geistlogistic

Please find here my presentation for the MUPPLE-09 workshops at the ECTEL 2009, Nice, organised by Marco Kalz, Daniel Müller, Matthias Palmer & Fridolin Wild.

You can find the text of the paper here.

Besides finalising my PhD thesis in A5 format I submitted one article to ICL 2009 conference and another one to the EC-TEL conference. I just received a message that my article for the ICL is accepted. That’s great because I never been to the ICL and I’m curious about the community that meets every year in Villach Austria.

Here you can find a description of the Special Track on ‘MashUps for Learning (MASHL09)‘ where I got the paper about the usability evaluation of ReMashed accepted. The track is organised by Sandra Schaffert and Martin Ebner.

Yesterday, I gave a presentation on the latest developments on ReMashed for the community building cluster within CELSTEC.

The cluster members were interested in the data set created so far by ReMashed to explore how to recommend persons in informal environments based on their competences. Therefore, the new competence fields in the personal profile of the learners are quite suitable. Further, we discussed the need to create a widget / portlet interface for ReMashed to integrate it into personal environments like iGoogle and Netvibes but also into Liferay or Moodle.

Following up on this, we talked about the need create an administration layer above the current systems to create different instances of ReMashed for various online communities. From here we stumbled into a ‘privacy issue / suitability of information’ discussion for different communities.

This time we decided to offer a qualitative evaluation of the ReMashed system. Therefore, we ask the contributor of the week Dr. Wolfgang Greller (best rated item in cat.: bookmark, picture and blog posting) for a short interview regarding his experiences with ReMashed. Wolfgang is e-learning Manager at the CELSTEC institute. He is involved in various European projects and blogs recently about developments in Technology-Enhanced Learning. He runs the web sites Wolfgangs e-learning space.

How satisfied are you with the ReMashed system?
What I like about ReMashed is the creation of cumulative data that is related to my topics. It is interesting to follow the contribution of the other users in the various web 2.0 channels. I especially like to have recommendations on top of the sea of information as they point me to information I might missed or even remind me on things I find interesting.

I like the way how ReMashed combines different data sources data sources that makes it different to other system. In case it would offer only text based information it would be to overwhelming. The pictures and slides make the system more attractive.

This time we decided to offer a qualitative evaluation of the ReMashed system. Therefore, we ask the contributor of the week Dr. Wolfgang Greller (best rated item in cat.: bookmark, picture and blog posting) for a short interview regarding his experiences with ReMashed. Wolfgang is e-learning Manager at the CELSTEC institute. He is involved in various European projects and blogs recently about developments in Technology-Enhanced Learning. He runs the web sites Wolfgangs e-learning space.

How satisfied are you with the ReMashed system?
What I like about ReMashed is the creation of cumulative data that is related to my topics. It is interesting to follow the contribution of the other users in the various web 2.0 channels. I especially like to have recommendations on top of the sea of information as they point me to information I might missed or even remind me on things I find interesting.

I like the way how ReMashed combines different data sources data sources that makes it different to other system. In case it would offer only text based information it would be to overwhelming. The pictures and slides make the system more attractive.  I’m looking forward to additional media integration.

Sounds like you are quite satisfied, any improvements?
Yes, of course. Just taking the picture example, I would suggest using not only specific services like flickr rather than combine different picture services  like flickr and picassa in one picture category.
I observed that there are high and low level picks for the different services. Regarding the pictures it seems that people upload pictures when they attend conferences or other events. In this time you get a whole bundle of new pictures. In normal seasons the picture section relative quite. Regarding this peak times I wish to have more control over the box size for instance through expand or minimizing them.
Regarding the delicious links, I miss comments to the links. Otherwise it is hard for me to judge how I should rate an item. Thus more information and transparency would be great. Further, I would like to add source that are not written by me but might be relevant for the community. It would be great if there would be an option to add resources I regularly reed on the web.

What is your personal information strategy for ReMashed, how do you use the system?
Well, ReMashed is like the online newspaper of the emerged community. I do not have all participants in my Google reader but I like their contributions. My strategy is to look first for new contributions, afterwards I check if I’m still top of the week  and finally I check the rating- based and afterwards the tag-based recommendations.

How do you rate?
Actually I only rate between 3 and 5 stars. I do not use 1 or 2 star ratings instead of that I would prefer a negative rating (a black star).

What is a 5 star rating for you?
I give a 5 star rating to information that is really new for me, something that as an AHA effect attached to it.  There is not a specific topic connect to my 5 star rating. I rate fun stuff also with 5 stars like industry news. I also use 5 star rating if I want to share the item with others thus to make them prominent.

When do you use a 3 star rating than?
For things I find interesting and want to know more about. 3 stars also serve as bookmark for things I want to discover later on.

Would a bipolar rating like thumb up / down efficient for you?
Actually not, because I would prefer 3 positive stars and one thumb down rating for items I don’t like. That would serve my rating behavior.

There are two things regarding the rating:

1. I would like to have an overview of the ratings I already gave to items. Something like a personal view on rated items.
2. I would like to see an average rating of the community or the amount of users that rated an item.

How does the tag- and rating-based algorithm serve you in the beginning compared to the current situation. Did you recognize any differences?
From the very beginning the tag-based recommendations were really efficient for me. They immediately showed sources of other person that are related to my interests. At the moment they become a bit less relevant. Related to that the rating-based recommendations were not so related in the beginning, but now they seem to work better and actually I like them now more than the tag -based recommendations. Therefore, the tag based recommendations could get maybe a new functionality.

Regarding a new functionality we currently consider to use specific learning goals to recommend items that support users in their competence development. We described in that in the last posting [you can find it here]. Can you imagine to rate items regarding your competence development goals?
Well, for me such a rating mechanism has to be as informal as possible because I use the system for pleasure. Thus, it should be free of policies or specific behavior model I have to follow to get pedagogic sound recommendations.  I could imagine defining specific goals with keywords or a tag cloud. This specification could influence the tag-based recommendation approach. It could recommend items that serve this learning goal. Through the rating of the items they can be weighted and become more or les important in the tag-based recommendation approach.

Like a combination of tag- and rating-based recommendation.
Yes something like that.

Many thanks for your time and for the interview.
It was a pleasure for me.

Latest stats

• 40 users are registered (+ 25%)
• 4261 items are available in the system (+ 26%)
• 220 items are rated (+ 71%)
• 713 recommendations are offered (+ 38%)

Those are great numbers ReMashers!

Latest News

Luckily David Wiley visited us at OUNL, Marco and I had the opportunity to talk to him about the Open Content issues. We also presented to him the ideas behind ReMashed and further development plans. David was pretty excited about the system and compared it with their OCW finder. We were happy to find him a day after our meeting as a member of ReMashed.

Other observations

The rating based algorithm seems to come to a threshold that makes its recommendation more reasonable. ReMashed user have now the opportunity to experience how the algorithm works in small communities. For example, I get now recommendations for items I never rated but my neighbors did. As five ReMashed users sitting in the famous ApeCage at OUNL we share our experiences with the system with each other. I complained about one rating-based recommendation I received and was wondering who could have rated such an item. My colleague Marco told me that he rated it just for fun and now I have to live with this recommendation. Arrgh! (Reminds me to add the rating of recommended items functionality to the system).

More room for improvement

I got various requests how people can rate an item that is recommended to them when they like it. As I said we are working on that update but it will come with different other updates. For now you have to follow the recommended item to its original source, tag it in delicious and rate it afterwards when it appears in ReMashed (Uhhh bad user interaction). In a future future release we have to create the possibility to add sources from ReMashed directly to delicious we added it to the ToDo list.

Export recommendations via RSS

Another request is the export of the recommendations via RSS. Three people already asked if they can subscribe via RSS to the recommendations of ReMashed. That’s a nice and important idea that we will keep in mind for future development and also added it to the ToDo list.

Future development

Talking about future development, I can present an initial mock-up of the next release were users can specify additional Web2.0 sources (twitter & Youtube) and define interests fields (learning goals) with a self-assessment slider. The interests fields will be taken into account for future recommendation regarding personal competence development. The idea is that a user can explicitly specify 1 to 3 interest fields (learning goals) and his related competence level between 1-5.  An additional recommendation algorithm will be triggered by this goals and present relevant items for future competence development to the user in a separated box.

As a smart edition the interest fields will be powered by an auto completion algorithm. This algorithm will be fed with learning goals of other users and tags that are available in the system. Using auto completion helps to support users to use a shared vocabulary regarding their competence development. Later on users can be grouped according to their shared learning goals. More tailored recommendations can be created through recommending resources of users that are on a higher competence level than the current user.

Using domain data sets to cover cold-start and add additional techniques

Through the current pilot we came across a new idea to create different domain data sets with rated items to cover the cold-start problem of the recommender. The running pilot is a good example of a Technology Enhanced Learning data set. If we could run different pilots in various domains we could create several domain data sets (medical, engineering, education, security etc.)  and apply them to cover the cold-start and add additional technologies like Latent Semantic Analysis.

If anyone is interesting in cooperating with us in additional ReMashed pilots don’t hesitate to contact us.

The ReMashed evaluation runs now for almost one week. Time for first reflections:

Some descriptive statistics:

• 32 users are registered from Japan, USA, Netherlands, Estonia, Canada, Austria, Germany and Sapin (people still start joining )
• 3363 items are available in ReMashed
• 128 items are rated so far (only! a real cold-start)
• 501 recommendations are offered.

First experiences:

A nice effect of ReMashed is the recommendation of ‘older’ information that is realted to my current information needs. Whenever new people sign up to the system my personal profile in ReMashed gets sources recommended that they created in the past but that fit to my current information needs.
For my own evaluation I use a developer, my own and a educational account to assess the recommendation algorithms. Each of the accounts receives tailored recommendation from the system regarding their interests. Thus, the personalization of the sources works fine. For the next release I want to integrate competence level of users and offer tailored recommendations with a context-aware recommendation algorithm.

Also interesting is the frequency of use of particular services. Most updates happen over delicious and blogs. Slideshare and flickr are used less. I’m planning to weight blog posting and slideshare contributions to make them more important for the recommendation technology.  Blog posting and slides from slideshare are less frequently used but they offer a higher quality contributions as people put a lot of effort into them. We still believe that people blog about topics they are interested in whereas they also tag information on delicious which are not part of their competence development profile. In order to create a recommender system (service) for informal learning environments these sources have to be weighted stronger to prevent that they are neglected by the overwhelming contributions of delicious bookmarks.

Many thanks to the community around ReMashed! Please keep the spirit and forward any idea or comment regarding ReMashed to me. You really help to improve the system and the underlying recommendation technologies.

The most frequent questions are:

Should I rate my own contribution?
Sounds strange but yes, please rate your own contribution to explain to the rating based algorithm that these are the topics you are interested in. At the moment ReMashed still suffers the cold-start problem of recommender systems. Thus, there are too few ratings in the system to make sufficient rating based recommendations. However, this is why we decided to go for a hybrid recommendation strategy and use the tags of the contributions as a second  recommendation technique. This 2nd technique (the tag based recommendation approach) works quite adequate. It looks like the tag based recommendation approach does a good job in covering the cold-start of the rating based algorithm. Maybe I should implement a threshold of ratings before I enable the rating based algorithm.

For the user of ReMashed please rate as much as possible! Also when you enjoy a information only a bit give it 1 or 2 star rating to feed the rating based algorithm to offer more relevant recommendations.

I miss the possibility to rate the recommendations I get.
Yeah, me culpa you are absolutely right. I also want to have more control over the recommendations that are offered to me. In the next release there will be the possibility to rate the recommendations.

The first week gave us already a lot of ideas for the future.
Have fun using ReMashed.

It was the last Winterschool within the TENCompetence (TCWS09) project which will end in November this year. Check out the movie here! My colleagues Rob, Sebastian and Danishalready blogged about the TENCompetence Winterschool. Thus, I just want to emphasize some new learning experiences.

So in short, we had a great week with lots of new learning experiences. Most of the sessions were longer than expected because the participants continued the discussion of the presented topics.

On Monday, I gave together with my colleague Christan Glahn a session about Mash-up environments. The objective of this session was to identify potentials of the different mash-up techniques for personalized learning environments (PLEs). The session was organized in two parts. We discuss how mash-up systems are related to learner support, context-awareness and self-directed learning and recommendation strategies; and presented the  prototype mash-ups (ReScope and the ReMashed system) which focus on the support of learners in informal and self-directed learning.

A rather interesting experience was the late night session on PowerPoint karaoke. PP Karaoke is a very good training for giving presentations, because you have to stick to rules of how to present efficiently and you learn how to improvise during the session. Skills you also need for serious presentations.You can find a picture here.

There are no mandatory rules for PowerPoint Karaoke. To get the ultimate experience some principles should be respected:
•    The presenter shall NOT see the presentation slides in advance.
•    The presentation time shall be limited. We used 10 minutes as a time limit.
•    The jury shall have a sense of humor.
•    The speech must be related to the Powerpoint slides. General nonsense is not allowed.
Try it out on your next event; there is also an Open Source tool that uses the Slideshare API. http://www.slidesharetoys.com/karaoke/

My first blog posting in 2009, the year started with good news regarding my theoretical article that I submitted to the  Journal of Digital Information (JoDI) in January 2008. Now, I have 3 publication out of 4 needed to finilise my PhD project. The article with the title: ‘Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning’ is finally published in JoDI Vol 10, No 2 (2009)! The article argues why recommender systems have to be adjusted to the specific characteristics of learning in Learning Networks. It describes a number of distinctive differences for personalised recommendation to learners when compared to recommendations for consumers. Similarities and differences for informal and formal learning are discussed and used to define the recommendation goal that recommender systems in informal learning networks have to address. The article further suggests an evaluation approach for recommender systems in Learning Networks.

What I really like about this issue are the prominent Co-authors. I still remember reading the latest research findings from Tiffany Y. Tang, Gordon McCalla (I-Help System) and being inspired by Jon Dron, Terry Anderson when I was starting my PhD in 2006. Now, my article stands next to their contributions .

To my surprise, Tang and McCalla took a similar focus for their article like me. They also discuss differences for recommendations in e-learning compared to recommendations to commercial domains. They also identify pedagogical features which are necessary to make appropriate recommendations of papers to students in an e-learning domain. These pedagogical features distinguish e-learning domains from many commercial domains where the only key factor is a user’s likes and dislikes. I especially like the method they use to evaluate the pedagogy reasoning for recommendations in e-learning.

For the near future, I focus now on the submission of my final Journal paper for the thesis and I want to prepare a conference paper where I present ReMashed – Recommendations for Mashup Environments. ReMashed is a kind of technical solution coming along with my thesis. I will present ReMashed the first time next Monday at the Winterschool in Innsbruck. Afterwards I will inform you more detailed about it.

Now it’s time for bug fixing.