Presentaion at Estonian-Finnish workshop on educational cloud services

1. 
Towards educational cloud
Mart Laanpere, head of the Centre for Educational Technology

2. Current situation
 Koolielu.ee (since 2009): repository of teacher-created learning
resources, more than half of Estonian teacher are registered
users, QA (subject moderators and QA checklist)
 LeMill.net: 50K users, 70K learning resources, shutting down
 Digital Exams: EIS prototype was received with mixed feelings
 Textbook publishers are experimenting with various e-textbook
formats (ePub, Web-based, apps, eLessons, LCMS)
 Majority of digital learning resources are scattered around Web
2.0 (blogs, wikis, LearningApps, Khan Academy, Kahoot, Weebly,
HotPotatoes etc)

3. Unsolved issues
 Scaling up the use, re-mix and re-use, interoperability
 Metadata collection from various repos
 LO quality assurance, curriculum coverage
 Teachers want to use hundreds different authoring tools
 Majority of UG content is hidden, locked and hard to find
 IPR violations, combining proprietary content and OER
 Supporting innovative pedagogical scenarios

4. Innovative pedagogical scenarios
 LEARNMIX project (re-conceptualizing e-textbook):
 Flipped classroom
 Project-based learning
 Problem-based learning
 Inquiry-based learning
 Game-based learning
Http://learnmix.tlu.ee

5. Educational cloud: interoperable services
Learning
Content
Administrative
SIS
Core: ID,
users, rights
Koolielu.ee
TAAT.edu.ee OAuth
eKool.eu,
Stuudium
EHIS
EIS
…
DLR Cloud
BYOD PLE Moodle Eliademy
…
… …

6. Digital Learning Resource cloud

7. DLR cloud: goals
 Metadata harvesting:
 Automatic, every 24 hrs from multiple repositories (incl. Finnish)
 Content provider responsible for interfacing and metadata quality
 Creating collections from DLR:
 Powerful metadata-based search and recommendation
 Collections created by teachers for students, for learners
 Shareable on multiple end-user platforms
 Learning analytics:
 Tracking the activities of users (TinCan API, LRS)
 Recommender systems