Date post: | 19-Oct-2014 |
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Analysing Learning Interactions in Digital
Learning Ecosystems based on Learning
Activity StreamsMaka 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 of TEL 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
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