From Learning Standards to Smart Learning Environments: A view of the challenges of technology enhanced learning
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![Activity example
<john> <launched> <cool book>
<john> <read> <page 1> ( d: "PT45S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] }
<john> <read> <page 2> ( d: "PT15S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] }
<john> <read> <page 3> ( d: "PT55S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] }
<john> <read> <page 4> ( d: "PT45S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] }
<john> <watched> <video 1> ( d: "PT3M" ) { p: ["page 4"], g: ["chapter 1", "cool book", "cool
class"] }
<john> <paused> <video 1> { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] }
<john> <resumed> <video 1> { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] }
<john> <watch> <video 1> ( d: "PT2M" ) { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"]
}
<john> <completed> <video 1> ( d: "PT5M" ) { p: ["page 4"], g: ["chapter 1", "cool book", "cool
class"] }
<john> <read> <page 5> ( d: "PT45S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] }](https://image.slidesharecdn.com/presentacionkeynoteateneo2017v4-171014023845/75/from-learning-standards-to-smart-learning-environments-a-view-of-the-challenges-of-technology-enhanced-learning-20-2048.jpg?cb=1672919852)
![33
Example #2: Semantic inference of
educational material
sun_java:'java/concepts/class.html'[
rdf:type->doc:Document;
dc:subject->doc:OO_Class].
doc:OO_Class[
rdf:type->doc:Concept;
doc:isPrerequisiteFor->doc:OO_Inheritance;
doc:subConceptOf->doc:Classes_and_objects].
doc:ClassesIntroduction[
rdf:type->doc:ConceptRole;
doc:isPlayedBy->doc:OO_Class;
doc:isPlayedIn->sun_java:'java/concepts/class.html';
doc:hasType->doc:Introduction].
doc:Introduction[
rdf:type->doc:ConceptRoleType;
doc:subConceptRoleOf->doc:Cover].](https://image.slidesharecdn.com/presentacionkeynoteateneo2017v4-171014023845/75/from-learning-standards-to-smart-learning-environments-a-view-of-the-challenges-of-technology-enhanced-learning-33-2048.jpg?cb=1672919852)
![Semantic inference
FORALL D,E weaker_example(D,E) <-
studyMaterial(D) AND example(E)
AND
EXISTS C (D[dc:subject->C] AND E[dc:subject->C]).
Weak example
• E and D are semantically labelled resources for education.
• E is an example (weak) for D if there is a C
Source: Nicola Hence et al. (2004) “Reasoning and Ontologies for Personalized E-Learning
in the Semantic Web” in Journal of Educational Technology and Society](https://image.slidesharecdn.com/presentacionkeynoteateneo2017v4-171014023845/75/from-learning-standards-to-smart-learning-environments-a-view-of-the-challenges-of-technology-enhanced-learning-34-2048.jpg?cb=1672919852)
From Learning Standards to Smart Learning Environments: A view of the challenges of technology enhanced learning
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The main purpose of TEL has been to provide an adequate support at the right time and with the appropriate means to foster the learning process by means of technology. In the last years we have provide modelization frameworks for learning content as long as improved intelligent analysis of the needs of students at an individualized level. The appearance of IoT and the new scenarios of massive courses introduces new factors and needs in the design of personalized and effective learning situations based on sensible environments, ubiquitous learning and scalable infrastructure. This talk overviews the evolution of TEL and describes some of its current challenges to provide orchestration of services and analytics that makes use of technical and semantic interoperability of the student data.
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From Learning Standards to Smart Learning Environments: A view of the challenges of technology enhanced learning
- 1. From Learning Standards to Smart Learning Environments: A view of the challenges of technology enhanced learning Miguel Rodríguez Artacho Universidad Nacional de Educación a Distancia (UNED, Spain) miguel@lsi.uned.es Twitter: @martacho Manila, 14 de Octubre de 2017
- 3. What do we NOT want? Non effective Non motivating Rigid Poor assessment Inefficient …etc.
- 4. Why reshape education? • New competences • Knowledge needs are changing • Life long learning • New interactions and ways to learn • New technologies • … Jean-Marc Côté
- 5. Traditional education • Prussian model since the XIX century • Few elements: Teacher, student and educational material • Rigid schemas: enrolment, schedule, assessment
- 6. Education “ecosystem” • Degree • Academic year • Subject • Classroom • Lecture • Lecture notes • Handouts • Exam
- 7. This model is effective but… • Group students by age • Regular school periods and calendar • Ideas and content structured in matters • Strict ethics: discipline • Competences in reading, writing and arithmetic
- 9. Educative innovations • Interactive software environments • Virtual and remote laboratories • Animations and simulations • Exercises platforms • Mobile applications • eBooks • Social networks
- 12. LMS-based learning • First integrated technology enhanced learning • Industry of e-learning • E-learning standards • Virtual mobility (e-portfolio) • Quality assurance models
- 13. LMS-based learning • Communities • Tracking • Authoring • Learning Objects • Assessment • Standards
- 14. LMS-based authoring • Focus on reusable resources • Label resources with metadata tags • Use of e-learning standards • Authoring tools
- 16. Evolution of e-learning standards Accesibilidad Evaluación Actividades/Contenido Gestión IMS Accesibility guidelines IMS Addition to LIP IMS Metadata extensions IMS QTI IMS QTI results IMS Content Packaging ADL SCORM 2004 IMS Learning Design IMS Simple Sequencing ADL SCORM 1.3 ISO SC36/WG2 Collab. Learning Inf. estudiante LIP IMS ePortfolio iCOPER PALO European Learner Mobility (ELM) IMS Enterprise IMS DRM IMS DR Metadata IEEE LOM IMS Metadata Dublin Core CanCore AEN/SC36 LOM Perfil de aplicación 2000 2010
- 17. • From package to open content • From local content to semantic search for suitable content • OER + Linked data New e-learning standards
- 18. xAPI statements I did THIS
- 20. Activity example <john> <launched> <cool book> <john> <read> <page 1> ( d: "PT45S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] } <john> <read> <page 2> ( d: "PT15S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] } <john> <read> <page 3> ( d: "PT55S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] } <john> <read> <page 4> ( d: "PT45S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] } <john> <watched> <video 1> ( d: "PT3M" ) { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] } <john> <paused> <video 1> { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] } <john> <resumed> <video 1> { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] } <john> <watch> <video 1> ( d: "PT2M" ) { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] } <john> <completed> <video 1> ( d: "PT5M" ) { p: ["page 4"], g: ["chapter 1", "cool book", "cool class"] } <john> <read> <page 5> ( d: "PT45S" ) { p: ["chapter 1"], g: ["cool book", "cool class"] }
- 21. Authoring issues • E-learning Standards are complex • Complex authoring tools • 2 examples: • Instructional ontologies and Educational Modelling Languages • Inferring new relationships in educational content
- 22. 22 Example #1: Semantic Web & education SOURCE: W3c Consortium (2002)
- 23. Dublin Core • Semantic framework • Interoperability of resources • Simple model • Widely used in the WWW and other industries
- 24. 24 RDF Resource Description Framework • Representing information about web resources • Provides a “meaning” to resources • Suitable for software processing • Described using XML
- 25. Dublin Core and RDF 25
- 26. 26 Ej. RDF http://www.example.org/index.html tiene un creador cuyo valor es John Smith ex:index.html dc:creator “John Smith,#4545534" creator http://www.example.org/ind ex.html John Smith http://www.example.org/index.html has a creator named John Smith
- 27. <?xml version="1.0"?> <rdf:RDF xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns# xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:exterms="http://www.example.org/terms/"> <rdf:Description rdf:about="http://www.example.org/index.html"> <dc:creator rdf:resource="http://www.example.org/staffid/4545534"/> </rdf:Description> </rdf:RDF> 27 <?xml version="1.0"?> <rdf:RDF xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns# xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:exterms="http://www.example.org/terms/"> <rdf:Description rdf:about="http://www.example.org/index.html"> <dc:creator rdf:resource="http://www.example.org/staffid/4545534"/> </rdf:Description> </rdf:RDF> RDF http://www.example.org/index.html has a creator named John Smith
- 28. 28 A semantic link in the WWW
- 29. 29 Ontologies for education An ontology is an explicit conceptualization of a domain knowledge
- 30. 30 Semantic authoring Cognitive Design Model Learning Design Model Conceptualization level Ontologies Instructional templates Instantiation level LT specification based content Instances
- 31. 31 SW-based authoring … Here you can find more <relation Name="Illustrates" Domain="Conceptual" Subject="invariant" Category="Example">examples </relation> of the concept invariant. …
- 33. 33 Example #2: Semantic inference of educational material sun_java:'java/concepts/class.html'[ rdf:type->doc:Document; dc:subject->doc:OO_Class]. doc:OO_Class[ rdf:type->doc:Concept; doc:isPrerequisiteFor->doc:OO_Inheritance; doc:subConceptOf->doc:Classes_and_objects]. doc:ClassesIntroduction[ rdf:type->doc:ConceptRole; doc:isPlayedBy->doc:OO_Class; doc:isPlayedIn->sun_java:'java/concepts/class.html'; doc:hasType->doc:Introduction]. doc:Introduction[ rdf:type->doc:ConceptRoleType; doc:subConceptRoleOf->doc:Cover].
- 34. Semantic inference FORALL D,E weaker_example(D,E) <- studyMaterial(D) AND example(E) AND EXISTS C (D[dc:subject->C] AND E[dc:subject->C]). Weak example • E and D are semantically labelled resources for education. • E is an example (weak) for D if there is a C Source: Nicola Hence et al. (2004) “Reasoning and Ontologies for Personalized E-Learning in the Semantic Web” in Journal of Educational Technology and Society
- 37. New frontiers in education • How to break down barriers • Nothing from the past is useful? What can we keep? • What are the new models • New reference frameworks • New ecosystem for education
- 38. MOOCs Can’t see the woods for the trees NON relevant questions: ¿business model? ¿Why only 2-7% finish the course? ¿Why are free? … MOOCs are not a panacea
- 39. MOOCs • Massive Open Online Courses • First success model for distance education • Worldwide adopted in many educational institutions • More adapted to post-industrial needs
- 40. Example: New schemas for new times • Content of the book: from cascade to agile software development • Book production: from traditional publisher to DIY • Teaching model: from traditional class to MOOC and SPOC Source: “Refactorizando la Educación” (2015) Carlos D. Kloos
- 42. Book production • Traditional publisher • Publisher printing • Traditional marketing and distribution • Slow cycles and not agile re-print • Expensive • Not adapted to demand • DIY model: Do it Yourself • LaTeX + GitHub • Print on demand • Print and electronic editions • Faster cycle • Adapted to demand
- 43. Enriched teaching resources: MOOC http://www.saasbook.info/courses Awesome feedback!
- 44. New learning interactions • Formal vs. Informal learning including Higher Education • Modularization of content https://micromasters.mit.edu • New industries and new jobs mismatch between educational programs and current jobs IKEA, GOOGLE training programs
- 45. • Do It Yourself model • Flexibility • Based on Competences • Student driven Autonomous learning
- 46. • Peer to peer education • Non centralized environments • Knowledge generated in microsocial interactions • Social networks as educational tools Social learning
- 47. • Not in the traditional ITS model • Coacher, not teacher • Emotive computing • Reputation • Context Smart Learning Environments Reshaping AI in Education
- 48. • Not restricted to formal learning • Autonomous and adaptive learning just-in-time • More than a VLE: learning guidance, hints, supportive tools • Personal factors into account • Multiple channels: IoT, ubiquitous computing devices, wearable computing Smart learning environments
- 49. • Context to provide relevant information • Presentation of information, service. Tagging of context to information • Student achievements • Real world: IoT and wearables SLE: Full context awareness
- 50. • Tailor instruction to individual needs • Collect data from disparate sources (physical, online,..) • Inference of learning requirements SLE: Big Data and Learning Analytics
- 51. • Emotive computing • Dynamic adaptivity • Autonomous pedagogical decission • Learning support across contexts • Personal factors into account • Recommends learning tools and strategies SLE: Autonomous decision making
- 53. Pedagogical innovations • Knowledge generated from micro-social interactions • Change in assessment practices • Learning in ubiquitous environments: virtual and physical integration • Real-time intervention in the learning process
- 54. Technological innovation • Flipped classroom: combining models • MOOCs: Open, free access, flexible, peer-to-peer • Game-Based learning: engagement, richer interactions • Augmented and virtual reality • Educational robots • Gesture-Based learning
- 55. — ”Everything needs to change, so everything can stay the same.” Giuseppe Tomasi di Lampedusa “The Leopard”
- 56. From Learning Standards to Smart Learning Environments: A view of the challenges of technology enhanced learning Miguel Rodríguez Artacho Universidad Nacional de Educación a Distancia (UNED, Spain) miguel@lsi.uned.es Twitter: @martacho Manila, 14 de Octubre de 2017 Thank you!