#LAK11 the #semantic web and how the intelligent curriculum will enhance our learning

This week was rather busy, developing an online mobile course that will be open to all, following LAK the open course on learning analytics and finalizing a needs assessment, on top of regular work. But! The LAK content keeps intriguing. This week's content focused on the semantic web, linked data and intelligent curriculum.

Intelligent curriculum: or how data analysis will tailor content to the learner
The topic that struck me the most in this weeks LAK-topic was the idea of an intelligent curriculum. Just imagine that all the data that you - as a learner or knowledge worker - go to, examine, read, ... are analyzed and put against the content of the rest of the Web? And just think that an algorithm is put upon that data to see which content might be of use to you, but really of use to you... that would certainly enhance in-depth learning in your field of preference.

So the screening of data would result in immediate relevant content (which affects what needs to be learned, as old/new knowledge can change rapidly). Professional learners will be able to get to relevant up-to-date information much quicker.

The benefits for learning are enormous: it would not only provide me with personalized, tailored content that fits my current hunger for knowledge, it would also allow me to stay in close contact with those who provide that information and share it amongst those who have a similar interest (if an algorithm would keep track of who has similar interests, or who is close to the content I go to). This would have an effect on my (and others) professional networks, for the professional groups will shift from a more localized network to real global networks that learn through connectivity (well, most of us do this already, but professional groups that link up will become more important and most of all attainable).

Semantic course: the course wraps itself around the learner
We - as educators or trainers - might even be in the possibility of creating a course which redesigns itself depending on the prerequisites and skills of a particular learner, thus giving her/him a much smoother learning path, without leaving the learning objective itself.

Moving away from old-fashioned classrooms divided by age, going for skills and enthousiasm classes
This approach of semantic learning might even take us out of the artificially divided classrooms. If we could cater lessons to a variety of learners, they would not be put into classes depending their age, but depending their ability to grasp what is necessary, or depending on their eagerness into specific subject matter.

Creating research proposals in a jiffy
It will also facilitate research: just imagine, that you want to launch a new project in your scientific field? You need to write a proposal, and with an algorithm (AI friend) which searches in your scientific field (e.g.) scholar.google and cross-references this to the data bank of EU/WHO or B.& B. Gates foundation for funding that was granted... you could be on your way to get a funded post-doctorate or project going with much more ease.

Corporate intake: Bert De Coutere launches use cases based on learning analytics
Bert who works at IBM and comes up with great learning strategies and educational games, launched an interesting concept which fits learning analytics. This is what he proposed to enhance learning performance: OK, I'll try one again. Does anyone else feel comfortable adding some use cases as we go along in this course?

The use case:
A young manager quarterly runs a tool to picture his social network, as part of his continuous leadership development. Data mining has shown that successful leaders in his corporation typically have 1/3 of their network amongst subordinates, 1/3 with other managers on the same level, and 1/3 higher up the chain. The tool allows the young manager to see the evolution in his network and get some action points to move it to the desired state.

The semantic web
The semantic web is growing, and it effects all of us who search for content or other things in the vast amount of digital data that is on the web.

• Semantic Web: An Introduction: http://infomesh.net/2001/swintro/
• Welcome to the Metaweb http://www.youtube.com/watch?v=TJfrNo3Z-DU&feature=player_embed (Metaweb was purchased by Google in 2010, but this is still a nice resource on dealing with ambiguity of words/text)
  
• Ray, K (2009) Web 3.0 http://vimeo.com/11529540

As the web will become more semantic, all the data that is on it will be tagged, linked, analyzed... and this should increase our feeling of the 'living web'. Which ties the semantic web into Artificial Intelligence (AI) nicely.

For those interested of hearing more on semantic technologies in learning environments, have a look at the video lecture given by Dragan Gasevic available at http://bit.ly/gfXqEF (cool guy).

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#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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#LAK11 a free and open #elearning course on #statistics starts today, join!

The open learning platform SCoPe is starting a Massive Online Course (MOOC) on Learning & Knowledge Analytics 2011 – LAK11 – is an open course that will be offered from January 10 – February 20, 2011. LAK11 serves as an introduction to the growing field of analytics in teaching, learning, training, development, and organizational knowledge.

LAK11 will address the following topics:

Week 1 (Jan 10-16): Introduction to Learning and Knowledge Analytics
Week 2 (Jan 17-23): Rise of “Big Data” and Data Scientists
Week 3 (Jan 24-30): Semantic Web, Linked Data, & Intelligent Curriculum
Week 4 (Jan 31-Feb 6): Visualization: Tools for, and examples of, Analytics
Week 5 (Feb 7-13): Organizational implementation
Week 6 (Feb 14-20): What’s next for Learning & Knowledge Analytics?

There is no fee to participate. The course will include synchronous (Elluminate) and asynchronous (blogs, moodle) interaction.

This course will be facilitated by George Siemens, Jon Dron, Dave Cormier, Tanya Elias, and Sylvia Currie.

To get started:
Please join this group: https://groups.google.com/group/LAK11/. Once the course begins, daily emails of course activity, readings, and other highlights will be sent to this group.
Course Tag: LAK11 (for tagging resources in delicious/diigo, Twitter hashtag (#lak11), and tagging blog posts)

The course syllabus and reading list will be posted in early January, 2011.

We will be using Moodle for course discussions. However, feel free to blog or participate in any forum you prefer. If you intend to blog, please send me (gsiemens (at) gmail.com) a link to your blog so I can include it in our aggregated blog list. Blogs, Moodle posts, tags, and related resources will be aggregated in the LAK11 Netvibes page.

I am in!

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