Quiz1. What is the purpose of the SLALOM model?

(a) Generate errors

(b) Provide user feedback

(c) Captures user proficiency in grammatical structures

(d) The actual grammar parser

2. Name three levels of knowledge in the user model?

(a) Acquired, ZPD, Unacquired

(b) Previous, Current, Future

(c) Irregular, Standard, Knowledgeable

(d) Beginner, Proficient, Expert

Quiz3. Name the two modules in the ICICLE System.

(a) Tutoring Session Module and User Interface Module

(b) Error Identification Module and Response Generation Module

(c) User Module and System Module

(d) Gradual Learning Module and Frequency of Practicing Module

4. What is the purpose of the ICICLE system?

(a) Improving English literacy and ASL skills for all students

(b) Improving English literacy skills for hearing students

(c) Improving ASL skills for deaf students

(d) Improving English literacy skills in deaf students

An Intelligent Tutoring System for Deaf Learners of

Written English

Lisa N. Michaud, Kathleen F. McCoy, Christopher A. Pennington

Presented by Allan Spale – EECS 578

Introduction

• Improve English literacy skills of deaf students who primarily use ASL– ASL (American Sign Language)…a foreign

language– Deaf literacy in English impacts a student

in every facet of education– Teach students English using a strategy

called “English as a Second Language”

Signing and Education

• Lipreading: a method for pre-lingually deaf children to acquire English– Usually not successful, only 40% of

English phonemes are visible

• Deaf children must learn English in school while learning to read and write– “…average deaf high school graduate has

only a fourth grade reading ability.”

Methods of Communication with Deaf Students in School

• Spoken English– Requires students to use “lipreading” for

acquiring language communication– Not effective, most students do not have

access to usable linguistic input

Methods of Communication with Deaf Students in School

• Manually Coded English (MCE)– Codes used to visualize the structure of

words and sentences of English– A morpheme in English has a sign– Signed languages are still more efficient

than MCE in communicating– People who speak and sign simultaneously

omit many morphemes in their signing

Deaf Students: Bi-Lingual and Bi-Cultural

• English is taught as a second language– “[C]hildren in bilingual programs have

better comprehension of spoken English than do those in immersion [programs].”

• System philosophy– Include similarities and differences with

English and ASL– Eventually provide instructional feedback in

ASL to supplement English explanations

ICICLE System Overview

• ICICLE is an acronym for Interactive Computer Identification and Correction of Language Errors

• Two modules– Error identification– Response generation

ICICLE System Overview

• Tutorial Instruction– User submits written material– System responds with list of errors

• Only reports relevant errors that can be tutored

– System guides the user through a review for improving aspects of the user’s writing

– User can make changes and request another analysis

ICICLE System Design Goals

• Computer systems are suitable for learning using post-performance review– Doing intensive tasks restricts learning during their

execution

• “[S]atisfy the deaf learner’s need for understandable English input”– Most written material read by deaf students comes

from academic texts– Use English grammatical structures that the user

is trying to utilize in writing

ICICLE System Design Goals

• Provide feedback without a human teacher– Students do not have to “feel bad” being

corrected by a teacher– Might encourage students to write more

ICICLE System Diagram

Current Implementation

• Error Recognition is the focus• Follows a “user input, system response

model”– System acquires user text– User requests analysis– System highlights errors in sentences– User click on a highlighted sentence

returns static error explanation– User can resubmit text for another analysis

Grammar Coverage

• Grammar used is English-based with error productions– Error productions allow errors to be flagged– Uses COMLEX Syntax 2.2 lexicon– Handles many parts of speech and their

alternate forms– Has grammar recognition limitations– New grammar system from C. P. Rose relaxes

some grammar requirements (reducing error rules)

User Interface

• Designed using Tcl/Tk

• Layout structure– Text entry area– Analysis controls– Highlighting of errors– “Fix-it” window

User Interface Example

Toward Modeling the User

• Necessary to model user’s knowledge– User history of system use– User knowledge of English grammar

• Solves some problems– Choosing appropriate grammar parses– Provide relevant feedback to the user

Three Levels of Knowledge

• Acquired– User can correctly use these English

grammar structures and rules

• Zone of Proximal Development (ZPD)– User is in the process of acquiring these

English grammar structures and rules

• Unacquired– User cannot correctly use these English

grammar structures and rules

SLALOM Model

• SLALOM is an acronym for “Steps of Language Acquisition in a Layered Organization Model”

• Used to separate English into “a set of feature hierarchies”– Each hierarchy contains an ordered list of

items in their acquisition order– Acquisition sequence is used based on

previous language studies

The SLALOM Model

• First use involves evaluation of writing– SLALOM tags in the feature hierarchies will

be labeled as acquired, ZPD, and unacquired

• Subsequent uses will update the tags– Typically, one would expect ZPD items to

become acquired over time

SLALOM Diagram

Problems Usingthe SLALOM Modeland Their Solutions

• Initial evaluation does not use SLALOM tags– Separate evaluation pass with evaluation

not directly related to SLALOM

• Concern with difficulties for establishing and maintaining the user model– Large number of user writings– SLALOM captures syntactic content from

user writings

Problems Usingthe SLALOM Modeland Their Solutions

• Inaccurate feedback from user errors– Include statistical confidence measures

into system

• My solution– Implement a neural network system into

parsing methods and writing analysis

Reasoning onPartial Evidence

• ICICLE will not have complete information from SLALOM when evaluating user competency

• General learner profile will supplement the specific learner

• “Unique” situations will rely on a general profile, while system decisions rely on specific learner

Developing the User Model

• SLALOM feature relationships will be established based on– Research from second language

acquisition– Human evaluations of writing samples from

a studied learner population

Generating a Response

• Error Identification system component analyzes text and produces error list

• Text and error list sent to the Response Generator

• Response Generator system component supplies tutorial feedback

Anatomy of a Response

• Content– Factual knowledge of the response to help

the student to improve some part of his/her writing style

– Influenced by• Error annotations• Error source• “[I]nformation about the languages involved“• “[I]nformation from the acquisition model”

Anatomy of a Response

• Method– Manner of content organization and creation for

the user– Depends on…

* The kind of information available in the language model

* Kind of information available about various English constructs involved in the error

* Receptivity of the student to various kinds of information in the correction

* Indicates material directly quoted from the paper

Anatomy of a Response

• Form– Utilizing appropriate background knowledge– “[Structures] information to ensure that the

appropriate rhetorical resources are used in realizing the chosen method”

• History– “[M]aking the response contextually aware

by [referring to] earlier tutorial information and [current user knowledge]”

Anatomy of a Response

• Manner– “[S]tyle of the actual language employed in

realization of the response as actual English text”

– Depends on…• Language acquisition level of the user• Current language constructs the user is

attempting to acquire

Multimodal Response

• Present some tutorial information in a format closer the user’s “native language”– Improve the user’s knowledge acquisition– Reduce user’s stress level in using the

system

• Provision of a “signing agent”

Issues with a Signing Agent

• Signing agent compensates for a missing perception channel

• Times for using the signing agent

• Should not be added to the system without studying how to integrate it appropriately into the system

Conclusion

• Problem of literacy of deaf students– Provide a computerized tutorial tool

for learning English grammar• Gives feedback on user’s grammatical

errors relevant to the user’s knowledge domain

• Tracks the students acquisition of grammatical rules and constructs

Conclusion

• The future language tutorial system components– “[U]nique user language model”– “[F]lexible and robust tutorial planning”– “[M]ultimodal capabilities via the inclusion

of an animated signing agent”

Questions and Comments