Timothy J. Nokes

Expertise promotes facilitation on a collaborative memory task

The effect of expertise on collaborative memory was examined by comparing expert pilots, novice pilots, and non-pilots. Participants were presented with aviation scenarios and asked to recall the scenarios alone or in collaboration with a fellow participant of the same expertise level. Performance in the collaborative condition was compared to nominal group conditions (i.e., pooled individual performance). Results suggest that expertise differentially impacts collaborative memory performance. Non-experts (non-pilots and novices) were relatively disrupted by collaboration, while experts showed a benefit of collaboration. Verbal protocol analyses identified mechanisms related to collaborative skill and domain knowledge that may underlie experts’ collaborative success. Specifically, experts were more likely than non-experts to explicitly acknowledge partner contributions by repeating back previously made statements, as well as to further elaborate on concepts in those contributions. The findings are interpreted according to the retrieval strategy disruption theory of collaborative memory and theories of grounding in communication.

Mechanisms of knowledge transfer

A central goal of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes activated for a given situation depends on both (a) the type of knowledge to be transferred and how it is represented, and (b) the processing demands of the transfer task. This hypothesis was investigated in two laboratory training studies. The results from Experiment 1 show that each mechanism predicts specific behavioural patterns of performance across a common set of transfer tasks. The results from Experiment 2 show that people can adaptively shift between transfer mechanisms depending on their prior knowledge and the characteristics of the task environment. A framework for the development of a general theory of transfer based on multiple mechanisms is proposed and implications of the theory are discussed for measuring and understanding knowledge transfer.

Comparing Multiple Paths to Mastery: What is Learned?

Contemporary theories of learning postulate one or at most a small number of different learning mechanisms. However, people are capable of mastering a given task through qualitatively different learning paths such as learning by instruction and learning by doing. We hypothesize that the knowledge acquired through such alternative paths differs with respect to the level of abstraction and the balance between declarative and procedural knowledge. In a laboratory experiment we investigated what was learned about patterned letter sequences via either direct instruction in the relevant patterns or practice in solving letter-sequence extrapolation problems. Results showed that both types of learning led to mastery of the target task as measured by accuracy performance. However, behavioral differences emerged in how participants applied their knowledge. Participants given instruction showed more variability in the types of strategies they used to articulate their knowledge as well as longer solution times for generating the action implications of that knowledge as compared to the participants given practice. Results are discussed regarding the implications for transfer, generalization, and procedural application. Learning theories that claim generality should be tested against cross-scenario phenomena, not just parametric variations of a single learning scenario.

Problem Solving and Human Expertise

Developing high-level problem-solving skill is critical to successfully perform a variety of tasks in both formal (e.g., school and work) and informal (e.g., home) settings. One way to understand how people acquire such skills is to examine research on expertise in problem solving. In this article, we provide an integrative review of the psychological research on expert problem solving, describing in detail what it is, how it is acquired, and the implications for education and instruction.

Concept and Category Learning in Humans

Concept and category learning is critical for intelligent thought and action. However, much of the laboratory work has focused only on classification learning, how people learn to assign category membership. This chapter attempts to integrate concept and category learning to the many cases in which it is critical in three ways. First, we examine other ways of learning, in which the categories are used to accomplish a goal. Second, we investigate more complex concepts and the use of prior knowledge. Third, we briefly consider the role of concept and category learning in problem solving and language use.

Facilitating Conceptual Learning Through Analogy And Explanation

Research in cognitive science has shown that students typically have a difficult time acquiring deep conceptual understanding in domains like mathematics and physics and often rely on textbook examples to solve new problems. The use of prior examples facilitates learning, but the advantage is often limited to very similar problems. One reason students rely so heavily on using prior examples is that they lack a deep understanding for how the principles are instantiated in the examples. We review and present research aimed at helping students learn the relations between principles and examples through generating explanations and making analogies.

Chapter Four - Incorporating Motivation into a Theoretical Framework for Knowledge Transfer

Abstract Knowledge transfer is critical to successfully solving novel problems and performing new tasks. Several theories have been proposed to account for how, when, and why transfer occurs. These include both classical cognitive theories such as identical rules, analogy, and schemas, as well as more recent views such as situated transfer and preparation for future learning. Although much progress has been made in understanding specific aspects of transfer phenomena, important challenges remain in developing a framework that can account for both transfer successes and failures. Surprisingly, few of these approaches have integrated motivational constructs into their theories to address these challenges. In this chapter, we propose a theoretical framework that builds on the classical cognitive approaches and incorporates aspects of competence motivation. In the first part of the chapter we review the classical and alternative views of transfer and discuss their successes and limitations. We then describe our transfer framework that begins to address some of the issues and questions that are raised by the alternative views. In the second part, we describe how our proposed framework can incorporate aspects of competence motivation—specifically, students’ achievement goals. We then describe an initial test of the framework and the implications for both psychological theory and educational practice.

Investigating the role of instructional focus in incidental pattern learning.

ABSTRACT The authors used a novel dual-component training procedure that combined a serial reaction time task and an artificial grammar learning task to investigate the role of instructional focus in incidental pattern learning. In Experiment 1, participants either memorized letter strings as a primary task and reacted to the stimuli locations as a secondary task or vice versa. In Experiment 2, participants were given the same dual-component stimuli but performed only one of the two training tasks. Instructional focus affected the amount of learning and the likelihood of acquiring explicit knowledge of the underlying pattern. However, the effect of instructional focus varied for the different types of stimuli. These results are discussed in terms of the role of focused attention in incidental learning.

Collaborative Dialog While Studying Worked-out Examples

Self-explaining is a beneficial learning strategy for studying worked-out examples because it either supplies missing information through the generation of inferences or because it provides a mechanism for repairing flawed mental models. Although self-explanation is generated with the purpose of helping the individual, is it also helpful to produce explanations in a collaborative setting? Can individuals help each other infer missing information or repair their flawed mental models collaboratively? To find out, we coded the dialog from dyads collaboratively studying examples and contrasted it with individuals studying examples alone. The results suggest that dyads were more likely to attempt to reconcile the examples with their attempted solutions, and avoid shallow processing of examples through paraphrasing.