This demonstration will present a system, called I-CAW (Intelligent Content Assembly Workbench), which aggregates content from social spaces into a semantically-enriched data browser to promote informal learning. The work pioneers a new way to interact with social content using nudges (in the form of signposts and prompts) that exploit ontologies and semantically augmented content. The demonstration will offer explanation on the underpinning ontology, how cultural variations are handled, possible application domains and hands-on experience with I-CAW which has been populated with culturally-rich social content for learners wishing to explore variations in interpreting body language for interpersonal communications

Dimitrova, V

Lau, LMS

Thakker, D

White Rose Research Online

This is a repository copy of I-CAW: intelligent data browser for informal learning on culturalvariations in interpersonal communications..White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/80232/Proceedings Paper:Thakker, D, Dimitrova, V and Lau, LMS (2013) I-CAW: intelligent data browser for informal learning on cultural variations in interpersonal communications. In: The Eighth European Conference on Technology Enhanced Learning (EC-TEL 2013). Workshop on Culturally-aware Technology Enhanced Learning, 17 Sep 2013, Paphos, Cyprus. . (Unpublished) eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. I-CAW: Intelligent Data Browser for Informal Learning on Cultural Variations in Interpersonal Communications  Dhavalkumar Thakker, Vania Dimitrova, Lydia Lau {D.Thakker, V.G.Dimitrova,L.M.S.Lau}@leeds.ac.uk;  University of Leeds, Leeds LS2 9JT, UK Abstract. This demonstration will present a system, called I-CAW (Intelligent Content Assembly Workbench), which aggregates content from social spaces into a semantically-enriched data browser to promote informal learning. The work pioneers a new way to interact with social content using nudges (in the form of signposts and prompts) that exploit ontologies and semantically aug-mented content. The demonstration will offer explanation on the underpinning ontology, how cultural variations are handled, possible application domains and hands-on experience with I-CAW which has been populated with culturally-rich social content for learners wishing to explore variations in interpreting body language for interpersonal communications. Keywords: Semantic Data Browser, Multicultural, Interpersonal Communica-tions, Adult Informal Learning 1 Introduction Trends and predictions (e.g. [1]) point out that social media will have a strong impact on learning in workplace, providing a huge resource for learning that may be un-planned and driven by circumstances – i.e. informal learning. With real life experi-ences frequently captured and reflected in social platforms, these user generated con-tent can be particularly appealing as a source for informal learning of soft skills (e.g. communicating, planning, managing, advising, negotiating and so on which are high-ly demanded in the 21st century [2]).     In today’s increasingly global society, cultural awareness is a key factor in effective interpersonal communication and is particularly well-suited for informal learning. This demonstration presents a tool I-CAW (Intelligent Content Assembly workbench) which has been developed to use a culturally enriched and activity driven ontology to (i) semantically annotate relevant user generated content which are assembled from social media, (ii) scaffold the navigational paths for a user to explore the content in a meaningful fashion, and (iii) provide semantically-driven ‘nudges’ for a user to bene-fit from informal learning. 2 Brief Description of I-CAW I-CAW is a semantic data browser for learning which combines key semantic tech-nologies - semantic augmentation, semantic query, relatedness, similarity, entity summarisation, and ontology summarisation. Fig. 1 gives the main components.   Figure 1.  Main components of the Intelligent Content Assembly Workbench (I-CAW).  Digital Traces Collection. I-CAW supports users to browse user generated con-tent including videos and comments from YouTube (linked within I-CAW) and per-sonal stories. The digital traces are collected from the open social spaces (e.g. YouTube) and the closed social spaces (such as blog-like story telling environment).  Ontological Underpinning. An Activity Model Ontology (AMOn)1 is developed by a multi-disciplinary team of computer scientists and social scientists [3]. The onto-logical underpinning for aggregation of content in I-CAW utilises AMOn, DBPedia and public ontologies. The ontologies are used by intelligent services for semantic augmentation, query and for designing semantic nudges as described below. Semantic Augmentation and Query2. Unstructured or semi-structured user gen-erated content is semantically tagged with ontology concepts, which is implemented using the General Architecture for Text Engineering (GATE). Semantic indexing is developed using semantic repositories converting the annotation sets to RDF triples. The mechanism for querying and browsing the semantically augmented content takes a focus concept (Cf) and outputs other concepts and content using a relatedness algo-rithm (deriving concepts linked via properties with Cf).  Semantic Nudges. I-CAW proactively suggests areas of exploration to learners in the form of nudges based on Sunstein and Thaler’s choice architecture [4]. A nudge is any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options, and tries to influence choices in a way that will make choosers better off. In our analysis for mapping the choice architecture to se-mantic technologies, we narrow down to two elements of the choice architectures that are possible to be implemented using semantic technologies. These two elements are mapped to signposting and prompts and are the ‘semantically-driven nudges’ in I-CAW. Signposting are related to “default options” which usually most users will end up with. The semantic data browsers generally have information on a focus concept with a list of known facts from ontological knowledge bases. The presentation (sign-posting) of these facts influences the navigational path that learners take and facts                                                           1 http://imash.leeds.ac.uk/ontology/amon/ 2 Details on services : http://imash.leeds.ac.uk/imreal/wp3/#services they can read. “All Facts” show all the facts about a concept from the ontologies. “Key Facts”  give a summary of a focus concept with fewer facts, and yet containing sufficient information for learners to quickly identify the concept. This provides im-mediate exploration space, generated using entity summarisation techniques [5]. Prompts provide non-invasive suggestions based on similar and/or contradictory learning objects. Similarity prompts use the subsumption relations, e.g. if the user explores content on ‘nervousness’, which is a negative emotion, a similarity prompt directs to content linked to other negative emotion like ‘anxiousness’. Contradictory prompts are based on finding mutually disjoint concepts, e.g. if the user explores con-tent on ‘aggression’, they can be suggested ‘empathy’ as positive and negative emo-tions are disjoint.  Browsing and interaction. I-CAW provides interface that incorporate the services and nudges and allows learners to search and browse relevant digital traces for inter-personal communications. The demonstration3 will offer hands on experience with I-CAW (an example screen shot is given in Fig. 2).    Fig. 2. Sample interaction screen - exploring the concept ‘nervous’ using signposting nudges. 3 Key Contribution I-CAW illustrates an original knowledge management approach that exploits semantic web technologies to meet the demands of an important domain – informal learning.                                                           3 Demo screen cast available at: http://vimeo.com/user8051533/icawdemo Semantic data browsers that combine semantically augmented data and ontological knowledge bases are being utilised in various domains (a recent review is given in [6]). 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. who may not be familiar with the conceptual space in the domain and may be unable to decide what is the best course of action for him-self/herself. Hence, directly adopting semantic web browsers in learning contexts would not be sufficient for effective learning environments – new intelligent tech-niques are needed to extend these browsers with features that facilitate informal learn-ing. I-CAW presents a novel approach to extend semantic browsers with nudges in order to influence the choices users make and benefit learning.      It is acknowledged that this approach requires further consideration in terms of quality control of the content and what kind of information should be made transpar-ent to the users (e.g. source of information).  Acknowledgements. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no ICT 257831 (ImREAL project).  References 1. Redecker, C., Ala-Mutka, K., Punie, Y: Learning 2.0 - The Impact of Social Media on Learning in Europe, Policy Brief, European Commission, Joint Research Centre, (2010). 2. Ananiadou, K., Claro, M.: 21st Century Skills and Competences for New Millennium Learners in OECD Countries. OECD Education Working Papers, No. 41 (2009). 3. Thakker, D., Dimitrova, V., Lau, L., Karanasios, S., Yang-Turner, F: A Priori Ontology Modularisation in Ill-defined Domains. In: I-Semantics 2011, pp.167-170  (2011). 4. Sunstein, C., Thaler, R: Nudge: Improving Decisions about Health, Wealth, and Happi-ness. New York: Penguin Books (2009). 5. Cheng, G.,  Tran, T.,  Qu, Y: RELIN: Relatedness and Informativeness-Based Centrality for Entity Summarization. ISWC  2011, pp. 114-129 (2011). 6. Popov, I.O., Schraefel, M.,Hall, W., Shadbolt, N: Connecting the Dots: A Multi-pivot Ap-proach to Data Exploration. International Semantic Web Conference. pp.553-568 (2011). 

I-CAW: intelligent data browser for informal learning on cultural variations in interpersonal communications.

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I-CAW: intelligent data browser for informal learning on cultural variations in interpersonal communications.

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