This document discusses analyzing learning interactions in digital learning ecosystems based on learning activity streams. It introduces three generations of technology-enhanced learning systems and describes how a digital learning ecosystem called Dippler uses an activity stream approach adapted from the TinCan API to log learner interactions. The document proposes using the Uptake framework to conduct sequential analysis of learning interactions across activities, media, time and space. Future research plans include integrating Dippler with the TinCan API, adapting the activity stream for uptake analysis, and developing analytic tools to empirically validate this approach.

1. 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

2. Mobile communication generations

3. S-curve of Moodle: the end of LMS era?

4. 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

5. 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

6. 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

7. 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)

8. 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

9. 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

10. 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

11. 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