John J. Clement

Students' Preconceptions in Introductory Mechanics.

Data from written tests and videotaped problem‐solving interviews show that many physics students have a stable, alternative view of the relationship between force and acceleration. This ’’conceptual primitive’’ is misunderstood at the qualitative level in addition to any difficulties that might occur with mathematical formulation. The misconception is highly resistant to change and is remarkably similar to one discussed by Galileo, as shown by comparison of his writings with transcripts from student interviews. The source of this qualitative misunderstanding can be traced to a deep‐seated preconception that makes a full understanding of Newton’s first and second laws very difficult. In such cases learning becomes a process in which new concepts must displace or be remolded from stable concepts that the student has constructed over many years.

NOT ALL PRECONCEPTIONS ARE MISCONCEPTIONS - FINDING ANCHORING CONCEPTIONS FOR GROUNDING INSTRUCTION ON STUDENTS INTUITIONS

This study begins the task of mapping out the domain of valid, potentially helpful beliefs of students and raises the possibility of drawing on these intuitions in teaching conceptual material. Some issues are explored surrounding the identification of such intuitions, referred to as anchoring conceptions or anchors. We attempt to: (1) propose some organizing theoretical and observational definitions of the anchor construct; (2) present some initial findings from a diagnostic test designed to uncover anchors for physics instruction; and (3) provoke an initial discussion of the new methodological issues that arise in this domain. The results of the diagnostic test indicate that a number of group anchors exist. In addition, some unexpected non‐anchors were identified. Furthermore, evidence was found indicating that some anchoring examples may be ‘brittle’, i.e., evidence that the anchor could not be extended analogically to help a student make sense of a target situation. Finally, it is suggested that furth...

Overcoming Misconceptions via Analogical Reasoning: Abstract Transfer versus Explanatory Model Construction.

In most work investigating factors influencing the success of analogies in instruction, an underlying assumption is that students have little or no knowledge of the target situation (the situation to be explained by analogy). It is interesting to ask what influences the success of analogies when students believe they understand the target situation. If this understanding is not normative, instruction must aim at conceptual change rather than simply conceptual growth. Through the analysis of four case studies of tutoring interviews (two of which achieved some noticeable conceptual change and two of which did not) we propose a preliminary list of factors important for success in overcoming misconceptions via analogical reasoning. First, there must be a usable anchoring conception. Second, the analogical connection between an anchoring example and the target situation may need to be developed explicitly through processes such as the use of intermediate, “bridging” analogies. Third, it may be necessary to engage the student in a process of analogical reasoning in an interactive teaching environment, rather than simply presenting the analogy in tetext or lecture. Finally, the result of this process may need to be more than analogical transfer of abstract relational structure. The analogies may need to be used to enrich the target situation, leading to the students construction of a new explanatory model.

Model Based Learning as a Key Research Area for Science Education.

A framework is presented for thinking about cognitive factors involved in model construction in the classroom that can help us organize the research problems in this area and the articles in this issue. The framework connects concepts such as: expert consensus model, target model, intermediate models, preconceptions, learning processes, and natural reasoning skills. By connecting and elaborating on these major areas, the articles in this issue have succeeded in moving us another step toward having a theory of conceptual change that can provide guidance to teachers in the form of instructional principles. Taken together, the articles remind us that individual cognition, while not the only factor in learning, is a central determining feature of learning. However, we must work to further develop the present partial theory of conceptual change to fill in the missing cognitive core of the present shell.

Observed Methods for Generating Analogies in Scientific Problem Solving

Evidence from videotapes of experts thinking aloud is presented which documents the spontaneous use of analogies in scientific problem solving. Four processes appear to be important in using an analogy: (1) generating the analogy; (2) establishing confidence in the validity of the analogy relation; (3) understanding the analogous case; and (4) applying findings to the original problem. This study concentrates on process (1). Evidence was found for three different methods of analogy generation: generation via a principle (1 case), generation via an association (8 cases), and generation via a transformation (18 cases). Although the mechanism underlying analogy generation is usually described as an association process, transformation processes, where the subject modifies or transforms some aspect of the original problem, may be just as important if not more important. In contrast to the usual view of an analogous case as already residing in memory, several of the analogous cases were quite novel, indicating that they were newly invented Gedanken experiments. The usefulness of some analogies appears to lie in a “provocative” function of activating additional knowledge schemas that is different from the commonly cited “direct transfer” function where established knowledge is transferred fairly directly from the analogous to the original case.

Learning via Model Construction and Criticism

There is growing recognition that mental models play a fundamental role in the comprehension of science concepts. The process of learning via model construction appears to be central to theory formation in science and central for science instruction but is still very poorly understood. This chapter uses evidence from case studies, in which a scientist is asked to think out loud, to argue that nonformal reasoning processes that are neither deductive nor inductive can play an important role in scientific model construction. The construction process is complex and involves repeated passes through a cycle of hypothesis generation, evaluation, and modification.

Creative Model Construction in Scientists and Students

We may not be able to make you love reading, but creative model construction in scientists and students will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.

DOES COMPUTER PROGRAMMING ENHANCE PROBLEM SOLVING ABILITY? SOME POSITIVE EVIDENCE ON ALGEBRA WORD PROBLEMS

Publisher Summary There is a common intuition among those in computer science education that programming encourages the development of good problem-solving skills. The term “problem solving” has a broader and deeper meaning than what is implied by its educational association with mathematics. This is an exciting idea, especially when one considers how the concept of problem solving has been synonymous with human endeavors of the best possible kind, with the mastery of the physical, social, and intellectual worlds we live in. Computer environments make it possible for students to experience some of the deeper ideas that underlie a correct understanding of what human problem solving entails. Students learn best in settings where they can move from the empirical to the theoretical and back again freely. This coincides with the way real problem-solvers function. Low-cost microcomputers make it possible to incorporate these ideas into mathematics curricula and also into new courses in computer science and computer literacy.

Expert novice similarities and instruction using analogies

Evidence is presented indicating that spontaneously generated analogies can play a significant role in expert problem solving. Since not all analogies are valid, it is important for the subject to have a way to evaluate their validity. In particular, this paper focuses on an evaluation strategy called bridging that has been observed in solutions to both science and mathematics problems. Spontaneous analogies have also been documented in the problem solving of students. The shared natural use of analogies for unfamiliar problems is an expert‐novice similarity. Some of the strategies observed in experts were incorporated in a teaching technique for dealing with students’ preconceptions in mechanics. Students taught via these units achieved large gain differences over control groups. Thus non‐deductive reasoning strategies used by experts can give us valuable clues concerning instructional strategies for science students. This complements the prior focus in the literature on expert novice differences with a ...

ACTIVE-LEARNING METHODS TO IMPROVE STUDENT PERFORMANCE AND SCIENTIFIC INTEREST IN A LARGE INTRODUCTORY OCEANOGRAPHY COURSE

Teaching methods that are often recommended to improve the learning environment in college science courses include cooperative learning, adding inquiry-based activities to traditional lectures, and engaging students in projects or investigations. Two questions often surround these efforts: 1) can these methods be used in large classes; and 2) how do we know that they are increasing student learning? At the University of Massachusetts, we have transformed the environment of a large-enrollment oceanography course (600 students) by modifying lectures to include cooperative learning via interactive in-class exercises and directed discussion. Assessments were redesigned as “two-stage” exams with a significant collaborative component. Results of student surveys, course evaluations, and exam performance demonstrate that learning of the subject under these conditions has improved. Student achievement shows measurable and statistically significant increases in information recall, analytical skills, and quantitative reasoning. There is evidence from both student surveys and student interview comments that for the majority of students, the course increased their interest in science — a difficult effect to achieve with this population.