Taming digital traces for informal learning dhaval

Taming digital traces for informal learning dhaval

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  TAMING DIGITAL TRACESFOR INFORMAL LEARNING: A SEMANTIC-DRIVEN APPROACH Dhaval Thakker, Dimoklis Despotakis, Vania Dimitrova, Lydia Lau, Paul Brna
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  Exploitation of digitaltraces as a source for informal learning
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  Exploitation of digitaltraces as a source for informal learning Exploration Environment digital traces digimind.com Taming Semantic Web Technologies •Retrieve • Semantic Data browsers •Aggregate • Semantic Nudges •Organise
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  Semantic Data Browser Focus Concept Facts Eye Contact is Body Language Social Content
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  Processing Pipeline: Semantic Data Browser Digital Traces Collection Semantic Augmentation & Browsing & Query Interaction Bespoke Ontologies Ontology & Linked Underpinning Data Cloud
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  Processing Pipeline: DTs collection •Availability of Social Web APIs •Noise filtration mechanisms* •Role of tutors/trainers in setting gold standard** Digital Traces Collection Semantic Augmentation & Browsing & 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
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  Processing Pipeline: Semantics Digital Traces Collection Semantic Augmentation & Browsing & Query Interaction Bespoke Ontologies Ontology & Linked Underpinning Data Cloud
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  Ontology Underpinning Stage 1: Activity Modelling on Interpersonal Communication Analysis Activity Use Case Theory Activity other relevant Social Web 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 Thakker, D. A., Dimitrova, V., Lau, L., Denaux, R., Karanasios, S., & Yang-Turner, F. (2011,). A Priori Ontology Modularisation in Ill-defined Domains. In I-Semantics (7th International Conference on Semantic Systems). Graz, Austria http://imash.leeds.ac.uk/ontology/amon/
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  Semantic Augmentation Service Purpose : Generic service designed to link content with the concepts from the ontological knowledge bases in order to Handshake BL fully benefit from the reasoning capabilities of semantic technologies. language Body Components: Handshake • Information Extraction: Finding Simulators mentions of entities in text reserved for when you wish to show you are in charge. handshake is almost always best. An authority handshake should be • Semantic Linking: between entity mentions and ontologies, linked data • Semantic Repository: forward chaining repository for semantic expansion • Ontologies: AMOn & External Information ontologies Semantic Linking Extraction Implementation: • RESTful interface for easy Semantic Ontology integration Repository AMOn
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  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 interest Components: Related Content • Concept Filtering: Identify matching Matching Content concepts and relevant information Term(s), Browsing Simulators • Content Filtering: Identify matching Concept(s) Tag Cloud contents and relevant information • Concept Frequency: CF/IDF analysis • Semantic Relatedness: Content & concept relatedness Concept Content Implementation: Filtering Filtering • RESTful interface • Contribution to the semantic browsing of content and knowledge bases Concept Frequency Semantic Relatedness
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  Semantic Data Browserfor Learning • Data browsers Limitations: • Learner is in-charge • Cognitive onus on Learner • Best course of action for him/her. • Require to build on sound frameworks • Intelligent techniques to extend I-CAW with features that facilitate informal learning, yet preserve the exploratory nature of social environments
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  Nudge – ChoiceArchitecture •Influence options in a way that will support choosers to act in their own interest, preserving freedom of choice •This can be realized via suitable alerts or nudges •Nudge should alert people's behavior in a predictable way and at the same time it should be easy and cheap to avoid
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  Nudges in Learning •(Kravčík & Klamma, 2011) studied the potential impact of such architecture on learning environments and listed some recommendation/interpretation in the TEL context: • For learning to be deep, it is necessary to provide various perspectives on the same topic. • People like stories but they may be misleading and oversimplified. Thus it is crucial to choose suitable analogies and metaphors. • In addition to confirming examples, also counter-examples are important to demonstrate the validity of hypotheses and to assess risks. Miloš Kravčík and Ralf Klamma. 2011. On psychological aspects of learning environments design. In Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning (EC-TEL'11), Carlos Delgado Kloos, Denis Gillet, Raquel M. Crespo García, Fridolin Wild, and Martin Wolpers (Eds.). Springer-Verlag, Berlin, Heidelberg, 436-441..
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  Our contribution: Semantic Nudges • How some of these can be achieved using semantic web technologies. • We map nudges against the semantic technologies that allows achieving them Nudges/Choice Architecture Impact on LE Semantic Nudges
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  I-CAW: introducing semantic nudges Digital Traces Collection Semantic Semantic Nudges Browsing & Augmentation & Interaction Activity Query Model Ontology Ontology (AMOn) & Underpinning Linked Data Cloud
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  Semantic Nudges: Signposting •Default Options • usually lot of people end up with this • The choice architecture encourages careful design of default choices • "default", usually lots of people will end up with it. • In semantic Data Browsers: • have information on a focus concept • facts from ontological knowledge bases. • The exact facts and amount of these facts that are available to read while browsing affects what the learners read, the path they can take for browsing and ultimately their awareness. Entity Summarisation
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  Semantic Nudges: Prompts •According to the choice architecture, the choice architect is intended to influence the choices in a way that will make the choosers better off, as judged by themselves. • This can be realized via suitable alerts • This influence can be realised via suitable prompts as non-invasive suggestions based on similar and/or contradictory facts(factual knowledge)/content.
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  Learner Trainer
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  Exploratory Study Q: Whatare potential benefits of using semantically augmented DTs and nudges in social spaces for informal learning, and what are the further issues to address? •Average age 44 years •Average age 28 years •Experiece: 3 with 10/15 •Experiece: 3 with 1-5 Interviews, 2 with >15 Interviews, 2 with 5-10 digital interviews interviews traces Interviewers Interviewee
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  Exploratory Study What nonverbalcues can be observed in job interview situations? (same for both groups) What nonverbal cues show nervousness? (same for both groups) How would an interviewer deal with an aggressive applicant? (for group 1) and How would an applicant deal with an aggressive interviewer? (for group 2)
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  Liked the authenticityof the Liked the authenticity of the content, content as Stimuli content, content as Stimuli “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]” •Stimuli –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) –Sense the diversity or consensus on the selected topic
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  Content can betaken as Norm (needs contextualising) For example, comment “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 viewpoints
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  Semantic Nudges: Potentialfor informal leaning in Semantic Browser Signposting •a fruitful way to provide a quick summary for understanding a concept •for exploration which leads to something new to learn. Prompts •Task setting (pointed at aspects participants might have not thought about ) •Complimentary knowledge (prompting participants to “look at the task in a holistic way”, “pointing at alternatives”, and “helping see the big picture”)
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  Nudges need contextualising, explicit viewpoints •Contextualise prompts – social content context , user interaction history, interaction focus •Elicit viewpoints and make it explicit •Different prompts (e.g. complimentary) •Different strategy for signposting
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  1. Learning context: – Informal Learning is important – Social spaces and user generated content offer new opportunities 2. Technology: – Nudges to empower exploration – Semantics is a promising technique for implementing nudges (“Semantic Nudges”) Thank You! Dr Dhaval Thakker, Research Fellow, University of Leeds [email protected] http://www.imreal-project.eu/