- Mouse Movements
- Mouse Clicks
- Field Focus Events
- Change of window.location.href
- Precise timings of events
- browser viewport resize
- scroll events
- keypress events
Thursday, 29 September 2011
Wednesday, 28 September 2011
Monday, 19 September 2011
- Producing a storyboard and development of the eRaUI blog. We also discussed the comments which have been made so far on the blog.
- Jisc Usability UK meeting on the 31st of August - this proved to be very informative and gave us insights into a tool which promises to be greatly beneficial in terms of connecting developers and web application designers to persons who are knowledgeable in the field of usability. Since usability is an essential facet of eRaUI, this could prove to be a very helpful tool. The meeting also enabled us to meet other people involved in Jisc funded projects.
- Machine-learning algorithms - while at present the focus of eRaUI is on the mechanism of data-collection from clients upon which eRaUI is embedded, we also discussed some of the uses to which the data can be put once collected. There are a number of methods which could be used to organize this data and make it 'learnable' in such a way as to inform the functionality of an intelligent search client.
- User Profiling - we have already implemented some basic mechanisms in eRaUI for tracking users. A challenge is to divide users up into categories such as 'Novice', 'Expert', 'Researcher'. We discussed the respective merits of making this categorisation internal as opposed to explicit (i.e. requiring user-interaction to specify the category). We also discussed the possibility of making the categorisation customisable on a per-application basis - i.e. a business web application may wish to divide users by attributes such as 'business type' - i.e.. small, medium, global, etc. While the categorisation of users is a good starting point for matching users to content, the eventual aim is to match users to the content according to more subtle analytics which will evolve as more and more users interact with the application.
- We saw a brief demonstration of eRaUI so far - demonstrating its potential for scalable collection of user data across web applications with minimal difficulty to embed in any user interface. We also saw a working prototype of the predictive search mechanism which will aim to guide users to the content they require.
- We discussed the possibility for categories of user to evolve as the user interacts with eRaUI.
Saturday, 17 September 2011
eRaUI prototype - challenges
- Instead of sending a simple data POST to the eRaUI server, we dynamically create a 'script' tag embedding a request for a target .js file. The data is sent to the .js in the querystring of the url.
- Cookies can be handled too by means of an embedded iFrame. The iFrame creates a request for a page on the eRaUI server to which a uniquely generated id is sent by the widget. This could in theory be used to maintain user tracking across any domain in which the widget is embedded.
Sunday, 11 September 2011
- Eamonn will work on the project report (see September objective no. 1)
- Investigate the use of heat maps for user modelling.
- User groups for evaluation of eRaUI - we have decided that each test group will contain 6 members.
- Creation of web server account for the deployment of a website simulating the functionality of NaCTeM. This would require the availablity of PHP and MySQL.
- Possibility for PhD research opportunities arising out of this project were also discussed.
Thursday, 8 September 2011
The following features could be integrated into eRaUI (when running in widget form) to enhance the user experience.
- Social Networking Icons – i.e. facebook and twitter (like / tweet)
- Print Page Icon
- Contact Us
- Feedback and bug reporting capabilities
Mechanisms of Deployment:
- · Another possibility is to create a Wibiya Application – allowing eRaUI to be deployed inside Wibiya.
- · Could also feasibly be run as a Browser Extension (like the Google Toolbar) so that it could work with any website or web application.
Algorithms and methods for machine learning
Machine learning algorithms could be used to determine which features of eRaUI are being used on a website and which are redundant. Thus the application could switch features on and off according to the extent to which they are made use of. This might be particularly useful when taking into account detected User-Agent or resolving a client’s IP address to their geographic location. Machine learning algorithms could be used to determine what elements of eRaUI are best for presenting to the user.
Types of Machine Learning Algorithms:
- · Supervised Learning – less suitable because of the need for an element of human supervision.
- · Semi-Supervised Learning – same issues as Supervised Learning in terms of requiring an element of human supervision.
- · Reinforcement Learning – possible
- · Genetic Algorithms – it may be possible to allow the underlying algorithms to modify themselves in such a way as to present the most useful information to the user.
- · K-Nearest Neighbour algorithm – this is a possibility – although it is the most simplistic of all machine learning algorithms
- · Cluster Analysis – although not an algorithm in itself, this could help with the process of separating user actions in such a way as to make them amenable to certain techniques.
- · Bayesian Methods
Monday, 5 September 2011
- Write a report about August achievements and activities.
- Investigate machine learning algorithms to carry out user modelling.
- Design and implement this feature of machine learning user modelling into eRaUI prototype.
- Organise at least 3 weekly technical meetings and one monthly meeting for evaluating the implementation of machine learning for user modelling in the eRaUI prototype.
- Produce an evaluation / testing plan for eRaUI machine learning module.