Konstantin Filtschew presents examples of how eLearning systems can provide educational assistance through interactive exercises in various subjects like vocabulary, history, math, and foreign languages. Natural language processing techniques like part-of-speech tagging and parsing can help generate automatic questions and analyze student answers, though human oversight is still needed. Mobile devices open opportunities for learners to make use of spare time for educational podcasts, tutorials, and customized information on the go.

1. eLearning systems on examples
Konstantin Filtschew @ Barcamp Mainz
29.11.2009

2. About me
 Name: Konstantin Filtschew
 Interested in:
 Innovation
 eLearning Systems
 Security in computer systems
 Software design
 New challenges
 ...
2

3. Agenda
 Motivation
 Definition eLearning
 Examples
 Natural Language Processing (NLP)
 Mobile learn experience
 Conclusion
3

4. 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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5. 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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6. 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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7. 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

8. 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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● ...

9. 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

10. 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

11. 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

12. Interactive Tutor (1)
 http://141.225.42.246/AutoTutorDemo/
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13. 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)

14. 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

15. 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

16. 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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17. Ambiguity
 http://de.wikipedia.org/wiki/Mehrdeutigkeit
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18. Sentence Tree
From Phineas Q. Phlogiston, “Cartoon Theories of Linguistics, Part ж—The Trouble with NLP“,
Speculative 18
Grammarian, CLIII(4), March 2008

19. 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

20. 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

21. 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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22. 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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23. 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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24. Thank you for your attention
 Questions?
 konstantin@filtschew.de
 http://konstantin.filtschew.de/blog/
 http://twitter.com/Fa11enAngel
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25. 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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