Abstract: Analogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by generalization over patterns of regularity. Three important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. In this paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning of feedback mechanism.
Abstract: Learning and problem solving are intimately related: problem solving determines the knowledge requirements of the reasoner which learning must fulfill, and learning enables improved problem-solving performance. Different models of problem solving, however, recognize different knowledge needs, and, as a result, set up different learning tasks. Some recent models analyze problem solving in terms of generic tasks, methods, and subtasks. These models require the learning of problem-solving concepts such as new tasks and new task decompositions. We view reflection as a core process for learning these problem-solving concepts. In this paper, we identify the learning issues raised by the task-structure framework of problem solving. We view the problem solver as an abstract device, and represent how it works in terms of a structure-behavior-function model which specifies how the knowledge and reasoning of the problem solver results in the accomplishment of its tasks. We describe how this model enables refection, and how model-based reflection enables the reasoner to adapt its task structure to produce solutions of better quality. The Autognostic system illustrates this reflection process.
Abstract: This paper discusses the design and evaluation of a hypertext-based environment that presents instructional material on programming in Lisp. The design of the environment was motivated by results from studies investigating students' strategies for knowledge acquisition. The effectiveness of the design was evaluated by conducting a study that investigated how subjects used and learned from the instructional environment compared to subjects using more standard, structured, linear instruction. The results showed an ability by environment interaction: the higher ability subjects using the hypertext environment improved and made significantly less errors when programming new concepts while the lower ability subjects did not improve and made more errors. Meanwhile, subjects using the control environment did not show this ability- based difference. These results have implications for the design of intelligent tutoring systems. They affect decisions involving the amount of learner control that is provided to students and the way student models are constructed.
Abstract: We report two studies involving an intelligent tutoring system for Lisp (the CMU Lisp Tutor). In Experiment 1, we develop a model, based on production system theories of transfer and analogical problem-solving, that accounts for effects of instructional examples, the transfer of cognitive skills across programming problems, and practice effects. In Experiment 2, we analyzed protocols collected from subjects as they processed instructional texts and examples before working with the Lisp Tutor and protocols collected after subjects solved each programming problem. The results suggest that the acquisition of cognitive skills is facilitated by high degrees of metacognition, which includes higher degrees of monitoring states of knowledge, more self-generated explanation goals and strategies, and greater attention to the instructional structure. Improvement in skill acquisition is also strongly related to the generation of explanations connecting the example material to the abstract terms introduced in the text, the generation of explanations that focus on the novel concepts, and spending more time in planning solutions to novel task components. We also found that self-explanation has diminishing returns. Finally, reflection on problem solutions that focus on understanding the abstractions underlying programs or that focus on understanding how programs work, seems to be related to improved learning.
ABSTRACT: The pilot study described in this paper considers the utility of diverse types of graphical representations of programs for use by novices when reasoning about the programÕs behaviour and/or states. The study tests the hypothesis of information congruency: i.e. that performance on a task improves when the type of information highlighted in a given representation is congruent with the information required by the task. It also looks at the effect of differing presentation type on performance, and the interaction between information congruency and presentation type. Finally, it presents a qualitative analysis of the types of strategies used and the misunderstandings which occur when attempting to navigate through the diagram. The results of this preliminary study tend not to support the information congruency hypothesis and seem to suggest that other factors are at work. Likewise, although there is a trend for presentation to affect performance, the results are not conclusive. The general outcome of the study suggests the need for a more formal analysis of the processes involved in reasoning over data flow and control flow representations.
From Case-Based Reasoning to Scaffolded Electronic Notebooks: A Journey by J. Kolodner
Supporting Collaboration and Reflection on Problem-Solving in a Project-Based Classroom by M. Guzdial, J. Vanegas, F. Mistree, D. Rosen, J. Allen, J. Turns, and D. Carlson
Effects with and of CSCL: Tracking Learning in a New Paradigm by J. Kolodner and M. Guzdial
Facilitating Learning During Group Design J. L. Kolodner and M. Guzdial
ABSTRACT: A critical aspect of much of engineering is design. Design is most frequently a collaborative activity today, while collaboration is rarely taught in traditional engineering education. We are developing educational technologies to facilitate both doing and learning about collaboration in engineering design activities. Our software environment, CaMILE (Collaborative and Multimedia Interactive Learning Environment), provides a structure for doing collaborative engineering design that facilities integrating diverse media and other resources. In addition, CaMILE structures the way students collaborate. The importance of juxtaposing both learning and doing is one of the lessons learned from our experience. by integrating collaboration with artifacts central to the design activity, we can motivate collaboration and facilitate learning.
Case Libraries in Support of Design Education: The DesignMuse Experiences. N. Narayanan & J. Kolodner. In D. Budny (ed.), Frontiers in Education 1995.
ABSTRACT: A major part of the reasoning designers do involves accessing old cases and using the lessons learned in those situations to address new problems. DesignMuse is a shell for creating on-line case libraries with built-in browsing capabilities that make cases available to design students in a natural way while they are designing. Archie-2 is the most extensive case library built with DesignMuse, and has been used in Georgia Tech's design studios. Archie-2 provides flexible access to multimedia case representations of buildings: libraries, courthouses and skyscrapers. Susie is a student-built library containing cases about sustainable technology and development. These two case libraries illustrate the two ways in which we believe case libraries can enhance learning in the classroom: (1) by using the case library to search for, analyze, compare and contrast cases that are similar to the problems students are solving, which will improve their problem solving skills, and (2) by constructing cases and building a library after doing research in a domain, which will help them learn domain knowledge. This paper describes our classroom experiences with these two DesignMuse-based case libraries and work in progress on enhancing various functionalities of these libraries based on feedback from students and informal classroom observations.