Z. Chen and D. Klahr. All other things being equal: Acquisition and transfer of the control of variables strategy. Child Development, 70(5):1098–1120, September/October 1999. [url]

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The authors were interested in understanding how the transfer of specific skills in the control of variables strategy can affect the learning process. Initially they discreiminate between Discovery learning and Formal learning. They believe that Discovery learning may be effective when problems outcomes provide informative feedback (Siegler, 1976). Therefore they envisioned the transfer of the above strategy as the result of explicit training (using examples and direct instraction to teach the general strategy) and implicit training via probes (providing systematic questions following children’s activities).

Their findings showed that with appropiate instruction, elementary schoolchildren are capable of understanding, learning, and transferring the basic strategy when designing and evaluationg simple tests. For them the analogical reasoning (analogical = ANALOGIES) plays a central role in the real world of scientific discovery.

In the literature of analogical transfer and problem solving appears several major cognitive processes:

1. contruct a representation of the source problem;

2. when encoutering a similar problem, students need to access the relevant source information and notice the similarity shared by the problems;

3. the key components of the problems needs to be mapped, so that the source solutions or strategies can be extended;

4. the relevant solution needs to be implemented in the new context or domain. Finally the authors concluded that one critical factor facilitating schema construction is the opportunity to process diverse instances that share a similar goal structure or solution principle.

In addition to this point, they report that when the task or problems generate outcomes that provide clear feedback, children are capable of modifying their initial mental model and discovering a rule or principle.

D. E. Broadbent. Levels, hierarchies, and the locus of control. Quarterly Journal of Experimental Psychology, (29):181–201, 1977.

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This old paper describes a multi-level adaptive control mechanism of problem-solving situation. The general principle explained is that human processing of information takes place on many levels: some of these levels modify or control the operation of others.

The author describe his setup for controlling problem-solving solution, which is constituted by a simulation of bus transport in a city. The users can alter the time interval between the buses entering the city, and they can alter the amount charged for use of the city car’s park. By altering these quantities, they can affect the load on the buses, which is usually expressed in the number of people carried per hundrerd buses. They can also affect the number of empty spaces remaining in the car parks. Before experience of the situation, and again afterwards, the users are questioned on the effect of their interventions on the system. Despite the satisfactory performance atteined in the task, their ability to answer questions about what they did on the system was poor.

Then, the author describes different control mechanisms, showing that the structure that could explain the obtained results of the task is an adaptive controller structure. The key point of the paper is that the transfer of controlshould include the possibility that one section of the program/structure can alter other sections program/structure. 

For testing out the ideas of my thesis, I am developing a game, Monopoly like, in which the users can experiment the relation between land price and the factors connected with its determination.

My objective is to constitute a software agent that can help the human agent on the resolution of the problem (aka win the game). This software agent should assume a symmetrical position than the human agent. Therefore, it wont have any hidden knowledge. For the same reason, it should solve the problem through some approximation heuristics and not through complex regression methods.

My problem can be schematized as follows:

1. A two dimensional map of the city is divided in cells;

2. For each cell there is a value of the land p, that is determined with a linear function like the one at (1):

(1) 

3. The values of x, for k=1,2,…, m are independent variables. Some of them are constant during the game. Others are variable (i.e., pollution); others are defined by the game interaction (i.e., the presence of an hotel nearby).

Finally, this software agent should be able to solve the problem by approximation, participating on the game and applying some heuristics ad hoc.

(via)

Will Wright, the creator of The Sims, and SimCity, presented his latest creation at 2005 DICE Summit : It is called Spores. The interesting idea behind is to enhance the “value to gamers” as he said: people loves to create their content. “Owning” the content in this way means that all the stories that the games creates are much more meaningful.

The fact is that his design, reminded me of “the Nerve Garden“, a public terrarium in the cyberspace, using which, users could develop their own virtual plant and seed it in a virtual islands, … pretty much the same design of the Spores game.

I like a lot simulations, I think they are great tools for learning. These microworlds influenced a lot my past and present work. Additionally, I think that in these kinds of environments, learning and fun can be really intertwined.

The 2D creature editor allows you to add bones, limbs, etc. in whatever configuration you can dream up.

Copyright notice: the present content was taken from the following URL, the copyrights is reserved by the respective author/s.

The term feedback originates in cibernetics (Wiener, 1948), where it is used to denote processes by which a control unit gets information about the effects and consequences of its actions. Linguistic Feedback (Allwood 1979, 1988) is the linguistic mechanism which enable the participants of a conversation to exchange information about the four basic communicative functions which are essential in human direct face-to-face cmmunication. These function are: contact (willing/able to continue), perception (willing/able to perceive), understanding (willing/able to understand), and attitudinal reactions (willing/able to react).

Allwood, J., Nivre, J., and Ahlsén, E. (1993). On the semantics and pragmatics of linguistic feedback. Journal of Semantics, 9(1):1–29. Available from: http://wwwantique.ling.gu.se/jens/publications/B%2520files/B33.pdf.

Is the criterion sufficient for the current purposes of the communication so that the contributor and his or her partner believe that the partners have understoood what the contributor meant. Technically, then Grounding is the collective process by which the participants try to reach this mutual belief.

This definition was taken from H. Clark and E. Brennan 1991.

Grounding is the attempt to maintain a shared understanding od the information exchanged in a communication process. Is it often used with a series of concepts considered synonims: common ground, mutual knowledge, mutual beliefs, and mutual assumption.



This definition was taken from H. Clark and E. Brennan 1991, although the authors refer to previous studies> Clark & Carlson, 1982; Clark and Marshall, 1981; Lewis, 1969; Schelling, 1960.



Grounding is therefore the process by which common ground gets updated during the communication process.



This definition was taken from H. Clark and E. Brennan 1991, although the authors refer to previous studies> Clark & Schafer, 1987, 1989, Clark & Wilkes-Gibbs, 1986; Isaacs & Clark, 1987.

I. Smith, S. Consolvo, A. LaMarca, J. Hightower, J. Scott, T. Sohn, J. Hughes, G. Iachello, and G. D. Abowd. Social disclosure of place: From location technology to communication practices. In Proceedings of Pervasive05, Munich, Germany, May, 8-13 2005. Ludwig-Maximilians-Universität München. [url]

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This article contain the state of the art in the field of social location disclosure. The authors report the design rationale and the study they did to develop the application named Reno, which provides this kind of push-and-pull peer-to-peer location disclosure.

The authors justify in details each design choice in relation with advantages and limitation of thechnology and in connection with the expected user experience.

An interesting part of the paper discuss the implemented Cell tower spotting technique, using which, the users can build themselves a database of the cells locations.

Learning by experimenting is central to the informal knowledge you can achieve while exploring a simulation environment. To achieve a better comprehension of the simulation principles, the learner should have a clear solution strategy, which means testing out hypothesis/variables and observing the results.

Machine differs from human because they are more structured and methodic. When they have a clear idea in mind (read instruction) they just follow it. You cannot expect the same simple behavior in humans.

For this reason we can expect a machine to be useful to a human, to give an example of methodological approach to free exploration of a simulation. If we agree on an optimal solution strategy, then the machine can just apply that strategy, making visible a structured approach. To simply differ from an opponent, that companion should make explicit its choices and the reasons why it choose that.

Additionally, the companion should not just work by its own. It should operate in relation with the human: when s/he is available and when s/he is able to see and follow the approach/example of the software agent. There should be a collaborative effort between the human and the software agent. The agent should take advantage of the human interaction in the system to test out hypothesis and considering results. The difference should reside in the strict application of the methodology.

The agent should test out hypothesis playing the game and achieving results (its goal and satisfaction). The agent has no hidden knowledge about the simulation principles. The interaction between the agents and the system is symmetrical: they both have the same possibilities of operating on the system.

via The Garden of Eden:

… a large field of computer scientists are trying to develop computers than can have a reasonable conversation with a user. … but I think maybe it is not good to try to ‘mimic’ existing forms of comm., but rather exploit the new possibilities. … so, instead of trying to let a computer make social conversation, just because humans tend to do so too, let’s try to let it make interesting (or: relevant) conversation!?

I fully agree with this. I have the impression that most of the AI approach to collaborative learning failed because of the language bottleneck that it is very difficult to solve …

Long life to the Garden!