This document discusses analyzing learning flows in digital learning ecosystems. It describes digital learning ecosystems as socio-technical systems consisting of digital tools, services, content, and user communities. It proposes combining the Experience API (xAPI) specification with the Uptake Framework to record and analyze learning interactions in digital ecosystems at scale. By annotating events with pedagogical verbs and domain concepts, this approach could provide feedback to learners and teachers through uptake diagrams and contingency maps of interaction patterns. The approach is demonstrated through a concept mapping scenario and holds potential for advancing analytics of distributed and user-defined learning interactions.

1. Analyzing Learning Flows in Digital
Learning Ecosystems
Maka Eradze, Kai Pata, and Mart Laanpere :: Tallinn University, Estonia

2. Socio-technical transitions
Geels 2002

3. Mobile communication generations

4. Moodle servers: global stats

5. Three generations ofTEL systems
Dimension 1.generation 2.generation 3.generation
Software
architecture
Educational software Course management
systems
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
Analytics Only feedback for
learner
Frequency-based
usage statistics
Interaction & uptake
analytics

6. 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.
 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.

7. Dippler: a prototype 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

8. Learning interactions
 Wagner (1994): reciprocal events that require at least two
objects and two actions. Interactions occur when these objects
and events mutually influence each other
 Dyadic model of learning interactions (Moore, 1998): learner-
learner, learner-teacher and learner-content
 Equivalence theorem by Anderson & Garrison (1998): reduction
in one dyad can be compensated by increase in another
 Suthers (2011): interaction is fundamentally relational, so the
most important unit of analysis is not isolated acts, but rather
relationships between acts

9. Learning analytics: a critical view
 Most of the LA research is conducted in closed LMS context
using frequency-based statistical analysis
 Only learner-content interactions are studied, not relations
between the interactions
 Social Network Analysis (SNA) is focusing on teacher-learner
and learner-learner interactions, but neglects the aspects of
quality and dynamics in interactions
 Communities of Inquiry (CoI) approach focuses on quality and
dynamics of learning interactions, but it is not scalable

10. Sequential analysis of learning flows
 In addition to frequency-based statistics, exploratory sequential data
analysis is needed for analytics of learning flows in DLE
 In Dippler: extending Activity Streams (activitystrea.ms) vocabulary
with pedagogical Action Verbs and Objects
 TinCan API or xAPI: specification for learning technology that makes it
possible to collect data in a consistent format about the wide range of
experiences a person has (online and offline)
 Uptake Framework (Suthers & Rosen, 2011): Uptake happens when a
participant takes aspects of prior events as having relevance for
ongoing activity; UF results with contingency graphs that can visualise
media dependency, temporal proximity, spatial organization, semantic
relatedness, inscriptional similarity

11. Sample scenario
 Collaborative concept mapping: identifying the core set of
concepts for a given domain along with and their relations with
each other, using a digital concept mapping tool
 Task is connected with some key concepts in domain ontology
and also with a specific learning outcome
 Event transcript in activity stream: In Assignment 3, John adds a
relation to conceptmap12 with CMapTool at 12:30 12-07-13.
 Results: contingency maps and uptake diagrams are created and
fed back to learner and teacher, irregular patterns notified

12. Conclusions and future research
 Combining xAPI with Uptake Framework creates new
opportunities for Learning Analytics and has several advantages:
 Recording interactions in dyadic events will encompass the
processes, traces, domains; feedback loop for teachers & learners
 The relations with a domain will be identified and generalised
through semantic annotation of events and artifacts
 Enables recording of the interactions that take place in distributed
and partly user-defined digital ecosystem
 Advanced learning interaction analytics is automated and scalable
 Next steps: building xAPI Learning Record Store for Dippler and
extending it to wider ecosystem, also to the physical world