Downloaded 13 times
![Exploratory Study: Good things about
DTs
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](https://image.slidesharecdn.com/integratingdigitaltracesintoasemantic-enricheddata-120417031845-phpapp02/85/Integrating-digital-traces-into-a-semantic-enriched-data-17-320.jpg)
More Related Content
Similar to Integrating digital traces into a semantic enriched data
Integrating digital traces into a semantic enriched data
- 1. INTEGRATING DIGITAL TRACES INTOA SEMANTIC- ENRICHED DATA CLOUD FOR INFORMAL LEARNING Vania Dimitrova, Dhaval Thakker, Lydia Lau
- 2. Outline Motivation and bigger picture Aggregating Digital traces into Semantic- enriched data cloud Semantic Data Browser Exploratory Evaluation Conclusions
- 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. Digital Traces broad, authentic, gradually increasing and up-to-date digital examples
- 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. Role of SemanticWeb Technologies 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. Processing Pipeline Digital Traces Collection Semantic Browsing & Augmentation & Query Interaction Bespoke Ontologies Ontology & Linked Underpinning Data Cloud
- 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 Interaction Bespoke Ontologies Ontology & Linked Underpinning Data 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. Processing Pipeline – Semantics Digital Traces Collection Semantic Browsing & Augmentation & Query Interaction Bespoke Ontologies Ontology & Linked Underpinning Data Cloud
- 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 Language Social 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. Semantic Augmentation Service Purpose: 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 ontologies Implementation: Semantic Ontology •RESTful interface for easy Repository integration AMOn •Contribution to the semantic augmentation in the IPC domain
- 12. Semantic Query Service Purpose: 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 Content Components: 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 Filtering Implementation: •RESTful interface for easy Concept Semantic integration, integrated with Frequency Relatedness Storyboard •Contribution to the semantic browsing
- 13. Trainer Learner
- 16. 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. of Interviews as an interviewer 10-15 10-15 10-15 >15 >15 0 0 0 0 1-5 No. of interviews as an applicant 10-15 10-15 10-15 >15 5-10 1-5 1-5 1-5 5-10 5-10
- 17. Exploratory Study: Goodthings about DTs 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
- 18. Exploratory Study: Issueswith DTs 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
- 19. 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.
- 20.
Editor's Notes
- #6 s