Collaborative filtering depending on time

Here i would like just to report about very interesting paper i read this weak. Collaborative Filtering with Temporal Dynamic shows the approach to catch the changes about customer preferences and items over a time in order to do appropriate suggestions about future preferences. Here are my thoughts how the approach can be adopted for learning environments where customers are learners and items are learner objects or other environment actors.

Firstly i would like to mention what I've learnt from the paper

• concept drifting - temporal changes in user behaviour or item behaviour. In the scope of learning communities learners changes their preferences, ways of knowledge, favorite activities within a time. Not only learners, but learning objects may change: a lectures notes are rewritten, the collaboration environment is changed.
• three approaches that solves the concept drift problem exist:

1. instance selection - don't pay attention to irrelevant data. In this case the data considered in a particular time-frame.
2. instance weighting instances get weights according to its relevance.
3. ensemble learning - the list of predictors exists and the more successful predictors get higher weights

In the paper the ideas of two last approaches are utilized but some other biases as well are considered.

Even, according to instance weighting the past events loose their influences, the preferences of users still dependent on their past. The parts of learners profiles are changing every day but there are some points that are stable on each of us: liking to read, understanding the information presented by video better then by audio, preferences to avoid formulas.

In ensemble learning only a fraction of behaviour is considered, e.g. preferences during preparing for an exam or collaborating with student colleagues. For each user a set of fractions is needed to be considered. Considering a huge number of learners such a generation of the sets is a complicated task.

Yehuda Koren proposed the solution that considers item bias, user bias, global average and user interaction. Afterwards, the time components was added.

How it can be applied in learning? - To be continued

Reflection to SNA workshop on JTEL Summer School

From 30th of May till 5th of June i was happy to be one of the students, one of the lectures and one of the organizers of Joint European Summer School in Technology Enhanced Learning.

From my point of view the event was excellent even there are some things to change, nearly all students desire to participate again. I don't want go deep in details what is summer school and how it was as many bloggers have already done this: Annafant's Notizblog (Anna Lea Dyckhoff), Summer School Fun (Erik Duval's blog) , Pontydysgu (Graham Attwell's blog)

The other thing i want to do is the reflections of our "SNA Workshop" that we have done together with Ralf Klamma. The purpose of it was to introduce TEL people with SNA basics and explain what they can find applying applying SNA analysis for E-Learning communities.

First steps in social network analysis

View more presentations from telss09.

After presentation we asked students to calculate 3 simplest SNA centrality measures: degree centrality, betweenness centrality and closeness centrality. According to reactions of students i changed the tutorial for the workshop. Now it has many examples and easy enough for anybody to understand sometimes tough or weird mathematical stuff.

SNAWorkshop

Some of you asked me about evidences and examples of SNA applications in E-Learning world. One of the examples is excellent work from Ruth Breuer that made SNA analysis of 45000 European schools. The application called EVA and the paper work about it will appear on the EC-TEL'09


The other paper i read recently "Patterns of participation and discourse in elementary students’ computer-supported collaborative learning" from Lasse Lipponen, Marjaana Rahikainen, Jiri Lallimo and Kai Hakkarainen discusses benefits and flaws of high/low density(depends on degree centrality) in learning networks.


Within ROLE project we are analyzing media networks collaborations within learning environments. I think that in next decades there will be a lot of news to report about our achievements.