This article offers a plausible domain-general explanation for why some concepts of processes are resistant to instructional remediation although other, apparently similar concepts are more easily understood. The explanation assumes that processes may differ in ontological ways: that some processes (such as the apparent flow in diffusion of dye in water) are emergent and other processes (such as the flow of blood in human circulation) are direct. Although precise definition of the two kinds of processes are probably impossible, attributes of direct and emergent processes are described that distinguish them in a domain-general way. Circulation and diffusion, which are used as examples of direct and emergent processes, are associated with different kinds of misconceptions. The claim is that students' misconceptions for direct kinds of processes, such as blood circulation, are of the same ontological kind as the correct conception, suggesting that misconceptions of direct processes may be nonrobust. However, students' misconceptions of emergent processes are robust because they misinterpret emergent processes as a kind of commonsense direct processes. To correct such a misconception requires a re-representation or a conceptual shift across ontological kinds. Therefore, misconceptions of emergent processes are robust because such a shift requires that students know about the emergent kind and can overcome their (perhaps even innate) predisposition to conceive of all processes as a direct kind. Such a domain-general explanation suggests that teaching students the causal structure underlying emergent processes may enable them to recognize and understand a variety of emergent processes for which they have robust misconceptions, such as concepts of electricity, heat and temperature, and evolution.

Students learn more and gain greater understanding from one-to-one tutoring. The preferred explanation has been that the tutors' pedagogical skills are responsible for the learning gains. Pedagogical skills involve skillful execution of tactics, such as giving explanations and feedback, or selecting the appropriate problems or questions to ask the students. Skillful execution of these pedagogical skills requires that they are adaptive and tailored to the individual students' understanding. To be adaptive, the tutors must be able to monitor students' understanding accurately, so that they know how and when to deliver the explanations, feedback, and questions. Before exploring whether in fact tutoring effectiveness can be attributed to tutors' pedagogical skills, we must first ascertain the accuracy with which tutors monitor their students' understanding. This article thus investigated monitoring accuracy from both the tutors' and the students' perspectives. By coding and recoding some data collected in a previous study, the article shows that tutors could only assess students' normative understanding from the perspective of the tutors' knowledge, but tutors were dismal at diagnosing the students' alternative understanding from the perspective of the students' knowledge.

Human one-to-one tutoring has been shown to be a very effective form of instruction. Three contrasting hypotheses, a tutor-centered one, a student-centered one, and an interactive one could all potentially explain the effectiveness of tutoring. To test these hypotheses, analyses focused not only on the effectiveness of the tutors’ moves, but also on the effectiveness of the students’ construction on learning, as well as their interaction. The interaction hypothesis is further tested in the second study by manipulating the kind of tutoring tactics tutors were permitted to use. In order to promote a more interactive style of dialogue, rather than a didactic style, tutors were suppressed from giving explanations and feedback. Instead, tutors were encouraged to prompt the students. Surprisingly, students learned just as effectively even when tutors were suppressed from giving explanations and feedback. Their learning in the interactive style of tutoring is attributed to construction from deeper and a greater amount of scaffolding episodes, as well as their greater effort to take control of their own learning by reading more. What they learned from reading was limited, however, by their reading abilities.

This study investigated whether collaborative learning leads to the construction of shared knowledge among participants. In this study, college student pairs collaborated to learn a biology text on the human circulatory system. The results showed that pairs shared not only correct knowledge that was presented in the text, but also incorrect knowledge and/or knowledge that had to be inferred from the text. In addition, pairs who interacted more shared significantly more inferred knowledge than those who interacted less did. Taken together, these findings indicate that interaction enables dyads to construct new knowledge and their representations tend to converge after collaboration.

Learning from a Computer Workplace Simulation Heisawn Jeong ( h e i s @ p i t t . e d u ) Roger Taylor (rtaylor@pitt.edu) Michelene T. H. Chi ( c h i @ p i t t . e d u ) Learning Research and Development Center; University of Pittsburgh 3939 O’Hara Street, Pittsburgh, PA 15260 USA Abstract Workplaces are rapidly changing, placing increased cogni- tive demands upon workers. The use of computer workplace simulations has been proposed to help students success- fully make the transition from school to work. In this study, we examined what kinds of learning occurred when students used a computer workplace simulation called Court Square Community Bank. We hypothesized that three types of learning would occur: (1) students would gain knowledge about the banking business in which the simu- lation is situated and (2) students would also learn general business knowledge and problem solving/decision making skills that they could apply in other work contexts. Thir- teen pairs of high school students used a workplace simula- tion. The results showed that students knew significantly more knowledge about the banking business. Students also adopted a new perspective to organize their knowledge and their problem solving activities became more coordinate. Taken together, the results of this study showed that com- puter workplace simulation can serve as a useful tool to prepare students to make a better school-to-work transi- tion. Introduction There is a growing concern that many of today’s high-school graduates are ill-prepared for succeeding in today’s demanding and rapidly changing workplaces. The world of work is expe- riencing a dramatic transition: jobs increasingly require complex thinking skills and adaptive performance. As a con- sequence, there have been many calls for school-to-work transition programs such as youth apprenticeship or techni- cal preparation. Recently, Ferrari, Taylor, and VanLehn (1999) advocated the use of computer simulations as a way to facilitate the school to work transition. They argued that computer simulations of workplace environments can help familiarize students with a particular workplace and assist them in developing the analytical/problem-solving skills needed to successfully participate in the workplace, while allowing them to remain safely situated in the classroom. The goal of this paper is to assess what kinds of learning opportunities are afforded and how much learning actually occurs when students use such a computer simulation. For our study, we selected a workplace simulation called Court Square Community Bank (CSCB), one of the simula- tions recommended by Ferrari et al. (1999). CSCB is an episode-based simulation. Students play the role of vice president, engaging in activities such as interacting with bank customers, consulting the opinions of other bank em- ployees, and making business decisions. Each of the 14 epi- sodes poses different kinds of problems that the vice presi- dent of a small community bank must deal with such as approving mortgages or selecting the best candidate for a position (see Ferrari et al., 1999 and McQuaide, Leinhardt, & Stainton, 1999 for more details on the program). In assessing learning from CSCB, 1 we were less con- cerned with evaluating the specific workplace simulation and were more interested in understanding the learning issues involved in workplace simulations in general. We hypothe- sized that two types of learning can occur when students interact with a computer workplace simulation. Students could acquire (1) knowledge about the banking business in which the simulation is situated, and (2) general business knowledge and problem solving skills that are applicable to a wide variety of workplaces. Below, we describe these in more detail and speculate on how learning such knowl- edge/skills might occur. 1. Knowledge about the Banking Business One of the most notable features of the computer workplace simulation in comparison to other medium of instructions (e.g., reading an expository text or listening to a lecture) is its contextualized nature. In the case of CSCB, the specific business context was a small town community bank. The contextualization was done by using the problems that arises from the banking business (e.g., when to approve a mort- gage) and implementing interactions with simulated charac- ters who are primarily bank personnel or customers. This means that much of the information about banking business is embedded in the problem descriptions and the students’ interactions with the characters of the simulation. In addition to this contextualization, declarative banking knowledge is presented in the form of on-line dictionary and procedural manual. In sum, the simulation provides extensive amount of information about banking business, either implicitly or explicitly. Although this banking knowledge is never the focus of the simulation (e.g., the program never asks stu- The effect of the simulation is likely to be different when used in schools compared to when it was used in the laboratory as in this study. For example, in one of the schools that used the CSCB as part of their curriculum, it was augmented with instruc- tional and teacher supports (see McQuaide et al., 1999 for more details).

A good deal of research has addressed the topic of naive physics knowledge, with a focus on the physics domain of classical mechanics. In particular, it has been proposed that novices enter into instruction with an existing, well-defined knowledge base that they have derived from their everyday experiences. Most relevant initial knowledge will be substance based, in the sense that it represents the novice's understanding of how material objects and other types of substances behave in the course of everyday life. Our position is that novices make every effort to assimilate new physics knowledge into their initial knowledge structures. Thus, abstract physics concepts will tend to be attributed with properties or behaviors of material substances. For example, force is considered by many novices to be a property of moving objects. Novices also appear to draw on their substance knowledge when they are asked to reason about other abstract concepts, such as light, heat, and electricity. Many researchers have explored naive conceptions of these concepts to the extent that a fairly broad view of the literature is now accessible. This article opens with a discussion of naive knowledge of material substances (including objects) and presents a broad theoretical framework called the substance schema, which is used throughout the article to refer to any generalized knowledge of material substances and objects. It must be noted that the term schema is used loosely in reference to any existing generalized knowledge; no arguments are presented concerning the actual "structure" of conceptual knowledge. Misconceptions of the concept of force are first briefly reviewed, followed by more extensive reviews of research concerned with naive conceptions of light, heat, and electricity. These reviews provide support for the claim that naive conceptions often reflect an underlying commitment to existing knowledge of material substances. The article closes with a discussion of the use of materialistic models by physicists and implications for instruction.

This article provides one example of a method of analyzing qualitative data in an objective and quantifiable way. Although the application of the method is illustrated in the context of verbal data such as explanations, interviews, problem-solving protocols, and retrospective reports, in principle, the mechanics of the method can be adapted for coding other types of qualitative data, such as gestures and videotapes. The mechanics of the method are outlined in 8 concrete steps. Although verbal analyses can be used for many purposes, the main goal of the analyses discussed here is to formulate an understanding of the representation of the knowledge used in cognitive performances and how that representation changes with learning. This can be contrasted with another method of analyzing verbal protocols, the goal of which is to validate the cognitive processes of human performance, often as embodied in a computational model.

One-on-one tutoring is a form of instruction that requires interaction between a tutor and a tutee. The effectiveness of tutoring is examined from the perspectives of the tutor's actions, the tutee's actions, and successive interactions. A tutor's actions that may not lead to successive interactions consist of asking an initiating question, providing feedback, and asking a comprehension-gauging question. It is suggested that these types of actions can lead to the learning of an ideal template of solution procedures for solving problems, but may not lead to deep understanding. A tutee's actions are postulated as self-explaining, in response to either tutors' questioning, tutors' prompting, or tutors' scaffolding. Finally, an interaction is defined as a sequence of tutor actions, such as scaffolding, which elicits a successive series of exchanges between the tutor and tutee, so that they collaboratively construct a response. An exercise in a detailed protocol analysis of a case study of a student being tutored in solving a mechanics problem is presented. It shows what misconceptions the student exhibited, whether these misconceptions were removed, and what actions triggered the learning. The results of this case study support the suggestion that tutor actions that prompt for co-construction (which includes self-explanation) may be the most beneficial in producing deep learning, in the sense of removing misconceptions. © 1997 John Wiley & Sons, Ltd.

Physics novices and experts solved conceptual physics problems involving light, heat, and electric current and then explained their answers. Novices were ninth-grade students with no background in physics; experts were two postgraduates in physics and two advanced physics graduate students. Problems were multiple choice, with one correct response and three alternative responses representing possible misconceptions. For each conceptual physics problem, an isomorphic material-substance problem was constructed by imagining a materialistic conception of the physics topic and creating the resulting version of the problem. In each physics problem, one of the incorrect choices corresponded to the correct choice in the isomorphic material-substance problem. The empirical question was whether novices would reason about the physics problem as if it were conceptually similar to the substance isomorph. This question was addressed by comparing subjects' responses in the problem pairs, as well as by examining their explanations concerning all problems. A content analysis of subjects' explanations revealed that physics novices were strongly inclined to conceptualize physics concepts as material substances, whereas expert protocols revealed distinctly nonmaterialistic representations. A theory of conceptual change involving ontologically distinct categories is substantiated by these findings.

Learning involves the integration of new information into existing knowledge. Generating explanations to oneself (self‐explaining) facilitates that integration process. Previously, self‐explanation has been shown to improve the acquisition of problem‐solving skills when studying worked‐out examples. This study extends that finding, showing that self‐explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth‐grade students were merely asked to self‐explain after reading each line of a passage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self‐explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated a large number of self‐explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model analysis of the self‐explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self‐explaining are considered as reasons for the gains in deeper understanding.

Ference Marton has produced a characteristically lucid and elegant commentary, for which I am grateful, He took the time to resay, from his position, that from my monograph which is most compelling to him-a contribution in itself. He identifies as a central product my uncovering the "structures of awareness" that underlie both everyday experience and, with development and learning, scientific apprehension of the physical world. I am happy for his descriptions of those aspects of my work. Marton also feels it is worth the trouble to try to save me and others from the "dogma of cognitivism." He doubts the wisdom of the fundamental commitments of information as a layer "between the brain and behavior." Instead, he advocates the experiential turn, analyzing experience itself rather than, as he sees it, looking for dubious constructs, such as "dictionaries, interpreters, and semantic nets," that constitute ghosts in the machine, an invisible and unlikely causal fabric that produces experience as an epiphenomenon.

Abstract In order for the general area of expertise in auditing to advance, researchers need to better understand the tasks auditors perform, the type of knowledge required for each task, and the training and experience auditors get at performing. Given the length of an auditor's career, it might be difficult to study auditors from their debut with the firm to their retirement, but studying their first years might provide useful insights. Auditors do no get as much experience at performing audits as chess masters do at playing chess. More information on the nature of the experiences auditors have with various audit tasks will help identify those tasks where auditors might have a well developed task-specific schema and those where the schema is more general. Because each client has specific characteristics and each industry requires different domain knowledge, an important characteristic of an auditor's expertise may be the ability to transfer his or her expertise when working in a new domain. There is a need to determine what knowledge and skills transfer when the domain changes.

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The present paper analyzes the self-generated explanations (from talk-aloud protocols) that “Good” and “Poor” students produce while studying worked-out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self-explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example-independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self-explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self-explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation-based learning to account for these psychological findings.