INTEGRATING DIGITALTRACES INTO A SEMANTIC-ENRICHED DATA CLOUDFOR INFORMAL LEARNINGVania Dimitrova, Dhaval Thakker, Lydia Lau
Outline   Motivation and bigger picture   Aggregating Digital traces into Semantic-    enriched data cloud   Semantic D...
Motivation   Modern learning models require linking    experience in training environments with    experience in the real...
Digital Traces  broad, authentic, gradually increasing and          up-to-date digital examples
Ill-Defined domains   Hard to specify and often require multiple    interpretations and viewpoints.   Soft skills –    c...
Role of Semantic WebTechnologies   To realise this vision, novel architectures are    needed which use: robust and cost-e...
Processing Pipeline              Digital              Traces             Collection       Semantic                        ...
Processing Pipeline – DTs  collection                                               •Availability of Social Web APIs      ...
Processing Pipeline –  Semantics              Digital              Traces             Collection       Semantic           ...
Processing Pipeline - Semantics     Stage 1: Activity Modelling on Interpersonal Communication                         Act...
Semantic Augmentation ServicePurpose :   Generic service designed to link                        is almost always best. An...
Semantic Query ServicePurpose :   Generic service for querying and   browsing using semantically augmented   content. In I...
Trainer   Learner
Exploratory Study    Domain : Job Interview    Digital Traces: User comments from YouTube -     cleaned from filtration,...
Exploratory Study: Good things aboutDTs   Participants particularly liked the authenticity    of the content:“Examples ar...
Exploratory Study: Issues withDTs   Issues requiring attention:     Two most experienced interviewers(p5 and p10)      c...
Conclusions   Social spaces bring new opportunities , i.e. as    a source of diverse range of real-world    experiences. ...
Thank You!D.Thakker@leeds.ac.ukhttp://www.imreal-project.eu/
Integrating digital traces into a semantic enriched data
Integrating digital traces into a semantic enriched data
Upcoming SlideShare
Loading in …5
×

Integrating digital traces into a semantic enriched data

1,255 views
1,186 views

Published on

Published in: Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,255
On SlideShare
0
From Embeds
0
Number of Embeds
393
Actions
Shares
0
Downloads
4
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • s
  • Integrating digital traces into a semantic enriched data

    1. 1. INTEGRATING DIGITALTRACES INTO A SEMANTIC-ENRICHED DATA CLOUDFOR INFORMAL LEARNINGVania Dimitrova, Dhaval Thakker, Lydia Lau
    2. 2. Outline Motivation and bigger picture Aggregating Digital traces into Semantic- enriched data cloud Semantic Data Browser Exploratory Evaluation Conclusions
    3. 3. Motivation Modern learning models require linking experience in training environments with experience in the real-world. Real-world experiences are hard to collect Social media brings new opportunities to tackle this challenge, supplying digital traces Exploiting social content as a source for experiential learning is being investigated in Immersive Reflective Experience based Adaptive Learning (ImREAL) http://imreal-project.eu/
    4. 4. Digital Traces broad, authentic, gradually increasing and up-to-date digital examples
    5. 5. Ill-Defined domains Hard to specify and often require multiple interpretations and viewpoints. Soft skills – communicating, planning, managing, advising, negotiating. Highly demanded Modern informal learning environments for soft skills can exploit digital traces to provide learning situations linked to real world experience by peers (other learners) or tutors.
    6. 6. Role of Semantic WebTechnologies To realise this vision, novel architectures are needed which use: robust and cost-effective ways to retrieve, create, aggregate, organise, and exploit Digital Traces in learning situations; in other words, to tame Digital Traces for informal learning. By combining major advancements in semantic web : semantic augmentation, semantic query, relatedness, similarity, summarisatio
    7. 7. Processing Pipeline Digital Traces Collection Semantic Browsing & Augmentation & Query InteractionBespokeOntologies Ontology& Linked UnderpinningData Cloud
    8. 8. Processing Pipeline – DTs collection •Availability of Social Web APIs •Noise filtration mechanisms* •Role of tutors/trainers in setting gold standard** Digital Traces Collection Semantic Browsing & Augmentation & Query InteractionBespokeOntologies Ontology& Linked UnderpinningData Cloud * Ammari, A., Lau, L. Dimitrova, V. Deriving Group Profiles from social media, LAK 2012 ** Redecker, C. et al. Learning 2.0- the impact of social media on learning in Europe, Policy Brief, European Commission, JRC, 2010
    9. 9. Processing Pipeline – Semantics Digital Traces Collection Semantic Browsing & Augmentation & Query InteractionBespokeOntologies Ontology& Linked UnderpinningData Cloud
    10. 10. Processing Pipeline - Semantics Stage 1: Activity Modelling on Interpersonal Communication Activity Analysis Use Case Social Web Theory Activity other relevant on a Use Model ontologies Case WN- Body Affect Stage 2: Activity Modelling Enrichment using Semantics LanguageSocial Multi-layered Web Activity Modelling Ontology (AMOn) for Interpersonal Communications Logical Encoding Stage 3: Providing Access to Real World Experiences Semantic Services: Augmentation,Query Story Boarding
    11. 11. Semantic Augmentation ServicePurpose : Generic service designed to link is almost always best. An authority handshake should be reserved for when you wish to show you are in charge. content with the concepts from the ontological knowledge bases in order to fully benefit from the reasoning capabilities of semantic technologies.Components: Simulators •Information Extraction: Finding mentions of entities in text •Semantic Linking: between entity mentions and ontologies, linked data •Semantic Repository: forward chaining repository for semantic Information Semantic expansion Extraction Linking •Ontologies: AMOn & External ontologiesImplementation: Semantic Ontology •RESTful interface for easy Repository integration AMOn •Contribution to the semantic augmentation in the IPC domain
    12. 12. Semantic Query ServicePurpose : Generic service for querying and browsing using semantically augmented content. In I-CAW, it allows searching of socially and locally authored data for real- world activities from the domain of Related Content interest Matching ContentComponents: Term(s), Browsing Simulators Concept(s) Tag Cloud •Concept Filtering: Identify matching concepts and relevant information •Content Filtering: Identify matching contents and relevant information •Concept Frequency: CF/IDF analysis Concept Content •Semantic Relatedness: Content & concept relatedness Filtering FilteringImplementation: •RESTful interface for easy Concept Semantic integration, integrated with Frequency Relatedness Storyboard •Contribution to the semantic browsing
    13. 13. Trainer Learner
    14. 14. Exploratory Study  Domain : Job Interview  Digital Traces: User comments from YouTube - cleaned from filtration, stories from blog-like environment by ImREAL volunteers  Participants Group 2: Applicants Group 1: Interviewers Participant ID P2 P3 P4 P5 P10 P1 P6 P7 P8 P9 No. ofInterviews as aninterviewer 10-15 10-15 10-15 >15 >15 0 0 0 0 1-5 No. ofinterviews as an applicant 10-15 10-15 10-15 >15 5-10 1-5 1-5 1-5 5-10 5-10
    15. 15. Exploratory Study: Good things aboutDTs Participants particularly liked the authenticity of the content:“Examples are the beauty of system – I will learn from examples [p10]”“Anything that facilitates the preparation of training material and provides real world examples to backup training is very helpful [p5]” Which probed them to:  Further reflect on their experiences, and in some cases help articulate what they had been doing intuitively  Provide their viewpoints (due to culture, environment, tacit knowledge) – acted as stimuli  Sense the diversity or consensus on the selected topic
    16. 16. Exploratory Study: Issues withDTs Issues requiring attention:  Two most experienced interviewers(p5 and p10) commented that some content could be mistaken as the norm.  For instance, a comment associated with a video stated “The interviewer has his hands in front of him, which indicates that he is concentrating and not fidgeting...”. P5 and P10 stressed that inexperienced users may see a comment in isolation and believe it would be valid in all situations  It was suggested that short comments could be augmented with contextual information to assist the assessment of the credibility of the different
    17. 17. Conclusions Social spaces bring new opportunities , i.e. as a source of diverse range of real-world experiences. signs are encouraging – digital traces as a  Initial source of authentic examples and stimuli  Further work is needed to capitalise on new opportunities brought by social content Semantics technologies provide apparatus for taming digital traces  Further work is needed to turn semantic browsing into informal learning.
    18. 18. Thank You!D.Thakker@leeds.ac.ukhttp://www.imreal-project.eu/

    ×