e-learning 3.0 and AI


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e-learning 3.0 and AI

  1. 1. E-Learning 3.0 anyone, anywhere, anytime, and AI Learning NetworksNeil RubensActive Intelligence GroupKnowledge Systems Lab /University of Electro-CommunicationsTokyo, Japanhttp://ActiveIntelligence.org http://www.flickr.com/photos/lifeinverted/5651315924/SPeL 2011: International Workshop on Social and Personal Computing for Web-SupportedLearning Communities
  2. 2. Evolution of eLearning: eLearning 1.0eLearning uses technology to enhance LearningTo understand where the eLearning might be going, we need to take a quicklook at where its been‣ eLearning 1.0: ‣ Web 1.0: ‣ reading: content became easily accessible ‣ logging: user’s activities could be logged and analyzed ‣ Learning Theories: ‣ Behaviorism: learning is manifested by a change in behavior, environment shapes behavior, contiguity ‣ Cognitivism: how human memory works to promote learning
  3. 3. Evolution of eLearning: eLearning 2.0‣ eLearning 2.0: ‣ Web 2.0: ‣ writing: anybody can easily create content (e.g. blogs, wiki, etc.) ‣ socializing: interaction is easy (e.g. facebook, twitter, etc.) ‣ Learning Theories: ‣ Constructivism: constructing ones own knowledge from ones own experiences (enabled through writing) ‣ Social Learning: people learn from one another (enabled through socializing)
  4. 4. eLearning 3.0? http://www.flickr.com/photos/christian78/2960519381
  5. 5. Review of Evolution of Systems Predictions: eLearning 3.0 Contextual Personal IntelligentE-Learning 2.0 Connected Integrated Collaborative Social/Communicative Dynamic Databases/RSSE-LearningInteractive 1.0 Multimedia Static HTML- or Text Greller, 2011
  6. 6. Review of Predictions: eLearning 3.0consume( create,(form,(share( socialise(transfer( par.cipate(( connect(transmit( reflect( create((together)(cer.fy( evidence( collaborate( recognise( Content( Process( m( vis m( ( m( uc . ris m vis str m( vio vis m( c. on vis ha uc. vis tru ec . Be tr i. s i o ?c n gn on oc on Ins C o C S C Greller,(2011( Greller, 2011
  7. 7. Review of Predictions: eLearning 3.0 e-Learning 1.0 e-Learning 2.0 e-Learning 3.0Meaning is Dictated Socially constructed Socially constructed and Contextually reinventedTechnology is Confiscated at the Cautiously adopted Everywhere classroom door (digital immigrants) (ambient, digital universe) (digital refugees)Teaching is Teacher to student Teacher to student and Teacher to student, student student to student to student, student to (progressivism) teacher, people-­‐technology-­‐ people (co-constructivism)Classrooms are In a building In a building or online Everywhere (thoroughlylocated (brick) (brick and click) infused into society: cafes, bowling alleys, bars, workplaces, etc.)Teachers are Licensed Licensed professionals Everybody, everywhere professionalsHardware and Are purchased at Are open source and Are available at low costsoftware supply great cost and available at lower cost and are used purposively ignoredIndustry views Assembly line As ill-­‐prepared assembly As co-­‐workers orgraduates as workers line workers in a entrepreneurs knowledge economy(adopted from Moravec 2009: 33) (Ogorshko, 2011)
  8. 8. Our Predictions: eLearning 3.0Typical predictions of eLearning 3.0:Learning -> Technologies Limitation: Needed technologies may not be availableOur Predictions:Technologies -> Learning ‣ What new technologies will become available? ‣ What aspects of Learning Theories could be activated by using and extending new technologies?
  9. 9. Why do we need eL 3.0? Whats Wrong with 2.0?http://etc.usf.edu/clipart/28000/28015/tower_pisa_28015.htm
  10. 10. Challenges: Is this6=Social? People Talking ; Social (S. Goel, et al. 2011)
  11. 11. Limitations: Broken Knowledge Cycle‣ Problem: The current cycle of knowledge creation/utilization is inefficient ! ‣ large portion of created content is never utilized by others* only 0.05% of twitter messages attracts attention (Wu et. al., 2011) only 3% of users look beyond top 3 search results (Infolosopher, 2011) ‣ large parts of created contents are redundant (Drost, 2011) ‣ Peak Social – the point at which we can gain no new advantage from social activity (Siemens 2011) utilize U0lized d ge no wle K is ting Ex Redundant create Knowledge Novel *there are some personal benefits e.g. externalization, crystallization, etc.
  12. 12. InformationOverload
  13. 13. Web 3.0 Radar Networks & Nova Spivack, 2007 http://www.technodiscoveries.com/2010/01 Web$3.0$ Web$x.0$Degree(of(Informa&on(Connec&vity( Seman&c(Web( Meta(Web( Connects$knowledge$ Connects$intelligence$ Web$1.0$ Web$2.0$ The(Web( Social(Web( Connects$informa6on$ Connects$people$ Degree(of(Social(Connec&vity( Steve(Wheeler,(University(of(Plymouth,(2011(
  14. 14. AI is poised to Play a Major Role ‣ AI has been successful in ‘restricted’ domains e.g. chess ‣ In more open domains (e.g. eLearning) success of AI has been limited: ‣ More Complexity -> More Parameters -> More Data, More Computational Resources ‣ Large scale data and computational resources have not been easily available ‣ Things are changing: ‣ Large-scale data is becoming available (BIG/Open data) ‣ Large-scale Computational resources are becoming accessible (cloud computing) * more specifically Machine Learning
  15. 15. BIG/Open data‣ Open data: freely available to everyone to use and republish as they wish; e.g. wikipedia, twitter, data.gov, etc.‣ Big data: ‣ amount of data generated is growing by 58% per year (Gantz, 2011) ‣ pieces of content shared on Facebook 30 billion/month (McKinsey, 2011)‣ Big Data in eLearning ‣ KDD Cup 2010: 36 Million ITS records (PSLC, CMU) ‣ Learning Dataset: > 30 Million tweets (Rubens & Louvigne et. al., 2011) ‣ includes data on how users learn outside of the classroom (not typically available)
  16. 16. Data Science Large data sets can potentially provide a much deeper understanding of both nature and society. Social scientists are getting to the point in many areas at which enough information exists to understand and address major previously intractable problems. (Science, 2011)‣ Traditional: ‣ Hypothesis -> Model -> Validation (data) ‣ Limitations ‣ Sometimes is disconnected from the reality ‣ Validation data is often biased by the initial hypothesis ‣ Time Consuming: model must be explicitly programmed‣ Data-driven ‣ Data -> Model ‣ Advantages ‣ model is constructed automatically by utilizing AI methods ‣ large number of dimensions could be analyzed ‣ can handle complexity well
  17. 17. Learning Theories (Ireland, 2007, link)
  18. 18. Learning Analytics‣ Education is, today at least, a black box. We dont really know: ‣ How our inputs influence or produce outputs. ‣ Which academic practices need to be curbed and which need to be encouraged. We are essentially swatting flies with a sledgehammer and doing a fair amount of peripheral damage.‣ Once we better understand the learning process — the inputs, the outputs, the factors that contribute to learner success — then we can start to make informed decisions that are supported by evidence. (Siemens, 2011)
  19. 19. Analysis of Large-scale Distributed Collaborative LearningAudi reached out to public to help todefine what Progress IS.What is Progress: faster, cheaper, eco,comfortable, beautiful?People could collaborate, discuss, andvote for each others definition of progress.> 100,000 tweets In collaboration with:
  20. 20. eLearning 3.0‣ Automatically discover new Learning Models ‣ by applying AI methods ‣ to BIG data