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: eTA Tutoring iqE eReader/ Speaker Teaching/ Research ePaper- Compare eGradereResearcher

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)

ePaperCompare 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

eGrader 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

eResearcher 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 – Redundancy

IQ Enhancer (iqE) Text and Exercises to improve IQ Online Interactive Tutorial Meta Method to improve ability to take standardized tests – ACT, – SAT, LSAT, – GMAT, – GRE Semantic Algorithms reduced to Boolean Logic Possible AI applications

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

Results Chart shows: a rise in interest from 1998 to 2001, a decline from 2001 to 2003, and an increase again from 2005 to Decline from = f (IT Bubble Bust?)

Interest in Specific Online Learning Products

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 Cramster.com eduFire.com TutorVista.com

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)

Conclusion 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 3.If education social networks become prominent online tutoring software could have great commercial potential 4.Effect on assessment given tutoring software 5.Ethical issues

Discussion Comments 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: