The I-TUTOR website section Learn more has been completed with the links of references of the recently published e-book “Intelligent tutoring system: an overview“. This rich pool of resources is clustered in the following categories in order for you to find articles in topics you are interested more easily:

• Intelligent Tutoring Systems
• Cognitive models and their applications in Intelligent Tutoring Systems
• Data mining in education and training
• User profiling in Intelligent Tutoring Systems
• Instructional design and Learning Design in Intelligent Tutoring Systems

We would also like to invite all our site visitors and kind collaborators, to share with us if you have suggestions for further resources that we could include in the Learn more section. Please, leave your suggestions in the comment section for this post or by commenting the Learn more section.

Thank you in advance!

Right now the first prototype of the profiler agent has been implemented. This prototype clusters all Moodle users depending on different activities that they done/not done on the Moodle server. So, for example, it will analyse all comments made within a specific course. The analysis involves clustering the users into categories from the least active “comment’ers” to the most active, within the specific course.

There are 22 different course specific activities that are clustered and 1 non-course specific activity.

After analysing these the system then performs 3 overall analyses. One that clusters the users depending on their activity levels in all of the course specific activities, and another that clusters on the activities coming from the log, the comments made, forum discussion participation and general posts. The last one clusters users on the actual marks they have achieved.

Below is a table of the different activities that are analysed with a description.

(Updated 09/04/2013)

Reflecting upon the results the the system has produced on the training data there are a few issues or questions.

Here we are performing data mining on a separate training set than the real data. That is analogous to asking a blind man to be a goal keeper in football. Yes, he might be able to hear the shot and therefore estimate where the ball is going, but he cannot see the ball, and therefore might miss it. The system has been tested on two different Moodle installations, but there is no way of finding out if the results produced will be meaningful in the actual pilots. Then it could (and perhaps should) be argued that this is excatly why pilots are necessary. It is definitely expected that changes are needed after the pilots has run. Perhaps even throughout the pilots.

It is also clear that there are big differences between how Moodle is used by teachers. This will have an impact on the importance of different activities. The system is at the moment not changeable by teachers. A question is if it would make sense to create a profiler that would have such a feature?

A more practical problem is when the profiler should be started in a course. Obviously activities build up over time. It does not make sense to start it on day 0, because all students will look like they aren’t active. Perhaps the log activity or the users last access are more interesting in the beginning of a course. Just to give an indication of early activity. However this probably should not be used by any active alerter agent.

Any thoughts on these reflection?

Reaching the midterm in the project, the partnership published the first project newsletter to share the results achieved, among which you will find the comprehensive study about the intelligent tutoring systems (and the implications of using intelligent tutoring systems in the European scenario) as well as an open source code for a survey chatbot that you are welcome to test.

The newsletter can be viewed in html HERE.

We would be happy to hear what you think, comments are welcome and encouraged!

To download: Go here(PDF)

The I-TUTOR consortium has published the e-book “Intelligent tutoring system: an overview“. The report presents the outcomes of a large literature review and the implications of using intelligent tutoring systems in the European scenario.

The first chapter provides an historical excursus and a description of the ITS, from a pedagogical and didactical point of view. Starting from the first domains-centered ITS, to arrive to the ill-structured domains ITS, and finally to reach the actual solutions. In these new solutions, a shift can be seen from ITS that supports one to one learning and interaction to system for collaborative and social learning, from ITS for learning in tightly defined domains and educational contexts to open-ended learning in ill-defined domains across varied physical and social cultural settings and throughout the lifetime, from ITS to support knowledge acquisition to systems for knowledge construction, skills acquisition and reflexive, motivational and affective support, leaving a psychological approach to reach a more pedagogical approach. Actually, research is increasingly focusing on accessible, ubiquitous, wireless, mobile, tangible and distributed interfaces. The final aim is to develop systems that could be used widespread and on a large scale.

The second chapter analyses Cognitive Modeling and the classic modeling used by informatics designers in the ITS construction. The third chapter, data mining in education, examines potentials and constraints in the use of data mining in education, summarizing the potential they have to offer meaningful support to: students, teachers, tutors, authors, developers, researchers, and the education and training institutions in which they work and study. In the fourth chapter a literature review is presented, in order to summarizes directions and research paths in the use of user-profiling, trying to define the main topics that should be considered in future research. Finally, the fifth chapter analyses the basic elements of Instructional Design and the perspectives of the ITS research, dealing with the issue of educational design software. This kind of software allows teachers, even if they are not experts in informatics but only in technology of education, to design their didactic path with the support of software agents

The report is available as a free download in PDF. The EPUB format will be available soon.

In order to ensure quality control on project aspects and outcomes in relation to Technology Enhanced Learning (TEL) and as part of the overall evaluation and quality assurance measures a review panel composed by 3 reputed experts will be established, covering the three domains of the project: pedagogy, computer science and EU policies in the field of Technology Enhanced Learning.

I-TUTOR Call for Experts

The experts will review and evaluate the project with respect to the design, the techniques to be applied, the development stages, the outcomes, and the best practices. As a consequence they will exploit their respective expertise in the fields of

• Pedagogy, and in particular:
  • Instructional design
  • Tutoring issues
• Computer science, and in particular:
  • Design and development of web based software architectures
  • Agent based software
  • Knowledge engineering and management
  • Data mining for educational purposes
  • NLP
  • Automated reasoning
  • HCI and user profiling
• EU policies in the field of Technology Enhanced Learning.

For the description of the tasks, the application and the remuneration details please, check out the call for experts.

The dead-line for the applications is 12 September 2012!

Dear Visitor,

on behalf of the I-TUTOR Team I would like to welcome you in our project’s website!

The I-TUTOR (Intelligent Tutoring for Lifelong Learning) project started in January 2012 with the aim of developing a multi-agent based intelligent system to support online teachers, trainers, tutors and students.

The aimed intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyses and give formative assessment and feedback loop to students within the learning environment and also give inputs to tutors and teachers involved in distance learning to better their role during the process of learning.

Our work during the project includes literature and case reviews of the educational use of artificial intelligence, the design and development of the intelligent tutoring tool, and the training of tutors in order for them to pilot the tool.

At the end of the project in December 2013, a piloted and reviewed intelligent tutoring system will be available. This tool is potentially implementable in all open source platforms.

We are inviting you to follow our work! We will be posting all results and resources here on our website as well as opportunities for you to join us in our work.