Daniel L. Schwartz

LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE

ABSTRACT This paper discusses Bettys Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding meta-cognitive strategies that governed Bettys behavior and self-regulation hints provided by a mentor agent. The study compared three versions: a system where the student was tutored by a pedagogical agent, a learning by teaching system, where students taught a baseline version of Betty, and received tutoring help from the mentor, and a learning by teaching system, where Betty was enhanced to include self-regulation strategies, and the mentor provided help on domain material on how to become better learners and better teachers. Results indicate that the addition of the self-regulated Betty and the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the SRL environment.

Shuttling Between Depictive Models and Abstract Rules: Induction and Fallback

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi-empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number-based rules. The first two experiments used the convergent evidence of problem-solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions.

Parallel prototyping leads to better design results, more divergence, and increased self-efficacy

Iteration can help people improve ideas. It can also give rise to fixation, continuously refining one option without considering others. Does creating and receiving feedback on multiple prototypes in parallel, as opposed to serially, affect learning, self-efficacy, and design exploration? An experiment manipulated whether independent novice designers created graphic Web advertisements in parallel or in series. Serial participants received descriptive critique directly after each prototype. Parallel participants created multiple prototypes before receiving feedback. As measured by click-through data and expert ratings, ads created in the Parallel condition significantly outperformed those from the Serial condition. Moreover, independent raters found Parallel prototypes to be more diverse. Parallel participants also reported a larger increase in task-specific self-confidence. This article outlines a theoretical foundation for why parallel prototyping produces better design results and discusses the implications for design education.

Scientific and Pragmatic Challenges for Bridging Education and Neuroscience

Educational neuroscience is an emerging effort to integrate neuroscience methods, particularly functional neuroimaging, with behavioral methods to address issues of learning and instruction. This article consolidates common concerns about connecting education and neuroscience. One set of concerns is scientific: in-principle differences in methods, data, theory, and philosophy. The other set of concerns is pragmatic: considerations of costs, timing, locus of control, and likely payoffs. The authors first articulate the concerns and then revisit them, reinterpreting them as potential opportunities. They also provide instances of neuroscience findings and methods that are relevant to education. The goal is to offer education researchers a window into contemporary neuroscience to prepare them to think more specifically about the prospects of educational neuroscience.

Toward Teachers' Adaptive Metacognition

In this article, we compare conventional uses of metacognition with the kinds of metacognition required by the teaching profession. We discover that many of problems and tasks used in successful metacognitive interventions tend to be reasonably well-defined problems of limited duration, with known solutions. Teaching has unique qualities that differentiate it from many of the tasks and environments that metacognitive interventions have supported. Teachers often confront highly variable situations. This led us to believe that successful teaching can benefit from what we call adaptive metacognition, which involves change to oneself and to ones environment, in response to a wide range of classroom social and instructional variability. We present several examples to illustrate the nature of metacognition required by teachers and the challenges of helping teachers recognize situations that require adaptive metacognition. We conclude the article by describing an approach, critical event-based instruction, which we have recently developed to help teachers appreciate the need for metacognitive adaptation by seeing the novelty in everyday recurrent classroom events.

Software for managing complex learning: Examples from an educational psychology course

Inquiry-based instruction including problem-, project-, and case-based methods often incorporate complex sets of learning activities. The numerous activities run the risk of becoming disconnected in the minds of learners and teachers. STAR.Legacy is a software shell that can help designers organize learning activities into an inquiry cycle that is easy to understand and pedagogically sound. To ensure that classroom teachers can adapt the inquiry activities according to their local resources and needs, STAR.Legacy was built upon four types of design principles: learner centered, knowledge centered, assessment centered, and community centered. We describe how a STAR.Legacy constructed for an educational psychology course helped preservice teachers design and learn about effective inquiry-based instruction.

Analog Imagery in Mental Model Reasoning: Depictive Models

We investigated whether people can use analog imagery to model the behavior of a simple mechanical interaction. Subjects saw a static computer display of two touching gears that had different diameters. Their task was to determine whether marks on each gear would meet if the gears rotated inward. This task added a problem of coordination to the typical analog rotation task in that the gears had a physical interdependency; the angular velocity of one gear depended on the angular velocity of the other gear. In the first experiment, we found the linear relationship between response time and angular disparity that indicates analog imagery. In the second experiment, we found that people can also solve the problem through a non-analog, visual comparison. We also found that people of varying spatial ability could switch between analog and non-analog solutions if instructed to do so. In the third experiment, we examined whether the elicitation of physical knowledge would influence solution strategies. To do so, we manipulated the visual realism of the gear display. Subjects who saw the most realistic gears coordinated their transformations by using the surfaces of the gears, as though they were relying on the friction connecting the surfaces. Subjects who saw more schematic displays relied on analytic strategies, such as comparing the ratios made by the angles and/or diameters of the two gears. To explain the relationship between spatial and physical knowledge found in the experiments, we constructed a computer simulation of what we call depictive modeling. In a depictive model, general spatial knowledge and context-sensitive physical knowledge have the same ontology. This is different from prior simulations in which a non-analog representation would be needed to coordinate the analog behaviors of physical objects. In our simulation, the inference that coordinates the gear motions emerges from the analog rotations themselves. We suggest that mental depictions create a bridge between imagery and mental model research by positing the referent as the primary conceptual entity.

Prototyping dynamics: sharing multiple designs improves exploration, group rapport, and results

Prototypes ground group communication and facilitate decision making. However, overly investing in a single design idea can lead to fixation and impede the collaborative process. Does sharing multiple designs improve collaboration? In a study, participants created advertisements individually and then met with a partner. In the Share Multiple condition, participants designed and shared three ads. In the Share Best condition, participants designed three ads and selected one to share. In the Share One condition, participants designed and shared one ad. Sharing multiple designs improved outcome, exploration, sharing, and group rapport. These participants integrated more of their partners ideas into their own subsequent designs, explored a more divergent set of ideas, and provided more productive critiques of their partners designs. Furthermore, their ads were rated more highly and garnered a higher click-through rate when hosted online.

Reasoning about the referent of a picture versus reasoning about the picture as the referent: An effect of visual realism

Research on picture perception and picture-based problem solving has generally considered the information that enables one to “see” and think about a picture’s subject matter. However, people often reason about a picture or representation as the referent itself. The question addressed here is whether pictorial features themselves help determine when one reasons about the referent of an image, as with an engrossing movie, and when one reasons about the image in its own right, as with abstract art. Two experiments tested the hypothesis that pictures with relatively high fidelity to their referents lead people to think about those referents, whereas pictures with relatively low fidelity lead people to think about the picture as a referent. Subjects determined whether marks on the bottom and top boards of an open hinge would meet if the hinge were closed. Accuracy and latency results indicated that subjects who saw realistic displays simulated the physical behavior of the hinge through analog imagery. In contrast, subjects who saw schematic displays tended to reason about static features of the display such as line lengths and angles. The results demonstrate that researchers must be cautious when generalizing from reasoning about diagrammatic materials to reasoning about the referents themselves.

Physical imagery : Kinematic versus dynamic models

Physical imagery occurs when people imagine one object causing a change to a second object. To make inferences through physical imagery, people must represent information that coordinates the interactions among the imagined objects. The current research contrasts two proposals for how this coordinating information is realized in physical imagery. In the traditional kinematic formulation, imagery transformations are coordinated by geometric information in analog spatial representations. In the dynamic formulation, transformations may also be regulated by analog representations of force and resistance. Four experiments support the dynamic formulation. They show, for example, that without making changes to the spatial properties of a problem, dynamic perceptual information (e.g., torque) and beliefs about physical properties (e. g., viscosity) affect the inferences that people draw through imagery. The studies suggest that physical imagery is not so much an analog of visual perception as it is an analog of physical action. A simple model that represents force as a rate helps explain why inferences can emerge through imagined actions even though people may not know the answer explicitly. It also explains how and when perception, beliefs, and learning can influence physical imagery.