Slides presented by Tobias Ley at the 3rd Learning Layers reviews meeting about how the Social Semantic Server (SSS) support the Layers tools in the Healthcare and Construction areas.

1. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.euhttp://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Learning Layers
Scaling up Technologies for Informal Learning in SME Clusters
SSS Healthcare Support
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2. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu 2
Agenda

3. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Tool Integration: Healthcare
• Bits and Pieces
– Completely based on SSS, thus uses nearly all services (e.g., Data Import, User
Event, Tag, Learning Episode, Recommendation, Category, Activity, Search ...)
• Discussion Tool
– Entity and Learning Episode services enable Bits to be attached to Q/As
• Living Documents
– Living Document service links Q/As to documents
– Planned: Recommend potential contributors or documents of interest
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4. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Examples in Bits and Pieces
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Tag Recommender
Resource
Recommender

5. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Examples in Discussion Tool
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Bits can be
attached to
discussions

6. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Examples in Living Documents
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Create new / link
Living Document to
discussion

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Agenda

8. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Resource Recommender based
on Cognitive Processes
• Recommender research exploits digital traces of
social actions and interactions
– e.g., Collaborative Filtering (CF) suggests resources of
most similar users
• BUT: In CF, users treated as just another entity
(such as a resource, a tag etc.)
• Structuralist simplification that neglects attention and
interpretation dynamics
• No ranking of resources in CF
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SUSTAIN (Love et al., 2004)
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• Resource represented by features
• Cluster(s) H
• Vector of values along the n feature
dimensions
• Fields of interest
• Attentional weights wi:
• Importance of feature for user
• Training (for each resource R)
• Start with one cluster H
• Form new cluster if sim(R,H) > T
• Adjusting Hi and wi after each run
• Testing (for each candidate c)
• Compare features of candidate to highest
activated cluster (Hmax)

10. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Evaluation
• Social Bookmarking datasets (e.g.,
BibSonomy)
• Resource features derived by Latent
Dirichlet Allocation (LDA) topics
• Per user: 20% most recent used
resources for testing, 80% for training
• In many cases only one resource for
training!
• Keeps chronological order
→ predict future based on the past
• State-of-the-art baseline algorithms
• Recall / Precision for k = 1 – 20
recommended resources
•  presented at WWW‘15 conference
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11. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Tag Recommender Online Study
• 2 tag recommender algorithms inspired by cognitive science
– 3Layers based on human categorization (semantic context)
– BLL based on learning and forgetting (time context)
• Offline evaluations showed good results in terms of accuracy
• Online study would show user acceptance in a workplace setting
•  Collaborative digital curation scenario using our KnowBrain tool
• 18 university employees explored the topic of „designing
workplaces that move people“ for a period of four weeks
– Collected at least 4 resources per week either alone or in group
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Study Interface (KnowBrain)
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Preliminary Results
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Learning Layers
Scaling up Technologies for Informal Learning in SME Clusters
SSS Construction Support
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Agenda
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16. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Tool Integration: Construction
• Learning Toolbox
– Create, search, tag Tiles, Apps and other contents (e.g., videos from
AchSo!)
• AchSo!
– Circle and Video services arrange videos and make them available to
Learning Toolbox (or maybe even Bits and Pieces / Living Documents)
• Bookmarker / Attacher
– Metadata, Tag, Search services for annotating and finding bookmarks
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Examples: Learning Toolbox
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Search for
content in LTB
using SSS

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Examples: Learning Toolbox
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SSS
query
result

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Examples: AchSo!
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Circle service to
arrange videos in
groups
or share videos with
colleagues

20. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu 20
Agenda

21. http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu
Context Aware Resource
Recommenders: Location
• Opportunity for Learning Toolbox
and AchSo! because the location
info is especially important in the
construction domain
• Exploit user location to improve
recommendations
– e.g., especially for cold-start users
with no explicit interaction data
• Use current location to find
nearby artefacts
• Use location history of user to
identify interests
– Find similar users → Collaborative
Filtering (CF)
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Evaluation
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• We simulate our workplace
setting with a dataset from
FourSquare
• Focus on cold-start users
→ no training data, 2,783
evaluated users
• Data of more than 2 million
users available for CF
• 3 Approaches based on CF
• Jaccard similarity
• Network-based
– Neighborhood overlap
– Adamic adar
• MostPopular (baseline)
→ Presented at RecSys‘15

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Agenda

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Giving learners the power to understand and analyse their learning process!
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Monitoring
Activities
Exploring
Topics
Assessing Informal Learning Support

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Assessing Informal Learning Support
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Understanding
explicit and implicit
social relations
• Evaluate Knowledge Acquisition, Participation, Knowledge creation (3
Metaphors of Learning)
• Presented at ECTEL‘15 in Toledo, Spain and at ICWL‘15 in Guangzhou, China