Four experiments explored participants' understanding of the abstract principles goincipl coinci simulatios o coulat adaptive systems. Experiments 1, 2, and 3shoBU better transfero abstract principlesacroc simulatioA that were relatively dissimilar, and that this e#ect was dueto participantswho perfocip relativelypolat o the initialsimulatioB In Experiment 4, participantsshoic better abstract understandingo asimulatio when it was depicted withcohA@CU rather than idealized graphical elements.Homents fo pom perfos.Aq/ the idealizedversio o the simulatio transferred betterto a newsimulatio gomulat by the same abstractioU The results are interpreted in termso cosAq6BP--A between abstract and codAP)U coAP)U@/A o thesimulatio)/ Individualsproi toiv coivid coividual tendto oodAPU abstractioH whenconA)C@ pro)C@qUA o superficial similarities are salient.

Traditionally, intelligent tutoring systems have provided feedback on the basis of a so-called expert model. Expert model tutors incorporate production rules associated with error free and efficient task performance. These systems intervene with corrective feedback as soon as a student deviates from a solution path. This thesis explores the effects of providing feedback on the basis of a so-called intelligent novice cognitive model. An intelligent novice tutor allows students to make errors, and provides guidance through the exercise of error detection and correction skills. The underlying cognitive model in such a tutor includes both rules associated with solution generation, and rules relating to error detection and correction. There are two pedagogical motivations for feedback based on an intelligent novice model. First, novice performance is often error prone and students may need error detection and correction skills in order to succeed in real world tasks. Second, the opportunity to reason about the causes and consequences of errors may allow students to form a better model of the behavior of domain operators. Learning outcomes associated with the two models were experimentally evaluated. Results show that learners who receive intelligent novice feedback demonstrate better learning overall, including better retention and transfer performance than students receiving expert model based feedback.

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The goal of our work is to produce a process model account of education in microworlds based on Soar, a theory of cognition and learning. In the context of a microworld that supports the exploration of qualitative electrostatics, we present operational models of both skilled and student interaction. In addition, we describe an episodic memory mechanism encoded in the student model that gives insight into the processes involved in learning from incorrect behavior. 1 Introduction In the field of computer-aided instruction, highly interactive microworlds have gained importance as educational tools aimed at supporting learning by exploration [7]. In contrast to more traditional educational strategies that "teach" the target knowledge to the student, learning by exploration focuses on stimulating the student's initiative in gaining knowledge about the domain[10, 11]. Highly interactive microworlds provide simulations that allow the student to experience the nature of some subject-matter do...

Syntactically correct code does not fall from the sky; the process that leads to a student’s first executable program is not well understood. At the University of Kent we have begun to explore the compilation behaviours of novice programmers, or the behaviours that students exhibit while authoring code; in our initial study, we have focused on when and what they choose to compile. By examining these behaviours, we have determined the most common errors encountered by students using BlueJ in our introductory course on object-oriented programming, how those students tend to program when in supervised laboratory sessions, and we have identified future directions of study driven by our initial observations. Our goal is to apply this research to the future development of BlueJ and instructional methodologies involving its use in the classroom. 1.

We explore how learning to play strategically in one game promotes strategic play in related games. Experiment 1 involves a simple linear transformation in payoffs between games (with presentation format changed as well). There is considerable but incomplete transfer as the growth in the learning process stalls. Experiment 2 changes responders ’ payoffs from supporting a pooling equilibrium to supporting a separating equilibrium. More strategic play is observed following the change than for inexperienced subjects in control sessions, contrary to the prediction of a fictitious play model. We present evidence that experience generates increased numbers of sophisticated players who anticipate responders ’ behavior following the change in payoffs, resulting in positive transfer.

This paper addresses issues associated with transfer of training; in particular, the importance of mimicking the contexts of application during training: When will a skill transfer from one situation of application to another, and what aspects of training affect a learner's ability to transfer the learning to new situations? Although there exist many examples of failure to show good transfer of training, the failures are more surprising and tend to mask the successes. With appropriate training there is generalization to a variety of contexts. Such training should include variable contexts during instruction, concrete examples, and abstract rules and procedures. Claims made by proponents of Situated Learning Theory suggest that training must be done in the context of the situation of application. We agree that learning is unlikely to transfer if taught in only one context with no examples from other contexts; however, because it is difficult to anticipate all future contexts of applicat...

The teaching of Experimental Psychology is at a crossroads. In order to learn what it means to conduct empirical research in psychology, students need to be able to design and analyze their own experiments. However, despite a massive investment in personal computer technology on the university level, the tools that allow students to build their own experiments are inadequate in a variety of ways. This means that few students end up learning what the science of psychology is really all about. Only by building, running, and analyzing their own experiments can students understand basic principles of experimental design and the way in which theories can be subjected to empirical tests. During the period between 1987 and 1995, there were 10 major attempts to address aspects of this problem through the building of experiment generation (EG) systems.

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Equivalence of learning paths 1 The equivalence of learning paths in early science instruction: effects of direct instruction