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