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eLearning systems on examples
Konstantin Filtschew @ Barcamp Mainz
29.11.2009

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About me
 Name: Konstantin Filtschew
 Interested in:
 Innovation
 eLearning Systems
 Security in computer systems
 Software design
 New challenges
 ...
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Agenda
 Motivation
 Definition eLearning
 Examples
 Natural Language Processing (NLP)
 Mobile learn experience
 Conclusion
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Do we need ”eLearning”?
 Wissensgesellschaft
 General Knowledge
 Specific Knowdledge
 Transfer of knowledge to the next generation
 cp -r / remotebrain: # maybe in future
 We can't stop learning!
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Definitions of eLearning
 Internet-enabled learning that encompasses training, education, just-in-
time information, and communication.
(http://www.eng.wayne.edu/page.php?id=1263)
 Any learning supported by digital means.
(http://azelearning.org/glossary/3)
 E-learning (or electronic learning or eLearning) encompasses forms of
technology-enhanced learning (TEL) or very specific types of TEL such
as online or Web-based learning.
(http://en.wikipedia.org/wiki/E-learning)
 eLearning kann verstanden werden als ein Lernprozess, der durch
Informations- und Kommunikationstechnologie unterstützt wird
(http://www.uni-hildesheim.de/de/9808.htm)
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Assistance through eLearning
 People must try to get better user experience
 People must do exercises for better knowledge
and understanding
 Some examples
 Vocabulary
 History
 Math
 Foreign languages
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Vocabulary Assistance
 Vocabulary trainer
 Does the user really know the word?
 Interactive trainer
 Enter vocabulary once
(semi automatic possible)
 Shows vocabulary randomly
 Shows/checks correct answer
instantly
 Can analyze time for answers
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 Vocabulary Frequency

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History eLearning example (1)
 We shouldn't try to replace books and paper
 Add interactive media (sound/video)
 Ineractive questions to text
 Multiple choice
 Plain text answers (keyword NLP)
●
+
Who was Cristopher Columbus?
In which year he started his travel?
+ +
●
● Was it India he found on his first travel?
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● ...

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History eLearning example (2)
 We have some problems to solve first!
 Many different books used
 People are lazy to search for more information
 Need to reference the learners book, text and part
 Need help to create tasks (NLP can help)
 Questions and multiple choice generation
 Help: Google books scan project 9

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Math eLearning
 Some examples:
 Gehirntraining
 Math quizzes:
 X + 11 = 23
 126 / X = 42
 sqrt(169) = X
 Motivation:
 Points (Challenge)
 Success experience
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 Not the same exercises

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Foreign Language
 Common difficulties in languages: Prepositions
Both Parties had much to offer ________ a time of growth in the region.
(a) in (b) at (c) about
 Part of Speech Tagger or Parser
 Can recognize prepositions
 Corpora
 Help identify wrong prepositions:
 Check whether this combination is really wrong
 Common with left word of prepositions but not
with right and vice versa 11

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Interactive Tutor (1)
 http://141.225.42.246/AutoTutorDemo/
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Interactive Tutor (2)
 Natural Language Processing (NLP)
 Questions by tutor
 Answers in plain text
 Tutor helps to ...
 find answer
 Helps to remember all important points
 Problems:
 Still need to reference the lecture
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 Not very ”human” in handling (no emotions)

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Natural Language Processing
 NLP is a great help:
 Analyze Text and extract important information
 Can generate questions (almost automatic)
 Can analyze answers
 Need:
 Corpora: digital texts
 Part of Speech Tagger and Parser
 A lot of computer power for given tasks
 Human control for results 14

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NLP: Corpora
 We have a lot of digital text:
 Wikipedia (and other wikis)
 Blogs
 Google book scan
 Problems:
 Common Speech (Umgangssrpache)
 Errors
 (Internet) slang → :) :/ ;) cu
 Ambiguity (Ambiguitäten) 15

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Part of Speech Tagger and Parser
 Part of Speech (POS) Tagger (Wortart Tagger)
 Analyzes only words
 Not so precise (about 80-90%)
 Faster than parser
 Parser
 Additionally to POS: sentence structure
 More precise (up to 96%)
 Sentence tree (word relations)
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Ambiguity
 http://de.wikipedia.org/wiki/Mehrdeutigkeit
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Sentence Tree
From Phineas Q. Phlogiston, “Cartoon Theories of Linguistics, Part ж—The Trouble with NLP“,
Speculative 18
Grammarian, CLIII(4), March 2008

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Human control
 Let people learn errors is a very bad learning
experience
 Wrong answers can be still right
 Computer can't decide about uncommon tasks /
questions / answers
 Advantage:
 Automatic generation of tasks
 Teacher has only to check the
written tasks 19

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Digital Books / Newspapers
 Add interactive tasks to common media
 Digital Books:
 We can add additional information (Video/Audio)
 Add exercises and quizzes
 Digital Newspapers:
 Schools can use for lessons (teacher selects)
 Teacher can create semi automatic tasks
 Advantage for newspapers: children learn to read
newspapers 20

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Mobile learn experience (1)
 We have:
 Powerful mobile phones: IPhone / Android / …
 Book/Newspaper readers
(not yet open for extensions)
 We have to travel
 Home → Work → Home
 Business travels
 Time slots for education
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Mobile learn experience (2)
 Busy people use every free minute
 Maybe you can't work, but you can learn
 Allready in use as eLearning:
 Podcasts
 Video and audio tutorials
 Already filtered information
 (Read it later add-on)
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Conclusion
 We have a lot to learn
 We have to use our free time slots
 We allready use eLearning
 eLearning is at the very beginning
 Politics say: Wissensgesellschaft
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Thank you for your attention
 Questions?
 konstantin@filtschew.de
 http://konstantin.filtschew.de/blog/
 http://twitter.com/Fa11enAngel
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Sources
 http://upload.wikimedia.org/wikipedia/commons/6/6e/Latin_dictionary.jpg (GPL) //Books
 http://media.photobucket.com/image/ship/MODELSHIPCONSTRUCTION/17Th%20Century%20Man-of-War/NewBritishShip.jpg
//Ship
 http://en.wikipedia.org/wiki/File:Ridolfo_Ghirlandaio_Columbus.jpg // Christopher Columbus
 http://web.airgamer.de/fileadmin/airgamer/images/spiele/01-gehirntraining/ss_gehirntraining_01.png //Gehirntraining
 http://upload.wikimedia.org/wikipedia/commons/3/38/Gregor_Reisch%2C_Margarita_Philosophica%2C_1508_%281230x1615%29.png
// Gregor Reisch, Margarita Philosophica
 http://openclipart.org/people/StefanvonHalenbach/StefanvonHalenbach_Teacher_L_mpel.png // Teacher
 From Phineas Q. Phlogiston, “Cartoon Theories of Linguistics, Part ж—The Trouble with NLP“, Speculative Grammarian,
CLIII(4), March 2008 // Pretty little girl
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