eLearning Systems on examples
- 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! 4
- 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) 5
- 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 6
- 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 7 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? 8 ● ...
- 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 10 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/ 12
- 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 13 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) 16
- 17. Ambiguity http://de.wikipedia.org/wiki/Mehrdeutigkeit 17
- 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 21
- 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) 22
- 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 23
- 24. Thank you for your attention Questions? konstantin@filtschew.de http://konstantin.filtschew.de/blog/ http://twitter.com/Fa11enAngel 24
- 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 25