Patrick’s definition of “Congruence” is pretty similar to our definition of co-referenced actions. His explanation had to deal with the problem solving strategies:

“Congruence is the degree of adherence, by an individual, to a shared solution strategy and its implementation”

This is the knowledge that is shared, but not known to be shared.

This definition was taken from: Borovoy, R., Martin, F., and Resnick, M. (1999). Revealing common ground: Augmentation on the edges of interpretive communities. In Hoadley, C. and Roschelle, J., editors, Proceedings of the Computer Supported Collaborative Learning (CSCL), Palo Alto, California, USA. Stanford University, Lawrence Erlbaum Associates. Available from: http://llk.media.mit.edu/papers/archive/A05.HTM.

A coordinated, synchronous activity that is the result of a continued attempt to construct and maintain a shared conception of a problem.

Roschelle, J. & Teasley S.D. (1995) The construction of shared knowledge in collaborative problem solving. In C.E. O’Malley (Ed), Computer-Supported Collaborative Learning. (pp. 69-197). Berlin: Springer-Verlag

P. Dillenbourg and J. A. Self. People power: A human-computer collaborative learning system. Lecture Notes in Computer Science, 608:651–660, 1992. [url]

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This article describe the People Power system developed by the authors, which represent an early approach of human-computer collaborative learning environment (HCCL). The structure of this environment was composed by a microworld for experimenting with the electoral system and a virtual co-learner agent that was tailored to offer guidance on the model of the social distributed cognition theory. The interaction proceeded dialogically with the learner, and the learning model was rule-based. The co-learner had no hidden knowledge.

The computational model the authors developed was based on three ideas: 1. a group of subjects can be seen as a single cognitive system; 2. the reasoning and collaborating are two instances of dialogue: reasoning is a dialogue with oneself and collaborating a dialogue with a peer; 3. the social dialogue generates structures for the reflective dialogue.

I thought a bit about the possible usage of the learning companion and how to frame its support in the learning experience. Basically I thought that it should not provide interaction regulation, a kind of mirroring system as proposed by Jermann (2004). On the other hand it should not work with a guiding principle, because not all the task/problems have a singular/simple definition of the task.

As the approach I propose is tailored in the distributed cognition theory, what this agent should do is essentially to propose examples of good strategy to solve the problem. The pace and the modality at which this should happen should be triggered by the learner activity in the simulation.

Very rough at the moment. I hope it will condensate into something more concrete as soon as I complete some more readings …

Current research in Computer Supported Collaborative Learning (CSCL) is oriented towards a definition of systems able to sustain human collaboration through two main philosophies: mirroring and guiding systems [Jermann et al.2001].

The former attitude involves the detection of the user activity and the constitution of some forms of visual representation of this activity. Through this feedback offered by the system, a change of attitude of the user is expected and measured. The latter attitude, implies the existence of a cognitive theory which should relate the measured data with a model of collaboration. From here, the system should be able to address critical aspect of the interaction that need to be corrected to maximise the outcome of the user experience (i.e., [Barros and Verdejo2000]).

Both approaches present structural and actual limits. The former is biased by the way the user can understand the visual representation and if s/he possesses the ability to switch attitude using this information. The latter is strongly challenged by the lack of computer models able to compete with the complexity of human reasoning. Examples using complex AI approaches are still missing.

A third way is needed between these two approaches described above, which can propel the research on the cognitive models at first and then in the translations of these models into computer algorithms (see [Dillenbourg et al.1996]). This is one of the objective of my study. My idea is to use a light AI approach, where the intelligence is not relied to be completely in the system. On the contrary, my approach argument in distributing the intelligence in a system constituted by the users plus the computational media.

C. Chou, T. Chan, and C. Lin. Redefining the learning companion: the past, the present and the future of educational agents. Computers & Education, (40):255–269, 2003. [url]

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This paper contains a literature review of the current trends in terms of building “learning companions”, which oppose to the approach of building Intelligent Tutoring Systems (ITS). The learning companion is defined as: a computer-simulated character, which has human-like characteristics and plays a nonauthoritative role in a social learning environment. Moreover, the learning companion can be inscribed in the bigger group of educational agents: an educational agent is a kind of computational support, which enriches the social context in a social learning environment either by providing virtual participants to enhance the member multiplicity of communities or by supporting facilities to foster communication among real participants.

Interesting approaches in the development of such companions are represented by the social distributed cognition (Dillenbourg and Self, 1992), and embedding machine-learning and knowledge acquisition to the system (Tecuci & Keeling, 1999). 

‘‘Agent’’ has different meanings and definitions in different domains and communities (Les, Cumming, & Finch, 1999). In this study, agent refers to a computer-simulated character, which presents users with human-like characteristics, such as domain competence, emotions, and other personal characteristics. The characteristics can be expressed or displayed in text, graph, icons, voice, animation, multimedia, or virtual reality.

Les, J., Cumming, G., & Finch, S. (1999). Agent systems for diversity in human learning. In Proceedings ofAI-ED 99 World Conference on Artificial Intelligence in Education, Le Mans, France, 13–20.

Finally, John Perry wrote an article that every PhD student should know by hart: how to structurally procrastinate. With Nicolas, we discuss many times every day on the correct methodology for procrastinating things, and finally … a guide!

Structured procrastination means shaping the structure of the tasks one has to do in a way that exploits this fact. The list of tasks one has in mind will be ordered by importance. Tasks that seem most urgent and important are on top. But there are also worthwhile tasks to perform lower down on the list. Doing these tasks becomes a way of not doing the things higher up on the list. With this sort of appropriate task structure, the procrastinator becomes a useful citizen. Indeed, the procrastinator can even acquire, as I have, a reputation for getting a lot done.

Scholarly communication exists for the benefit of the world’s research and teaching community. Authors want to share new findings with all their colleagues, while researchers, students, and other readers want access to all of the relevant literature.

However, the traditional system of scholarly communication is not working. Libraries and their institutions worldwide can no longer keep up with the increasing volume and cost of scholarly resources. Authors communicate with only those of their peers lucky enough to be at an institution that can afford to purchase or license access to their work. Readers only have access to a fraction of the relevant literature, potentially missing vital papers in their fields.

Involvement by the academic community is critical in ensuring that efforts to reclaim scholarly communication for scholars and researchers succeed. Together we can develop a new system that meets your needs and those of future scholars and students. It’s time to create change!

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