Analysing the Use of Distributed Digital Learning Resources: a Case Study on eSchoolbag Platform in EstoniaMart Laanpere, sen.researcher @ Centre for Educational Technology, Tallinn University

Conference on Data Science and Social Research :: Naples, 19 February, 2016

Learning Analytics “in the Wild”

Most of Learning Analytics research is conducted on the data that comes from a single closed system (e.g. Moodle, MOOC)

As the digital footprints of learners are increasingly expanding towards “the Wild” (open Web), we need Learning Analytics that is able to aggregate the data from distributed environment

National strategy for lifelong learning: Digital turn towards BYOD and digital textbooks, analytics & recommender systems

Need for Learning Analytics that is not “pedagogically neutral”, i.e. includes the metrics and indicators that are drawn from contemporary learning theories

Current situation with DLR in Estonia

Koolielu.ee (since 2009): repository of teacher-created learning resources, more than half of Estonian teacher are registered users, Quality Assurance (subject moderators and QA checklist)

LeMill.net: 42K users, 73K learning resources, getting old

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 actively used digital learning resources are scattered around Web 2.0 (blogs, wikis, LearningApps, Khan Academy, Kahoot, Weebly, HotPotatoes etc)

Towards DLR cloud: requirements for eSB

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) Indicators and metrics drawn from trialogical learning theory Recommender system

Digital Learning Resource cloud

Configurations of digital textbook 2.0

Planetary systemmodel

Linuxmodel

Legomodel

Stabilecore

Dynamic core

No core at all

Levels of textbook co-authorship

Level Learner’s contribution Examples of tools6: Creating Creates a new resource

from scratchGeoGebra, iMovie, Aurasma, PhotoStory, GarageBand, iBooksAuthor

5: Remixing Rips, mixes, cuts, adds visuals or subtitles

“Hitler gets angry” video, 9gag, samples, GeoGebra, GDocs

4: Expanding Curates, adds external resources to collection

Scoop.it, blog

3: Submitting Solves a task, submits to teacher for the feedback

Kahoot, Khan Academy, online tests, worksheets made with Gdocs

2: Interacting Self-test, simple game LearningApps, HotPotatoes, SCORM1: Annotating Likes, bookmarks,

commentsYoutube video, ePub, PDF, Web page

0: Consuming Views, listens, reads PowerPoint, PDF, video

Discussion & conclusions Learning analytics works differently in a distributed

environment, tools need adaptation

LA becomes more relevant to teachers and students if the units of analysis relate to a theory of learning (if possible, several alternative theories)

Open issues: privacy-preserving data mining, aggregating the data from state registries, research and Learning Analytics