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Byrne, Tang, Tang, Tranduc Web 2.0 Electronic Teaching and Tutoring Assistant (eTA) Products and their Distribution Potential Research Products: the Electronic Tutoring and Teaching Assistant (eTA) Suite: Research in Online Education Mittal, Ankush , 2006; Chen, Nian-Shing, 2008

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Byrne, Tang, Tang, Tranduc

Web 2.0 Electronic Teaching and Tutoring Assistant (eTA) Products and their Distribution Potential

text mining as core technique
Research in Online Education

Mittal, Ankush , 2006;

Chen, Nian-Shing, 2008

Subset of Text Mining = Semantic Analysis

Richard Landauer

Technique = Latent Semantic Analysis

Application = Automated Essay Scoring (Burstein, 2004)

Text Mining as Core Technique
A client based and server computer application to detect plagiarism

Key word function to download Web Pages

Compares up to 100 student papers

Uses author’s semantic algorithm to eliminate noise

Fast and stable

automated student essay scorer to assess student writing

Algorithms based on:

Flesch Kincaid Equations

Latent Semantic Analysis

Proprietary semantic algorithm

Initial Beta Testing = 70-85% correlation between machine and human reader

ereader espeaker
application to improve reading speed, comprehension and retention

Traditional speed reading scroll bar

Allows student to:

Translate text to speech

Increase speech speed

Highlight important parts of text

Generate reports

Print out of highlighted words and phases

List of frequency of important names and concepts

Vector context of frequent names and concepts

Academic Research Search Engine

Key word function to download Web hits

Compares template articles with Web articles

Automatically filters out and selects relevant articles based on:

Content/ lack of

Writing sophistication congruency/ in-congruency


iq enhancer iqe
Text and Exercises to improve IQ

Online Interactive Tutorial

Meta Method to improve ability to take standardized tests





Semantic Algorithms reduced to Boolean Logic

Possible AI applications

IQ Enhancer (iqE)
market analysis
Method: Bibliometrics

Developed by library scientists: Ball, 2006

Method to measure frequency of publications as indicator of interest.

Interest in “Online Learning Software”

According to Google = general interest (in hundreds)

According to ERIC = education interest

Market Analysis

Chart shows:

  • a rise in interest from 1998 to 2001,
  • a decline from 2001 to 2003,
  • and an increase again from 2005 to 2008.
  • Decline from 2001-2003 = f (IT Bubble Bust?)
current levels of interest
Current Levels of Interest
  • Hits correlate well with our previous bibliometric analyses with high interest in
    • reading improvement
    • online tutoring

May 15, 2009 Google News Hits

unexpected results
“Online study groups’ as a subset of “online tutoring groups.” Received hits for the first time

Chaker, 2009

Unexpected Results
student of fortune
Founded by: Sean McCleese and Nikhil Sreenath (aged 25) (

Average: $15 Fee/transaction

Revenue in millions at a 12.5% WEEKLY increase

Users make money

Top tutor: college senior

$60,000 per year

Source: May 10, 2009)

Student of Fortune
Rank of interest in applications

1. reading improvement software

2. online tutoring software

3. plagiarism detection software

4. critical thinking software

5. academic search engines

6. automated writing scoring machines

more research needed
Potential areas of needed research

1. emerging education social networks for the student market

2. possible social networks for the teaching market

If education social networks become prominent online tutoring software could have great commercial potential

Effect on assessment given tutoring software

Ethical issues

More Research Needed

Marketing suggestions?

Call for participation:

All applications described are or will be open source.

• Would anyone like to work on any of our research projects?

• Would anyone be interested in testing any of the products?

• Please contact Michael Tang at: