Towards educational cloudMart Laanpere, head of the Centre for Educational Technology

Current situation in Estonia

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)

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

Innovative pedagogical scenarios

Majority of available digital learning resources follow the conservative pedagogy

Innovative pedagogical scenarios from LEARNMIX project (re-conceptualizing e-textbook): Flipped classroom Project-based learning Problem-based learning Inquiry-based learning Game-based learning

Http://learnmix.tlu.ee

Educational cloud: interoperable services

Content

Core: ID,users, rights

SIS

Administrative

Learning

Koolielu.ee

TAAT.edu.ee OAuth

eKool.eu,Stuudium

EHIS

EIS…

DLR Cloud

BYOD PLE Moodle Eliademy

…… …

Digital Learning Resource cloud

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