An overview of research on experts and novice problem solvers in physics

Problem solving is regarded as one of the most important cognitive activities in our daily life and in professional contexts. There are differences between expert and novice in terms of their behaviour and knowledge organisation in solving physics problem. In terms of behaviour, usually experts employ planning, monitoring, evaluating and making qualitative analysis in their solution as compared to novices. Studies in problem solving usually compare behaviour between the two groups to see how these two groups performed. There were three criteria uses to select the group such as experiences, performances and background knowledge

Characteristics of problem solving success in physics

Skills in problem solving, including finding and applying the appropriate knowledge to a problem, are important learning outcomes from the completion of a Physics degree at University. This thesis investigates the characteristics of successful and unsuccessful novice University students solving problems in Physics in various contexts. Gaining an insight into student behaviour can clarify areas of weakness and potentially provide research based instructional strategies in these contexts. Access to external information during problem solving, such as the Internet, is becoming an increasingly relevant research area, as students use resources for homework questions and then in employment after University. Three chapters (Chapters 3-5) investigate individual novice problem solving with and without resources, such as a textbook. Participants were from introductory years one and two of Undergraduate study at University. The results from this chapter show successful and unsuccessful approaches by students to multi-step problems. One notable result is that unsuccessful students demonstrated an inability to apply the appropriate physics concepts, with or without the availability of resources. These results have implications for the skills required in closed and open-book exams. Three chapters of the thesis focus on the analysis of Peer Instruction (Chapters 6-8), an instructional method designed to improve conceptual understanding. Peer Instruction was used with a first year Introductory University class. Technical word use was not associated with success on Peer Instruction questions. Conversations were also analysed qualitatively. The results reflect diversity in reasoning regardless of correctness on the question. Some recommendations for the implementation of Peer Instruction are presented. The thesis is organised as follows. A literature review was conducted in relevant areas of study and is presented to set the context of the work. Three chapters report the study with novice individuals solving multi-step problems with and without resources. Three further chapters investigate successful and unsuccessful Peer Instruction discussions in Physics. The final results chapter (Chapter 9) presents a study of a group of experts solving physics problems. Overall successful and unsuccessful problem solving strategies were compared, as well as preliminary comparisons between expert and novice behaviour when solving physics problems

Towards an alternative to Benner’s theory of expert intuition in nursing: A discussion paper

Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner (1984). We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise

Expertise and intuition: A tale of three theories

Several authors have hailed intuition as one of the defining features of expertise. In particular, while disagreeing on almost anything that touches on human cognition and artificial intelligence, Hubert Dreyfus and Herbert Simon agreed on this point. However, the highly influential theories of intuition they proposed differed in major ways, especially with respect to the role given to search and as to whether intuition is holistic or analytic. Both theories suffer from empirical weaknesses. In this paper, we show how, with some additions, a recent theory of expert memory (the template theory) offers a coherent and wide-ranging explanation of intuition in expert behaviour. It is shown that the theory accounts for the key features of intuition: it explains the rapid onset of intuition and its perceptual nature, provides mechanisms for learning, incorporates processes showing how perception is linked to action and emotion, and how experts capture the entirety of a situation. In doing so, the new theory addresses the issues problematic for Dreyfus’s and Simon’s theories. Implications for research and practice are discussed

Learning-based constraints on schemata

Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models

Rewritable routines in human interaction with public technology

In this paper, the cognitive ergonomics of using public technology is investigated. A methodology for predicting human error with technology has been developed. Predictions from the method (combined with observation of user performance) form the foundation of the concept of ‘rewritable routines’. This is in keeping with the tradition of building models of user cognition on the basis of observed and predicted errors. The concept is introduced and illustrated with examples. Implications for cognitive ergonomics are discussed

Learning physics in context: a study of student learning about electricity and magnetism

This paper re-centres the discussion of student learning in physics to focus on context. In order to do so, a theoretically-motivated understanding of context is developed. Given a well-defined notion of context, data from a novel university class in electricity and magnetism are analyzed to demonstrate the central and inextricable role of context in student learning. This work sits within a broader effort to create and analyze environments which support student learning in the sciencesComment: 36 pages, 4 Figure

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

A literature review of expert problem solving using analogy

We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of non-trivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts’ cognitive processes and software tools contributes to the erratic performance of analogy-based prediction

A pattern-recognition theory of search in expert problem solving

Understanding how look-ahead search and pattern recognition interact is one of the important research questions in the study of expert problem-solving. This paper examines the implications of the template theory (Gobet & Simon, 1996a), a recent theory of expert memory, on the theory of problem solving in chess. Templates are "chunks" (Chase & Simon, 1973) that have evolved into more complex data structures and that possess slots allowing values to be encoded rapidly. Templates may facilitate search in three ways: (a) by allowing information to be stored into LTM rapidly; (b) by allowing a search in the template space in addition to a search in the move space; and (c) by compensating loss in the "mind's eye" due to interference and decay. A computer model implementing the main ideas of the theory is presented, and simulations of its search behaviour are discussed. The template theory accounts for the slight skill difference in average depth of search found in chess players, as well as for other empirical data