the capabilities of accept- 
ing 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 lit- 
eracy. 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 pro- 
vided by the system would then be hand-tailored 
toward the individual user and his/her level of ac- 
quisition 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 cor- 
recting 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 updat- 
ing a user model to be consulted in both the selec- 
tion of errors to be corrected and the generation of 
corrective text. This user model would take into ac- 
count 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 climb- 
ing 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 appli- 
cation, we introduce the architecture of our even- 
tual 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 pro- 
pose to model this process. 
2 Literacy Issues for People Who 
are Deaf 
The problem of deaf literacy has been well: 
documented 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 con- 
siderably lower than that of their hearing counter- 
parts, "...with about half of the population of deaf 
18-year-olds 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). 1 ASL is a visual-gestural language whose 
grammar is distinct and independent of the grammar 
1While we recognize that many people who are deaf 
or hard of hearing use other communication systems, our 
47 
/ 
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 dif- 
ferent 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 syn- 
tax includes systematic modulations to signs as well 
as non-manual behavior (e.g., squinting, raising of 
eyebrows, body shifts, and shaking, nodding or tilt- 
ing the head) for morphological and grammatical 
purposes (Baker and Cokely, 1980), (Liddell, 1980), 
(Padden, 1981), (Klima and Bellugi, 1979), (Kegl 
and Gee, 1983), (Ingrain, 1978), (Baker, 1980). The 
modality of ASL encourages simultaneous communi- 
cation of information which is not possible with the 
completely sequential nature of written English. 
In addition to radical differences in the struc- 
ture of ASL and English, another obstacle to the 
ASL user acquiring English is the unique processing 
strategies s/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, hear- 
ers 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 parceled into small parts that 
are recursively reinforced, referring back to previous 
details as each new piece of information is added, 
another characteristic atypical of spoken language. 
Adding to these difficulties is the fact that ASL 
has no accepted written form, eliminating the op- 
portunity to establish literacy skills in a fluent na- 
tive language and then transfer those skills to the 
new language being learned. Perhaps the worst dif- 
ficulty for the deaf learner is that s/he has little to 
no understandable input in the language s/he is at- 
tempting 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 cur- 
rently 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 
focus has been on those people who are (near native) 
users of American Sign Language. 
user with feedback on his or her writing without in- 
volving a human teacher. Some students might pre- 
fer 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 na- 
tives 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 Language 
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 un- 
derstanding 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 check- 
ers" and "grammar checkers" on the market, these 
programs do not satisfy the needs of the deaf. Ed- 
ucators 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 er- 
rors that are common in the writing of people who 
are deaf, and, at the same time, they flag many con- 
structions 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. 
There have been some attempts to develop "gram- 
mar checkers" for people who are deaf. Perhaps 
the most notable of these is the system named 
Ms. Pluralbelle which was developed and tested 
with students at Gallaudet University (Loritz, 1990), 
(Loritz, Parhizgar, and Zambrano, 1993). The work 
described here differs from this earlier work mainly 
in its emphasis on correction and on its model of the 
user's acquisition process. 
3 Overview of System Design 
Figure 1 contains a block diagram of the system un- 
der development. The system, called ICICLE (In- 
teractive Computer Identification and Correction of 
Language Errors), is designed to be a general pur- 
pose language learning tutor. While our current fo- 
cus is on users of ASL, and thus some of the modules 
48 
I USER MODEL \[ 
Figure 1: ICICLE Overall System Design 
will be specific to the errors and difficulties of this 
learner population, our eventual goal is to have the 
language-specific aspects of the system to be excis- 
able, allowing modules for different native languages 
to be inserted, so the system would eventually be us- 
able for any learner of English as a Second Language. 
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 Analy- 
sis component which is responsible for tagging all 
errors. This component first performs a syntac- 
tic parse of a sentence using an English grammar 
augmented with error-production rules, or mal-rules 
(Sleeman, 1982), (Weischedel, Voge, and James, 
1978). These mal-rules 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 infor- 
mation (Suri and McCoy, 1993). The initial taxon- 
omy was developed from an analysis of forty-eight 
Freshman and Sophomore writing evaluation sam- 
ples from Gallaudet University (a liberal arts uni- 
versity for the deaf), seventeen writing evaluation 
samples from the National Technical Institute for 
the Deaf (NTID, a deaf school in Delaware), and five 
letters and essays written by ASL natives and col- 
lected through the Biculturai Center in Washington, 
DC. In total, the samples represent about 25,000 
words. The errors were hand-counted and catego- 
rized, leading to the development of the mal-rules 
which represent them. 
The possible effects of ASL on the errors identi- 
fied are captured in the Language Model. The ef- 
fects from the acquisition of English as a Second 
Language are captured in the Acquisition Model (de- 
scribed later in this paper). These two models affect 
a scoring mechanism which is used to identify a sin- 
gle parse (and set of errors) when multiple possibil- 
ities 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 pars- 
ing using independent error rules. Finally, the Error 
Identification module is responsible for updating any 
discourse information tracked by the system (e.g., fo- 
cus 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 er- 
ror rules, will be passe d to the Response Generator. 
The Generator component processes this informa- 
tion (along with data from the User Model and pos- 
sibly 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 Re- 
sponse Generator is which kind of correction strat- 
egy 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 annota- 
tions on the errors that were passed from the Error 
Analysis component; additional content for the re- 
sponses may be provided by the ASL/English "Ex- 
pert" (Language Model) and influenced by the Ac- 
quisition Model. Finally, the responses will be dis- 
played to the user who then has an opportunity to 
enter corrections to the text and have it re-checked. 
At the same time, information from the Response 
Generator will be used to update the recent and 
long-term "history" of the user. This knowledge can 
then be utilized to assess the user's second-language 
ability and other user characteristics, and to eval- 
uate the success (or failure) of the correction tech- 
niques employed thus far. 
4 Implementation 
Our implementation to this point has concentrated 
most heavily on the analysis phase of process- 
ing. The user interacts with the system through 
49 
a windows-based interface 2 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 ana- 
lyzed by the system. 
The text is analyzed (one sentence at a time) by 
a bottom-up parser found in (Allen, 1995) using a 
grammar which has been augmented with mal-rules 
to capture errors uncovered in our analysis of writing 
samples. 3 The mal-rules are indexed with the errors 
that they realize. 
The following is an example of a mal-rule from the 
grammar currently in implementation: 
((s (inv-) 
(error-feature +) 
(wh ?w) ) 
-my01.2> 
(np (case sub) 
(wh ?w) 
(agr p) ) 
(head (vp (vform (? v pres past)) 
(agr s) 
(person 3)) ) ) 
The rule shown is a simple sentence rule that states 
that an s is an np followed by a vp (where the vp is 
the head). The left and right hand sides of the rule 
are delimited by the rule name (-my01.2> in this 
case), and each constituent has a set of features that 
are associated with it. This rule would recognize an 
error at the sentence formation level, in subject-verb 
agreement - specifically, an error where the subject 
is plural but the verb form is third-person singular, 
such as "We does..." or "They has..." By tagging 
the sentence parse with the feature (error-feature 
+), it is identified as containing an error, and the 
parse tree can be examined to discover the real-rule 
(in this case, my01.2) that was used in the parse. 
After all of the sentences have been parsed in this 
way, the current system displays the text with col- 
ored highlighting over all error-containing sentences 
(different colors are used for different classes of er- 
ror, again as identified from the real-rules which 
were used). In addition, a color-coded menu ap- 
pears 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 
2We thank Robert Jeffrey Morriss for his work on the 
interface design and implementation. 
3We 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. 
edit particular sentences, which results in an imme- 
diate 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 possi- 
ble parses of an input string, where some parses may 
contain mal-rules and others do not, some may con- 
tain different mal-rules than others, etc. Deciding 
between these multiple parses corresponds to decid- 
ing which errors (if any) the student made in the 
given sentence. One area of our current work con- 
cerns 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 lan- 
guage acquisition and use it for this task. For ex- 
ample, if we had a model of what the student had 
already acquired, what the student was currently ac- 
quiring, 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 construc- 
tions 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 con- 
tains mal-rules 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 cru- 
cial for these tasks as well. Research in second lan- 
guage 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 sec- 
ond language acquisition by (Krashen, 1981). Intu- 
itively the knowledge or concepts within the ZPD 
are "currently being acquired". 
50 
I 
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 information s/he is unable to com- 
prehend 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 s/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 subject-verb agreement. Because the 
subject is third-person 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 stu- 
dent 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 de- 
pending on the actual reason for making it. In the 
first case, some general tutoring should be given, ex- 
plaining that agreement exists in the language, the 
circumstances in which the agreement needs to be 
marked, and the iform 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 sec- 
ond language can help a system distinguish among 
the cases above. If subject-verb agreement is some- 
thing 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 di- 
rect 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 subject-verb 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 pro- 
cess. 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 (Ingrain, 1989), (Du- 
lay and Burt, 1974), (Bailey, Madden, and Krashen, 
1974). In addition to studies concentrating on sec- 
ond language acquisition, research in language as- 
sessment 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 stu- 
dents 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. 
We have attempted to account for the preced- 
ing results in a language assessment model called 
SLALOM ("Steps of Language Acquisition in a Lay- 
ered Organization Model") 4. 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 rel- 
ative clauses). Within any single hierarchy, the fea- 
tures are ordered according to their "difficulty" of 
acquisition, reflecting their relative linguistic com- 
plexity. The ordering within feature hierarchies has 
been the subject of investigation in work such as (In- 
gram, 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 hierar- 
chies in the figure: morphological syntactic features, 
noun phrases, verb complements, and various rela- 
tive clauses. Within each hierarchy, the intention 
4The initial work on SLALOM was done by Christo- 
pher A. Pennington. 
51 
Complex 
Simple 
SLALOM 
+s verb dctN SVOO ~"~ ~----~----- 
+s +ed plural past ~~_ +s poss { adj N S V 0 
i N SV ~ norel 
+ing prog pro~N S or v 
. i A B C D 
Feature Hierarchy 
Figure 2: Language Complexity in SLALOM 
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 gener- 
ally acquired before the +s form of possessives, etc. 
Notice that there are also relationships among the 
hierarchies. This is intended to capture sets of fea- 
tures 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 (Crys- 
tal, 1982). The figure indicates that while the +s 
plural ending is being acquired, so too are both 
proper and regular nouns, and one- and two-word 
sentences. While a learner is acquiring these fea- 
tures, we do not expect to see any relative clauses 
which are beyond that level of acquisition. 
We anticipate that SLALOM, when fully devel- 
oped, will initially outline the typical steps in ac- 
quiring English as a second language. This model 
will then be tailored to the needs of individual stu- 
dents via a series of "filters," one for each user char- 
acteristic 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 Lan- 
guage (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 written-English instruction programs 
might alter the model, possibly stressing certain fea- 
tures 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 hand-corrected samples to see what 
error classes co-occur 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 ex- 
pect 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. Oth- 
ers 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 algo- 
rithm 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 cur- 
rently 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 el- 
ement is tied into already-learned data, and is pre- 
sented so that pieces of new knowledge introduced 
together are related conceptually, the learning pro- 
cess gains a more significant meaning and new ma- 
terial is assimilate more quickly and entirely. 
In addition, responses should encourage both de- 
ductive and inductive learning (where in the former, 
a standard practice for many foreign language class- 
rooms, 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 ex- 
posure 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 else- 
where. 
The Response Generator should also take into ac- 
count that feedback to a language learner occurs at 
52 
two levels, affective and cognitive (Vigil and Oiler, 
1976). The cognitive level is that which concerns 
the content of the feedback, or the part which ad- 
dresses 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 indi- 
cating 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, person- 
alized interaction in a non-threatening (non-human) 
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 tar- 
get 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 Re- 
search Center Grant from the National Institute on 
Disability and Rehabilitation Research of the U.S. 
Department of Education (#H133E30010). 

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