• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Taming digital traces for informal learning  dhaval
 

Taming digital traces for informal learning dhaval

on

  • 1,070 views

Modern learning models require linking experiences in training environments with experiences in the real-world. However, data about real-world experiences is notoriously hard to collect. Social spaces ...

Modern learning models require linking experiences in training environments with experiences in the real-world. However, data about real-world experiences is notoriously hard to collect. Social spaces bring new opportunities to tackle this challenge, supplying digital traces where people talk about their real-world experiences. These traces can become valuable resource, especially in ill-defined domains that embed multiple interpretations. The paper presents a unique approach to aggregate content from social spaces into a semantic-enriched data browser to facilitate informal learning in ill-defined domains. This work pioneers a new way to exploit digital traces about real-world experiences as authentic examples in informal learning contexts. An exploratory study is used to determine both strengths and areas needing attention. The results suggest that semantics can be successfully used in social spaces for informal learning – especially when combined with carefully designed nudges.

Statistics

Views

Total Views
1,070
Views on SlideShare
413
Embed Views
657

Actions

Likes
0
Downloads
0
Comments
0

8 Embeds 657

http://imash.leeds.ac.uk 401
http://localhost 107
http://www.imreal-project.eu 85
http://imreal-project.eu 56
http://translate.googleusercontent.com 3
https://twitter.com 2
https://si0.twimg.com 2
http://webcache.googleusercontent.com 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • Conference 21 st century skills Tom – 21 st century Social media savvy Recent graduate So informal learning does happen for the 21 st century learners So far ad hoc bases
  • Just for Dhaval: If you noticed in the previous slide, what if he did not know about handshake, how does he comes to that Exploratory search seems suitable for this Or if he knew abt handshake, he can benefit from nowing what are the other body language around handshake?
  • Semantic data browsers[3] are the new breed of applications to come from the research efforts in the semantic community. Such browsers offer browsing of ontologies and semantically augmented data (e.g. content) by laying out browsing trajectories using relationships in the ontologies.
  • Semantic browsers can offer opportunities to build learning environments in which exploration of content is governed by ontologies that capture contextual aspects. Data browsers assume that the users are in charge of what they do when using the browser. This puts the cognitive onus on the user, and is particularly acute in the case of a user being a learner, i.e. not familiar with the conceptual space in the domain and may be unable to decide what is the best course of action for him/her.
  • Mainly used in the public administrators and policy making, but also businesses ….
  • And learning…in terms of suggestions…not a technical solution…
  • Procedure and data collection. In each session, a participant was firstly introduced to I-CAW [5 min] by following a script to perform a simple independent task. A standard script with the three tasks (Table 2) was then given to the participant which required the use of search box or signposting (All Facts, Key Facts and Overview) to find/browse relevant examples in I-CAW. When the participant finished a task, a semantic prompt was presented by the system when appropriate (e.g. task 2 included a similarity-based prompt, and task 3 included a contradiction prompt). After a participant completed all the tasks, the experimenter collected the participant’s feedback on his/her experience with I-CAW (using a semi-structured interview and a questionnaire). The materials for the study are available online. http://imash.leeds.ac.uk/imreal/icaw.html#evaluation
  • Two most experienced interviewers(p5 and p10) commented that some content could be mistaken as the norm .

Taming digital traces for informal learning  dhaval Taming digital traces for informal learning dhaval Presentation Transcript

  • TAMING DIGITAL TRACES FOR INFORMAL LEARNING: A SEMANTIC-DRIVEN APPROACH Dhaval Thakker, Dimoklis Despotakis, Vania Dimitrova, Lydia Lau, Paul Brna
  • Exploitation of digital tracesas a source for informal learning
  • Exploitation of digital traces as a source for informal learning Exploration Environment digital tracesdigimind.com Taming Semantic Web Technologies •Retrieve • Semantic Data browsers •Aggregate • Semantic Nudges •Organise
  • Semantic Data Browser Focus Concept FactsEye Contact is Body Language Social Content
  • Processing Pipeline: Semantic Data Browser Digital Traces Collection Semantic Augmentation & Browsing & Query InteractionBespokeOntologies Ontology& Linked UnderpinningData Cloud
  • 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 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
  • Processing Pipeline: Semantics Digital Traces Collection Semantic Augmentation & Browsing & Query InteractionBespokeOntologies Ontology& Linked UnderpinningData Cloud
  • 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 EncodingThakker, 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, Austriahttp://imash.leeds.ac.uk/ontology/amon/
  • Semantic Augmentation ServicePurpose : 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 BodyComponents: 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 ExtractionImplementation: • RESTful interface for easy Semantic Ontology integration Repository AMOn
  • 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 ContentImplementation: Filtering Filtering • RESTful interface • Contribution to the semantic browsing of content and knowledge bases Concept Frequency Semantic Relatedness
  • Semantic Data Browser for 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
  • Nudge – Choice Architecture •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 peoples behavior in a predictable way and at the same time it should be easy and cheap to avoid
  • 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-TEL11), Carlos Delgado Kloos, Denis Gillet, Raquel M. Crespo García, Fridolin Wild, and Martin Wolpers (Eds.). Springer-Verlag, Berlin, Heidelberg, 436-441..
  • 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
  • I-CAW: introducing semantic nudges Digital Traces Collection Semantic Semantic Nudges Browsing & Augmentation & InteractionActivity QueryModel OntologyOntology(AMOn) & UnderpinningLinkedData Cloud
  • 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
  • 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.
  • LearnerTrainer
  • Exploratory StudyQ: What are potential benefits of using semanticallyaugmented DTs and nudges in social spaces for informallearning, 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
  • Exploratory StudyWhat nonverbal cues can be observed in jobinterview situations? (same for both groups)What nonverbal cues show nervousness? (samefor both groups)How would an interviewer deal with an aggressiveapplicant? (for group 1) and How would anapplicant deal with an aggressive interviewer?(for group 2)
  • Liked the authenticity of 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 andprovides 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
  • Content can be taken as Norm (needs contextualising)For example, comment“The interviewer has his hands in front of him, which indicates thathe is concentrating and not fidgeting...”.P5 and P10 stressed that inexperienced users may see a commentin isolation and believe it would be valid in all situationsIt was suggested that short comments could be augmented withcontextual information to assist the assessment of the credibilityof the different viewpoints
  • Semantic Nudges: Potential forinformal 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”)
  • Nudges need contextualising, explicit viewpoints•Contextualise prompts – social contentcontext , user interaction history, interactionfocus•Elicit viewpoints and make it explicit•Different prompts (e.g. complimentary)•Different strategy for signposting
  • 1. Learning context:– Informal Learning is important– Social spaces and user generated content offer new opportunities2. Technology:– Nudges to empower exploration– Semantics is a promising technique for implementing nudges (“Semantic Nudges”)Thank You!Dr Dhaval Thakker, Research Fellow, University of LeedsD.Thakker@leeds.ac.ukhttp://www.imreal-project.eu/