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Analysing Learning Interactions in Digital Learning Ecosystems based on Learning Activity Streams
 

Analysing Learning Interactions in Digital Learning Ecosystems based on Learning Activity Streams

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Presentation on European Conference of Educational Research, ECER'13 Istanbul

Presentation on European Conference of Educational Research, ECER'13 Istanbul

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    Analysing Learning Interactions in Digital Learning Ecosystems based on Learning Activity Streams Analysing Learning Interactions in Digital Learning Ecosystems based on Learning Activity Streams Presentation Transcript

    • Analysing Learning Interactions in Digital Learning Ecosystems based on Learning Activity Streams Maka Eradze, Mart Laanpere:: Tallinn University, Estonia European Conference of Educational Research :: Istanbul, September 2013
    • Mobile communication generations
    • S-curve of Moodle: the end of LMS era?
    • Three generations ofTEL systems Dimension 1.generation 2.generation 3.generation Software architecture Educational software Course management systems, LMS Digital Learning Ecosystems Pedagogical foundation Bihaviorism Cognitivism Knowledge building, connectivism Content management Integrated with code Learning Objects, content packages Mash-up, remixed, user-generated Dominant affordances E-textbook, drill & practice, tests Sharing LO’s, forum discussions, quiz Reflections, collab. production, design Access Computer lab in school Home computer Everywhere – thanks to mobile devices
    • Digital Learning Ecosystem  Ecosystem (biol.) is a community of living organisms (plants, animals and microbes) in conjunction with the nonliving components of their environment (e.g. air, water, light and soil), interacting as a system. Nutricion cycle, energy flow, self-regulation  DLE is an adaptive socio-technical system consisting of mutually interacting digital agents (tools, services, content used in learning process) and communities of users (learners, facilitators, trainers, developers) together with their social, economical and cultural environment.  Every actant leaves digital traces behind in DLE, these can be used for building dynamic learner models and recommender systems
    • Dippler: a prototype of DLE Social media Blog Profile Courses Activities RSS Users Analytics Courses Widgets Institutional BOS Middleware: BackOffice Service Cloud Storage HTTP WS Types of tasks: Post Structured post Artefact (file) Discussion Self-test Test Group task Offline task All courses Featured My courses Course page Summary Course info Outcomes Announcem. Participants Groups Resources Tasks Settings Categories Learner's Wordpress with Dippler plugin Dippler: institutional client, teacher's tool IOS app: mobile client
    • Analysing learning interactions  Interactions:” reciprocal events that require at least two objects and two actions. Interactions occur when these objects and events mutually influence each other” (Wagner, 1994)  Learning interactions: an important unit of analysis in pedagogy  Three types of learning interactions: learner-content, learner-learner, learner-teacher (Moore, 1989; Anderson & Garrison, 1998)  In classroom settings: ethnographic methods, observation, coding  In LMS: educational data mining, frequency analysis, CoI (qualitative)  In PLE and social media: Social Network Analysis, tagging, CAM  Limitations: difficult to harvest, document, aggregate, automatize and scale up, often pedagogically meaningless (EDM)
    • Emerging alternatives  ActivityStrea.ms: timeline-based logs consisting of events; each event is human & machine-readable proposition consisting of actor, action verb, target and timestamp  TinCan AP, also xAPI (tincanapi.org): replacing SCORM, harvesting digital footprints of learners in distributed learning ecosystems, format similar to ActivityStreams (no restricted vocabulary for verbs), Learning Record Stores  New kind of analytics is needed: exploratory, sequential, scalable, pedagogically meaningful, theory-based
    • Uptake framework (Suthers & Rosen 2011)  Interaction is distributed across actors, media, space, and time  Sequential analysis of interactions in learning episodes  Capturing the aspects of the coherence of the mediated interaction that are not apparent in the threaded structures  Analytic program based on theoretical assumptions, intersubjective meaning-making  Uptake: when a participant takes aspects of prior events as having relevance for ongoing activity  Contingency graphs: media dependency, temporal proximity, spatial organization, semantic relatedness, inscriptional similarity
    • Implementation in Dippler  Adapted activity stream: pedagogic vocabulary added to actors, objects, verbs  Linking events and learning resources with tasks and learning outcomes  Adding semantics through domain ontology keywords (taxonomy) and user-defined tags (folksonomy)  Using native features of Wordpress: categories and tags  Not monitored: interactions that are not related with tasks
    • Future research  Building TinCan Learning Record Store for Dippler, connecting it with wider ecosystem of social media  Adapting Dippler activity stream to gain compatibility with Uptake framework  Add analytic tools (similar to Google Analytics) based on uptake framework  Empirical validation