the democratization of data
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LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
The Democratization of Data
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Creative Commons
http://www.slideshare.net/ulr
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Learning Analytics
”Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”
http://www.solaresearch.org
”Learning Analytics is about collecting traces that learners leave behindAnd using those traces to improve learning”Professor Eric Duval http://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Learning Analytics
Source: Intro to Learning Analytics on LAK13 course https://www.youtube.com/watch?feature=player_embedded&v=KqETXdq68vY
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Learning Analytics – working with data and toolsDr. Grace Owen, Open Universities, Australia http://www.youtube.com/watch?v=XjcSfcahjWY, says that LA is about ”making a meaningfull difference”. She says about the challenges with size and storing: ” If we pulled one week out of learning managementsystem of data from prep students, which were 34 students, it was a tb of data, we are talking massive massive data, so the trick is: how do you get the data? And how are you getting it, is it unstructured or structured, what are you going to do with it and once you get it where are you going to put it?”
Key areas, when working with data: First to get hold of the data, storing them, then cleaning them – data is messy, then maybe integration with other datasets, analyzing them and finaly vizualisation and presentation in a format that makes sense and is understandable.
Dr. G. Owen highlights the following quistions to consider for an institution working with data: • Who owns learner-produced data?• What are the conditions under which an institution jumps data silos(i.e. blending analytics from
student information systems with social media analysis?• Who has access to the analytics an institution conducts on learners?• Or, for what matter, who owns the analytics(if it is individual student data worked through an
institutional algoritm, who owns the outcome?)
• LASI13: Who owns the data ? or better What does it mean to own the data?
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
The Democratization of Data
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
The Democratization of Data
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
General tutorials about Datavisualization
http://www.youtube.com/watch?v=T5lRLA_Vn7o
http://www.youtube.com/watch?v=YaGqOPxHFkc
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Data Analysis Tools for Dummies
http://gigaom.com/2013/01/31/data-for-dummies-5-data-analysis-tools-anyone-can-use/
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Articles on userfriendly tools e.g. Scraperwiki
http://gigaom.com/2013/05/10/scraperwiki-lets-anyone-scrape-twitter-data-without-coding/
http://blog.scraperwiki.com/2013/05/07/summarising-serendipity/
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Scraping Twitter
https://beta.scraperwiki.com/
Twitter api`s:https://dev.twitter.com/docs/using-searchhttps://dev.twitter.com/docs/streaming-apishttp://blog.scraperwiki.com/2013/05/07/summarising-serendipity/
Blogpost on using the summerize tool:
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
The Tags explorer
http://hawksey.info/tagsexplorer/http://mashe.hawksey.info/2011/10/tagsexplorer-intro/
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
The Nodexl Graph Gallery
http://nodexlgraphgallery.org/Pages/Default.aspxhttps://www.google.dk/search?source=ig&rlz=1G1ASUT_ENDK388&q=nodexl+how+to&oq=nodexl&gs_l=igoogle.1.9.0l10.5524.10257.0.16703.6.6.0.0.0.0.343.1057.3j0j1j2.6.0...0.0...1ac.1.12.igoogle.rlA5_XsFB5I
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Fusion tables Google
http://www.google.com/drive/apps.html#fusiontables
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Text Visualization Tools
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Many Eyes, IBM
http://www.youtube.com/watch?v=aAYDBZt7Xk0
http://www.youtube.com/watch?v=7ivjJs7bSVI
http://www.youtube.com/watch?v=yi7HrtCADS4
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Tableau public
http://www.tableausoftware.com/public
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Open Knowledge Foundation
http://okfn.org/opendata/
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Linkedup-project
http://linkedup-project.eu/
LASI-Aalborg, e-Learning LAB, Aalborg Universitet, 3. juni 2013 - Ulla lunde Ringtved - ulr@ucn.dk . ulr@hum.aau.dk
Linked Data
http://linkeddata.org/
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