Barbara Y. White

An approach to training based upon principled task decomposition

Abstract The primary purposes of the present research are (1) to specify a principled basis for analyzing the skill and knowledge components of expert performance within a domain. (2) to develop an instructional strategy based upon this decomposition, and (3) to study the knowledge and skills resulting from such instruction. The domain of application, the Space Fortress game (Mane et al. 1984; Mane 1985), is a complex task which involves the concurrent and coordinate use of perceptual and motor skills, conceptual and strategic knowledge, in the service of multiple goals. There are three primary aspects to our approach. The first is a decomposition of the task domain from the perspective of the inherent structure of the task, its human information processing demands, and the characteristics of expert performance. The decomposition identifies the top level goals of experts and the strategies, skills, and knowledge developed by them in pursuit of those goals. The second aspect of our approach is an analysis of hierarchical relations among the skills and knowledge components to be acquired. Hierarchical relations derive from conditional and functional relations among the components. This process attempts to determine a possible set of transformations that will turn novices into experts. As part of this process, skills may be identified that are not present in expert performance but which are necessary precursors to the acquisition of some skill or knowledge that is a component of expert performance. The third aspect of our approach is a construction of activities for training the individual components and their integration based upon a set of design principles, and an evaluation of the nature of the skills and knowledge acquired. In contrast to the training strategies pursued by other investigators within the Learning Strategies project, our training activities are not necessarily set in the context of the Space Fortress game. This allowed us to constrain task performance in such a way as to necessitate the development of particular concepts needed for expert performance. In addition, our training strategy focused, more than any of the other training strategies, on the cognitive aspects of expertise in the game. In particular, we were concerned with facilitating the development of the conceptual knowledge that our analyses indicated is necessary for expert ship control. Our experimental results indicate the subjects exposed to our series of training tasks not only performed better than control subjects on the Space Fortress game but also performed better on other dynamic control tasks as well as on a paper and pencil physics test that measured their understanding of how accelerations affect the motion of objects.

Dynamic Mental Models in Learning Science: The Importance of Constructing Derivational Linkages among Models

We present a theory of learning in science based on students deriving conceptual linkages among multiple models which represent physical phenomena at different levels of abstraction. The mod- els vary in the primitive objects and interactions they incorporate and in the reasoning processes that are used in running them. Students derive linkages among models by running a model (embodied in an inter- active computer simulation) and reflecting on its emergent behaviors. The emergent properties they iden- tify in turn become the primitive elements of the more abstract, derived model. We describe and illustrate derivational links among three models for basic electricity: a particle model, an aggregate model, and an algebraic model. We then present results of an instructional experiment in which we compared high school students who were exposed to these model derivations with those who were not. In all other respects, both groups of students received identical instruction. The results demonstrate the importance of enabling stu- dents to construct derivational linkages among models, both with respect to their understanding of circuit theory and their ability to solve qualitative and quantitative circuit problems.

Pedagogical Practices to Support Classroom Cultures of Scientific Inquiry.

This article examines the pedagogical practices of two science inquiry teachers and their students using a Web-based system called Web of Inquiry (WOI). There is a need to build a collective repertoire of pedagogical practices that can assist elementary and middle school teachers as they support students to develop a complex model of inquiry based on testing alternative hypotheses. A repertoire of effective instructional practices may help students learn the language of science, develop an understanding of scientific tools and representations, and take up a scientific worldview that emphasizes the generative, social nature of science (National Research Council [NRC], 2007). The Web of Inquiry (WOI) (http://www.webofinquiry.org) is a dynamic website where students carry out scientific inquiry projects to develop and test their theories; learn scientific language, tools, and practices of investigation; engage in self assessment; and provide feedback to peers. Two teachers and their classes participated in this study using a variety of science content. We examine and discuss the teachers’ use of instructional strategies to support students to develop a coherent and theoretically driven model of scientific inquiry using the WOI. Implications of this work are addressed with respect to student and teacher learning and preservice teacher education.

Teaching and Learning Generic Modeling and Reasoning Skills

Scientists, engineers, and technicians are frequently called upon to apply their expertise to new domains. We hypothesize that the knowledge needed to foster such transfer is: (a) an understanding of forms of models that are applicable in multiple domains; (b) inquiry skills for developing models and evaluating their appropriateness within a domain; and (c) generic reasoning strategies used in applying models when solving problems, such as those used when designing or troubleshooting a system. In this paper, we present examples of each of these forms of expertise and how they can be developed, based upon our work on creating conceptual models that facilitate the learning of physics, on the teaching of scientific inquiry and modeling skills in urban classrooms, and on the design of computer‐based learning environments for teaching electronic troubleshooting. We conclude by drawing some instructional implications of this view of expertise that are not often followed in the design of computer‐based learning ...

The Nature of Scientific Meta-Knowledge

We argue that science education should focus on enabling students to develop meta-knowledge about science so that students come to understand how different aspects of the scientific enterprise work together to create and test scientific theories. Furthermore, we advocate that teaching such meta-knowledge should begin in early elementary school and continue through college and graduate school and that it should be taught for all types of science, including the biological, physical, and social sciences.

Conceptual Models for Understanding the Behavior of Electrical Circuits

We are investigating the role that computer-based models can play in helping students to learn science. In the research reported in this chapter, we conducted experimental trials of a computer environment that provides linked models that represent circuit behavior from different perspectives (such as a microscopic versus a macroscopic perspective) and at differing levels of abstraction. In these trials, we varied the number of linked causal models that were given to different groups of students. Our objective was to determine whether working with reductionistic models (a) reduces students’ misconceptions, particularly their adherence to the commonly held “current-as-agent” misconception, and (b) increases the robustness and flexibility of students’ knowledge as they solve circuit problems and explain circuit phenomena. The first model that we developed and utilized, called the “particle model,” illustrates the behavior of mobile, charged particles within a conductive medium and their changes in position over time. The basic interaction among particles within this model is the Coulomb interaction (like charges repel, unlike charges attract). A second model that we developed depicts — at a higher level of abstraction — the properties of a system that incorporates such a mechanism. This model, called the “transport model,” incorporates more abstract representations of voltage and charge flow. The particle model can be used to provide an explanation or “unpacking” for processes that are considered primitives within the transport model. We conducted an experiment that examined students’ performance on a variety of circuit problems before and after they learned either (a) the transport model alone, or (b) the transport model augmented with explanations of its processes in terms of the particle model. We then compared performance on problems for which a current-as-agent conception is sufficient with performance on problems that require a full understanding of how voltages are created and distributed within a circuit. The posttest results revealed that both groups achieved a high level of performance on a wide range of problems. However, the subjects who received a particle model explanation for the basic concepts and processes of the transport model achieved a higher level of performance than the other group on problems that require an understanding of voltage and charge distribution. We conjecture that this is due to the particle model explanations providing students with a mechanistic model for voltage and charge distribution that is consistent with the behavior of the transport model and that inhibits the construction and use of the current-as-agent misconception.

Alternative Approaches to Using Modeling and Simulation Tools for Teaching Science

Computer modeling and simulation software are transforming the way science and engineering are done. They make possible analytic and conceptual tools that allow scientists to employ new forms of analysis, engage in new kinds of thought experiments, and create new types of theories. In this chapter, we illustrate how such computer-based tools can also transform the practice of science education. We describe how modeling and simulation tools, such as those embodied in our ThinkerTools software, facilitate a variety of instructional approaches that attempt to realize the increasingly ambitious and varied goals being advocated for modern science education. These goals include engaging young students in authentic scientific inquiry in which they learn about the nature of scientific models and the processes of modeling. They also include enabling students to learn abstract and complex subject matter at increasingly younger ages.

Acquiring and transferring intellectual skills with modifiable software advisors in a virtual inquiry support environment

Our previous work with the ThinkerTools Inquiry Curriculum found that students who were prompted to reflect on their work performed better on inquiry projects, and attained a better understanding of the inquiry process. These prompts, however, were in a pencil-and-paper form, which did not allow for individual, on-line needs. We hypothesize that improvement will be further enhanced by introducing an adaptive, user-controlled system of software agents that can advise students on completing specific inquiry tasks and their application of general intellectual and collaborative skills. In addition, students can modify the system to help students meet their own knowledge-building goals. Factors such as an advisors goals, the type of help an advisor can offer, when the advisor gives advice, and an advisors personality can be modified. For example, an inquiry task advisor, the Hypothesizer, helps students come up with alternative hypotheses that are testable. The students and the advisors work in a virtual inquiry support environment of typical artifacts such as a journal, progress report, meeting rooms and a dialogue box for communication.

Designing for science learning and collaborative discourse

A prototype Web-based environment, called the Web of Inquiry, was developed that built on previous work in science learning and technology. This new system was designed to meet constructivist-learning principles, support self-reflection, and meet specific interaction goals within the classroom environment. The system was tested it in fifth, sixth, and seventh grade (ages 10--13) classrooms. Mixed methods results suggest that the system met many of the initial design goals and also identified areas that could be improved in future iterations of the system.