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.
4.18.24 Movement Legacies, Reflection, and Review.pptx
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!