Hi everyone,
After our look at the semantic web and future possibilities, week 4 of LAK11 takes a turns back practical application of analytics in current learning environments.
I've started compiling a list of tools and their potential uses in the LAK Tools wiki and encourage everyone to add tools and suggestions for their use to the wiki.
If you have ideas for the application of LAK but are unaware of a tool that can make it happen, add that as well. Maybe someone else will be able to point you to a tool that can help.
You might also try using some of the tools and add comments about what you liked/ didn't like about them. (Or write out some instructions for using some of these tools, for the technologically challenged in the group )
I have added to the wiki the tools that I usually use for learning analytics, that I think are usually underrated:
R (http://www.r-project.org/):
The wonderful statistical package that let you work with huge datasets that will choke SPSS and co. Step learning curve but very rewarding at the end.
I have used it to process information about the production and reuse of learning objects and other user generated content. Processing million of datapoints is doable in a common laptop.
R is also the language that statisticians talk when they present their newest techniques. I was able to download code shared in an article published in 2007 and use it to analyze data in my research. The list of available statistical and analytical procedures is astounding. Check the CRAN repository.
R also is able to produce very professional looking research graphs.
Creating a learning analytics module would be a great project for this community.
Cytoscape (http://www.cytoscape.org):
This is a tool original developed for biology, but it is great for other uses. It let you process (very) large networks, run analytics over them and visualize them. If you have thousand of nodes and million of connections, Cytoscape will help you to visualize it and process it.
R (http://www.r-project.org/):
The wonderful statistical package that let you work with huge datasets that will choke SPSS and co. Step learning curve but very rewarding at the end.
I have used it to process information about the production and reuse of learning objects and other user generated content. Processing million of datapoints is doable in a common laptop.
R is also the language that statisticians talk when they present their newest techniques. I was able to download code shared in an article published in 2007 and use it to analyze data in my research. The list of available statistical and analytical procedures is astounding. Check the CRAN repository.
R also is able to produce very professional looking research graphs.
Creating a learning analytics module would be a great project for this community.
Cytoscape (http://www.cytoscape.org):
This is a tool original developed for biology, but it is great for other uses. It let you process (very) large networks, run analytics over them and visualize them. If you have thousand of nodes and million of connections, Cytoscape will help you to visualize it and process it.
It's a great idea. Thanks
Hi Tanya,
Thanks for doing this, will add in comments on some this week.
I also dug back through, especially on elearnspace including visualizing information - 2003 , data visualization.
Thanks for doing this, will add in comments on some this week.
I also dug back through, especially on elearnspace including visualizing information - 2003 , data visualization.
- Guide to graphical perception,
- huge list of data visualization references
- interesting visualization of Royal Society papers archive 1665 - 2005 from Chris Harrison
- finding the right chart type for data
- pattern browser with rollover explanations for when to use
Hello everyone,
I'll state the obvious --> Spreadsheets!
Why, they are easy and everyone knows how to use them. People can extract the data related to their jobs. They understand their information best and can interpret the data for themselves.
The human being with their intent, ability to conceptualize and understanding of the context is probably the best tool out there.
I was an analyst for *many* years primarily working on govt financial systems and ERP systems for mid sized cities.
Back in the day Toad was my favorite database analytics tool
http://www.toadworld.com/
http://www.quest.com/downloads/
P.S.
Thanks for putting on this conference.
I expect many people are lurking and like me need to pick and choose their opportunities to make a meaningful contribution.
Therese
I'll state the obvious --> Spreadsheets!
Why, they are easy and everyone knows how to use them. People can extract the data related to their jobs. They understand their information best and can interpret the data for themselves.
The human being with their intent, ability to conceptualize and understanding of the context is probably the best tool out there.
I was an analyst for *many* years primarily working on govt financial systems and ERP systems for mid sized cities.
Back in the day Toad was my favorite database analytics tool
http://www.toadworld.com/
http://www.quest.com/downloads/
P.S.
Thanks for putting on this conference.
I expect many people are lurking and like me need to pick and choose their opportunities to make a meaningful contribution.
Therese
Weka, a data mining software.
Pentaho open source (business) data analysis. It is possible to extend it with R analytics.
Orange, Open source data visualization and analysis for novice and experts. Novices can get going through visual programming.
Somewhat relevant too, a page on Information Visualisation, techniques and resources on WikiEducator. The primary goal is to encourage the teaching or use of information visualization at school here in NZ, but many links reflect industry / real-life usage.
Pentaho open source (business) data analysis. It is possible to extend it with R analytics.
Orange, Open source data visualization and analysis for novice and experts. Novices can get going through visual programming.
Somewhat relevant too, a page on Information Visualisation, techniques and resources on WikiEducator. The primary goal is to encourage the teaching or use of information visualization at school here in NZ, but many links reflect industry / real-life usage.