Enhancing Intercultural Learning through Social and Linked Data Modeling

1. Modelling user cultural exposure utilising social and linked data Ronald Denaux, Claudia Hauff, DhavalkumarThakker, Lucia Pannese,Declan Dagger, VaniaDimitrova, and Geert-Jan Houben

2. Outline • Overview of ImREAL project • Problem: Modelling of learner's intercultural awareness to enable adaptation • Approach: interactive dialogue exploiting both Social and Linked data • Current work: evaluation • Summary

3. Immersive Reflective Experience-based Adaptive Learning http://www.imreal-project.eu/

4. Key Problem Simulated environments for learning are disconnected from the real-world Simulated Environmentfor Learning Learning experience Simulation design • Effectiveness • Engagement • Motivation • Meta-cognition • Authenticity • Diversity • Timeliness • Cost-effectiveness

5. ImREAL: The Approach User generated content can provide a source for simulator enrichment Simulated Environmentfor Learning Learning experience Simulation design • Exploits ontologies and linked data • Focuses on interpersonal communication and cultural diversity • Intelligent services for content aggregation, user and context modelling, and meta-cognitive scaffolding

6. Potential of Digital Traces User-generated content presents a vast source of digital traces of individuals’ experiences shared in social spaces Advantages • Continuously updated • Spread across different sources • High volume • Enable participation and active engagement • Authentic and unbiased by the application • Represents different communities & perspectives Domains • Disaster prevention and predictions • Transport and environment • Public services • Citizen journalism • … Great potential for informal learning not exploited (OECD, 2011)

7. Social Media Revolution Multi-disciplinary research is needed to develop- inspirational prototypes- innovative evaluations- robust technologies [Shneiderman, 2011] • Facebook reached 1 billion monthly active users [Sept 2012] Twitter has over 465 million accounts producing on average 175 million tweets per day [2012] Over 800 000 videos uploaded on YouTube every day; 10 videos uploaded any second [Aug 2012] Social media creates content as a social object. Smart analysis can result in new insight, and that has powerful value for organizations.” [Reichental, 2011] “disruptive impact” of social media on industries and social lives of people Convergent with long-term societal trends [JRC Policy brief, 2008]

8. Simulated Environmentfor Learning Project Strata Pedagogy <–> Use Cases

9. Simulated Environmentfor Learning Project Strata Affective Meta-cognitive Scaffolding Pedagogy <–> Use Cases Augmented user modelling Making Sense of Digital Traces

10. Simulated Environmentfor Learning Project Strata Affective Meta-cognitive Scaffolding Pedagogy – Use Cases Augmented user modelling Making Sense of Digital Traces Integration Framework

11. Simulated Environmentfor Learning Project Strata Affective Meta-cognitive Scaffolding Pedagogy – Use Cases Evaluation – User Trials Augmented user modelling Making Sense of Digital Traces Integration Framework

12. Outline • Overview of ImREAL project • Problem: Modelling of learner's intercultural awareness to enable adaptation • Approach: interactive dialogue exploiting both Social and Linked data • Current work: evaluation • Summary

13. Problem • In order to tailor learning, simulators need to know learner's current competencies • For ill-defined domains (e.g. intercultural awareness): • Limited use for data mining techniques • No/few ontologies available

14. Approach • Initial User Model • Visited Countries • Estimated Cultural Exposure Social Web Sensors Perico Dialogue Agent • Updated User Model • Verified Visited Countries • Enhanced Cultural Exposure Score Quiz Generator Cultural Fact Extractor AMOn+ User Profile Generator Dialogue Planner

15. Social Sensors for Location Detection = + external data sources: Claudia Hauff and Geert-Jan Houben, Placing images on the world map: a microblog-based enrichment approach, SIGIR 2012, pp. 691-700, 2012

16. Derive Knowledge Base from DBpedia

17. Distilling Intercultural Facts from DBpedia Alignment with AMOn+ Infer instances of cultural descriptors Infer where descriptors occur

18. Extracted Knowledge Base • 40K facts (OWL logical axioms) • 270 countries • 565 items of clothing • >4K items of food • 88 gestures • 159 currencies • 288 languages • 20K annotations (labels and depictions)

19. Probing and Modelling Learner’s Knowledge • Goal: Assess learner's (socio-political and intercultural) knowledge of country • Ask facts (derived) from DBpedia • Ask “trick” questions: close world around country • Mark answers based on expected truth value and add to Country-awareness Profile

20. Dialogue Agent to manage Interaction

21. Evaluation • How accurate is the learner model? • Gather learner models (CrowdFlower) • Compare to standard instrument (CQS) • How suitable are the derived facts and dialogue strategy for assessing intercultural awareness? • Ask domain experts to rate facts used in dialogue sessions. • How is the usability of the system? • Standard instrument (SUS)

22. Summary • Hybrid approach, exploiting both Social and Linked data, for bootstrapping a learner competency model regarding an ill-defined domain (intercultural awareness) • Approach for the extraction of focused (culturally-relevant) factual knowledge from DBpedia • Semantic-based user-friendly interface for interactive learner model refinement using dialogue agent