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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
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
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
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
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
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
52.
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
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!