#LAK11 analyse your learner/worker interactions with SNAPP to increase #elearning outputs, pedagogy and #performance

Many of us use learning management systems, and some of us even track students to support learning processes, but let's face it: tracking does not always result in clear visualization of the interactivity that is taken place amongst learners. Here is where SNAPP comes in.

There are other visual analyzing tools, but SNAPP is simple to use and easy to install, which is why I really like it.

SNAPP is developed by the University of Wollongong (who I think are coming up with a great deal of interesting educational innovations and thoughts). SNAPP allows you to transcript interactions in Moodle, Blackboard (and webCT, Desire2Learn into maps that immediately show who is a hyperconnected learner or participant, and who is more of a lurker, or even to pinpoint learners that at the core of the interactions.

SNAPP is a software tool that allows users to visualize the network of interactions resulting from discussion forum posts and replies. The network visualisations of forum interactions provide an opportunity for teachers to rapidly identify patterns of user behaviour – at any stage of course progression. SNAPP has been developed to extract all user interactions from various commercial and open source learning management systems (LMS) such as BlackBoard (including the former WebCT), and Moodle. SNAPP is compatible for both Mac and PC users and operates in Internet Explorer, Firefox and Safari.

By mapping interactions, you have a better overview of what people are doing and how they learn/perform. As such I see two fields of interest: education/training and knowledge management in corporations.

Education and training benefits
you can localize students or learners that are at risk (of not understanding, of having decreasingly less time...). The SNAPP-people have listed some great benefits of network diagrams:
* identify disconnected (at risk) students;
* identify key information brokers within your class;
* identify potentially high and low performing students so you can plan interventions before you even mark their work;
* indicate the extent to which a learning community is developing in your class;
* provide you with a “before and after” snapshot of what kinds of interactions happened before and after you intervened/changed your learning activity design (useful to see what effect your changes have had on student interactions and for demonstrating reflective teaching practice e.g. through a teaching portfolio)
* allow your students to benchmark their performance without the need for marking.

SNAPP also allows you to pinpoint lurkers (who might be a good target audience for research, e.g. did they get something out of the course, and why did they only lurk?
Additionally, you can filter out those students that might be good future facilitators. For students that are in the middle of the interactions, are clearly motivated and strong in reaching out and communicating.
Another educational benefit is for looking at peer-2-peer interactions. It might be that you do not get much feedback from a student, but after analyzing the interactions, you might see that that same student is a real peer-2-peer knowledge node, and as such has great course value.

Performance and knowledge management benefits
This SNAPP approach can also be used for knowledge management. Let's say you have a internal, central 'help' forum inside your company. And there is a person responsible for giving help on that forum. It might well be that after you screen the forum with SNAPP, you can see that someone else in your organization offers just as much help or support, just because they know the subject. As such you can give a bonus, or a different function to that active employee and stimulate knowledge transfer in your organization.

Interpretation of visual network maps
SNAPP does not simply provide the software, they also give an overview of how to interpret the visual data that you get from analyzing the network interactions.
So how does it work?
You download SNAPP and you get cracking with it!
I will take Moodle as an example:
Moodle

1. Visit a Forum in Moodle. Ensure that “Display replies in nested format” is selected.
2. Click on the SNAPP Bookmarklet (from where you placed it in the Bookmarks menu in Firefox or the Favorites menu in IE). You may need to enable the Menu bar in IE 7, to view the Favorites menu. The SNAPP interface will be inserted at the bottom of the page.
3. The SNAPP interface consists of five tabs: Visualization, Statistics, Export, Help and Credits. Within the Export tab, social graph data can be saved as GraphML or to the .vna format. The .vna format can be opened in NetDraw for additional analysis and visualization.

What will you see on your screen: the program will go through all the threads, one-by-one (you see the screen flip from one thread to another), and eventually a Java-icon will be displayed. You just wait and the graphic will appear, together with 5 tabs.

Netdraw - also a free tool developed by Steve Borgatti a nice bearded bowling professor in Kentucky, allows you to fine-tune the graphs you get with SNAPP.

The picture depicted here is a representation of a welcoming forum in Moodle.

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#lak11 getting #learning #analytics on track demands interdisciplinary teams and connectedness

Grasping all the factors of learning analytics is at the core of LAK11 course. In the first week John Fritz took the mic of the Elluminate session (you can see the recording here) and got all the LAK11 participants introduced to learning analythics.

In order for learning analytics to take off, strategies must be in place that clearly recommend teacher or trainer interventions to pick-up those learners who are at risk of dropping out of courses or trainings.

Need for interdisciplinary teams
While listening to his presentation, I immediately got the idea that the mere fact of learning analytics is interdisciplinary. You cannot analyze learner metrics and hope to take educational action, if the educational intervention team is not connecting different fields together.

John mentioned in this presentation that the biggest question for learning analytics is: how do we move on, where do we go from here? John mentioned that most educational and training institutes are still at the analysis stage, but they do not move on to an intervention stage to get their learners on track. I think this is part of the fact that within institutions interdisciplinary teams are scarce. Learning analytics demands more than the data, it demands true understanding of learning theories, of sociology, of instructional design, of psychology, IT (if not more). So if an institute wants to cross the barrier of mere analyzing their students, they will have to build a framework, or at least see if framework builders are willing to work together in their institutes.

Is there such a thing as zone of proximal development for researcher teams?
I feel that if a new research that expands across many fields of interest has any chance of succeeding, it must connect experts from all of these fields. That is easier said then done, as the diversity of fields might put the experts so far away, that there is no common ground on which to stand and understand themselves. So, getting together a mix of disciplines is not enough. In a way all of these experts must be within their own zones of proximal development, their knowledge must be close enough to be able to link to the newly to construct knowledge in an understandable way.
If an interdisciplinary task-force to work on learning analytics and its strategies needs to be put together, I think the profiles of this team must be overlapping just enough to allow cross-understanding.

Self-regulated learning
John also talked about giving the learners the tools to adjust their learning. This is a huge meta-learning challenge, for it demands that learners understand many of the factors that influence their learning, which seems difficult to me. Nevertheless, what John says about this is interesting. It might help some students, but at the same time I feel that if even the researchers do not come up with strong solutions, how can we put most of the burden on the learners? (but I do agree with John that learners must also take responsibility for their learning). John mentions a great resource for research on self-regulated learning: Zimmerman.

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#LAK11 what about #privacy when using #data analytics to create #knowledge?

Learning Analytics: EDUCAUSE

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LAK11, the course on learning analytics and knowledge has started and immediately discussions touch a sensitive and complex issue: privacy. While talking about educational data mining, the issue of privacy was raised by many participants.

First a quick note on educational data mining: data mining, also called Knowledge Discovery in Databases (KDD), is the field of discovering novel and potentially useful information from large amounts of data. And with many social media apps gathering massive amounts of data, this field is of growing interests to companies as well as researchers. Educational data mining has a different aim, not to make money, but to map/increase/analyse educational knowledge creation and awareness. What makes educational data mining so interesting, is that it is increasingly using models drawn from the psychometrics literature in education.

The idea behind EDM is a great one: modeling student individual differences in these areas enables software to respond to those individual differences, significantly improving student learning for example. Another key area of application of EDM methods has been in studying pedagogical support (both in learning software, and in other domains, such as collaborative learning behaviors), towards discovering which types of pedagogical support are most effective, either overall or for different groups of students or in different situations. The only thing is, how do you cope with the privacy issues?

Privacy can be good or bad
Gathered data – just like anything else – can be used for the good or the bad. An early form of data gathering was getting all the Jews registered in Germany. Once that was done – for purely administrative purposes at first – it was a small step to gather the Jews themselves for … processing. There is also a recent initiative: in Nepal the third sex has been added to Nepal's next demographic surveys. This is a wonderful thing (I for one do not apply for many 'either/or' categories), but it makes abuse possible.

To me privacy is more relevant to minorities and vulnerable groups, then any other groups in society. If facebook sells gay related data to the Ugandan government, I would not like to be a gay person in Uganda, for you risk being seriously harassed if not killed (at this point in time). No matter when, privacy should be an option that is respected, disconnected from your own online identity, certainly you – as a user – should be able to use a ‘non-traceable’ account for certain reasons?

For instance by using disconnect(ere).

Out of the crowdsourcing cloud, out of the real world?
A way forward to keep your own data private is via: disconnect. But then if all of the people from a specific group use disconnect, their data will no longer be in the crowd, and as such 'non-existent', which makes vulnerable groups even more invisible and less catered to.

perspective of a LAK11 participant
Sarah Haavind, a participant of LAK11, mentions two interesting points on privacy as well:
First: the FB and twitter users no longer clutch to privacy (is this really true and has anyone researched this or the reasons behind it? I do mind and I adjust my privacy settings)
Secondly she adds to the privacy-commercial link: “What if the data-mining marketers know what I like? Isn't it a positive to be bombarded with ads for products I'm interested in rather than a random assortment, given their presence in our lives?”.
She has a point there, but… how can small, very targeted businesses come up through the data analytics that is available?
If I had to choose, I would also like smaller companies to be able to take part in this ad-data-world. I am not interested in starbucks, I am interested in the local coffeeshop burning its own coffee beans, just because I like a variety of tastes. If I go to Ethiopia, I want to listen to local contemporary music, I am not interested in the hashed music that can be found everywhere. So to that effect, I would like it better if I could choose localized data from my profile of ‘local business’ then the big mainstream stuff. But do I need to open up my private life to get access to such time-saving and potentially interesting stuff?

Having said this, gathering and mining educational data might help us a long way to get quick access to some sustainable outcomes (learner preferences, learner critical thinking skills…). But of course, like all data mining, the proof is in the algorithm. If the algorithm is based on a wrong hypothesis, will the results then still be useful? And if data is sold to the highest bidder, then the ethics of that highest bidder better be in sync with my interpretation of ethics.

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