Cognitive apprenticeship : teaching the craft of reading, writing, and mathtematics

Includes bibliographical references (p. 25-27)This research was supported by the National Institute of Education under Contract no. US-NIE-C-400-81-0030 and the Office of Naval Research under Contract No. N00014-85-C-002

Emerging technologies in physics education

Three emerging technologies in physics education are evaluated from the interdisciplinary perspective of cognitive science and physics education research. The technologies - Physlet Physics, the Andes Intelligent Tutoring System (ITS), and Microcomputer-Based Laboratory (MBL) Tools - are assessed particularly in terms of their potential at promoting conceptual change, developing expert-like problem-solving skills, and achieving the goals of the traditional physics laboratory. Pedagogical methods to maximize the potential of each educational technology are suggested.Comment: Accepted for publication in the Journal of Science Education and Technology; 20 page

RELATIONSHIP BETWEEN ORGANIZATIONAL CULTURE AND CULTURAL INTELLIGENCE

This article examines one of the key competences of the 21st century, cultural intelligence. In our empirical research studies, we examined the cultural intelligence of full-time university students. We identified the corporate culture they would like to work in, and also examined if there is a correlation between their cultural intelligence and their preference for a particular corporate culture. We found that the majority of student would prefer to be employed in a Clan-type corporate culture. We also identified a correlation between their preferred corporate cultural and their cultural intelligence and its components. Students with a high degree of cultural intelligence would like to work in an adhocracy.Cameron and Quinn, CQS, cultural intelligence, Hungarian university student, OCAI, organizational culture.

Domain-general and Domain-specific Patterns of Activity Support Metacognition in Human Prefrontal Cortex

Metacognition is the capacity to evaluate the success of one's own cognitive processes in various domains; for example, memory and perception. It remains controversial whether metacognition relies on a domain-general resource that is applied to different tasks or if self-evaluative processes are domain specific. Here, we investigated this issue directly by examining the neural substrates engaged when metacognitive judgments were made by human participants of both sexes during perceptual and memory tasks matched for stimulus and performance characteristics. By comparing patterns of fMRI activity while subjects evaluated their performance, we revealed both domain-specific and domain-general metacognitive representations. Multivoxel activity patterns in anterior prefrontal cortex predicted levels of confidence in a domain-specific fashion, whereas domain-general signals predicting confidence and accuracy were found in a widespread network in the frontal and posterior midline. The demonstration of domain-specific metacognitive representations suggests the presence of a content-rich mechanism available to introspection and cognitive control

Is my Aha! bigger than yours? Investigating individual differences in the experience of insight

A Neural Network Theory (NNT) account of insight processes and individual differences in cognitive ability predicts that, compared to routine problem solving, insight experiences will be associated with less involvement of control functions and will occur less frequently among people with greater fluid ability. The present study investigated the role of fluid intelligence and metacognitive control in the elicitation of Aha experiences. Seventy-six participants, predominantly university students (84% female), attempted a set of problems, including classic insight, non-insight and riddles. Subjective experiences of insight, certainty and suddenness of the solution process were measured, using a purpose-built concurrent rating apparatus and retrospective report. Participants completed Raven’s Advanced Progressive Matrices (fluid intelligence) and an attention-switching task (metacognitive control). Hierarchical Generalised Linear Modelling was used to model Aha experiences as a function of item-level predictors (Level 1) and person-characteristics (Level 2). The overall odds of reporting an Aha experience were 0.42. Higher fluid intelligence, but not metacognitive control, was associated with reduced odds of reporting Aha on a problem (OR=0.88, 95% CI: 0.82,0.95), controlling for accuracy, solution suddenness, and verbal skills. Aha experiences were significantly associated with multiple theoretically meaningful retrospective and concurrent problem-solving experience ratings, with fluid intelligence moderating some associations. These findings support the NNT account of insight as a special process and fluid intelligence as a factor limiting the complexity, and accessible solution states from the initial problem representation, leading to the requirement for an alternative representation. The study demonstrates some methodological solutions to difficulties inherent in measuring insight. A chimpanzee named Sultan, two sticks, and a hard-to-reach banana; this is the scenario from which almost a century of research investigating “Aha!” experiences emerged (Ohlsson, 1992). Early Gestalt psychologist Wolfgang Kohler observed that after repeated attempts to reach the banana, Sultan entered a failure-induced sulk. However, he became suddenly re-energised, and purposefully joined two sticks together to retrieve the banana (Kohler, 1921 cited in Ohlsson 1992). How, after numerous attempts and apparent acceptance of failure, did the solution to this intractable problem suddenly appear in Sultan's consciousness? What processes simmering beyond conscious awareness conjured a fully formed solution and planted it so abruptly into Sultan's dormant and tortured mind? "Aha!" Aha experiences are thought to be indicative of a moment of insight and have historically been associated with exceptional creativity, scientific discovery and genius (Hill & Kemp, 2018; Metcalfe & Wiebe, 1987; Shen et al., 2016; Sternberg & Davidson, 1995). These experiences have been shown to be distinct neurophysiological phenomena (Bowden & Jung-Beeman, 2003; Kounios et al., 2006; Sandkühler & Bhattacharya, 2008; Tik et al., 2018) that facilitate memory (Danek et al., 2013), improve learning (Dominowski & Buyer, 2000), and provide motivation during difficult learning (Liljedahl, 2005). Current definitions of insight moments describe them as the occurrence of a solution or path to a solution suddenly and unexpectedly coming to mind following a pause in active thinking when a problem-solver feels unable to make further progress (Bowden et al., 2005; Sternberg & Davidson, 1995). Insight is asserted to be a special process that is distinct from analytical problem-solving (Knoblich et al., 1999; Ohlsson, 1992; Sternberg & Davidson, 1995). Analytical problem-solving is continuous and incremental and does not engender a salient Aha experience (Schooler et al., 1993). Despite a substantial body of research seeking to demystify these processes, the specialness of insight is still the subject of much debate. Recent research suggests that the lack of clarity is a result of the way insight is operationalised in many studies as “solving an insight problem” (Danek et al., 2016; Webb et al., 2016) without verifying that the problem-solver has experienced an Aha moment. These studies indicate that insight is not reliably evoked by these problems, suggesting the processes engaged in solving insight problems may be idiosyncratic (Danek et al., 2016; Webb et al., 2016). That is, it is possible that rather than task requirements, individual differences of the problem-solver influence whether or not insight processes are used to solve a problem and the subsequent occurence of an Aha experience. Two individual characteristics that may be relevant to the Aha experience are fluid intelligence and metacognitive control. Fluid intelligence is defined as the ability to use controlled and deliberate mental operations to solve problems, deduce patterns, identify relations and draw inferences (McGrew, 2009). Metacognitive control is a facet of metacognition that refers explicitly to the control processes involved in regulating and directing information processing resources (Nelson & Narens, 1990). Differing levels of these abilities may influence the cognitive processes engaged during problem-solving (Dix et al., 2016). Thus, the central aim of this study is to investigate whether individual differences in fluid intelligence or metacognitive control influence whether an Aha experience occurs upon problem-solving. The present study agrees with several others that an Aha reported by the problem-solver is verification that insight has occurred (Bowden et al., 2005; Danek et al., 2016; Webb et al., 2016). However, some researchers argue that Aha experiences occur randomly (Chuderski, 2014) or are related to post-solution affect of evaluations of the solution (Topolinski & Reber, 2010). The second aim of the present study is to determine if Aha moments are associated with problem-solving experiences that are indicative of a special process. Due to current limitations in methodology, a new device was developed to accomplish this aim. This is described in section 2.5

Online ill-structured problem-solving strategies and their influence on problem-solving performance

Ill-structured problem-solving ability is key to success in our personal and professional lives. A small but growing body of research has investigated ill-structured problems; however, little if any research has examined strategies that individuals use while solving ill-structured problems in the context of a web-based problem-solving environment. Further, this research has not addressed the relationship between these strategies and problem-solving performance. Two objectives were addressed in the current study. The first objective was to characterize students\u27 ill-structured problem-solving strategies in a Web-based problem-solving environment. Cluster analysis revealed four groups of students who approached the same online problem-solving task in considerably different ways. Some students tended to focus on writing tasks, while others focused on exploring resources. Students also differed on their resource use, and the degree to which they discriminated relevant from irrelevant resources. The second objective was to examine the effect of these problem-solving strategies on students\u27 problem-solving performance. Forced-order hierarchical regression showed that the problem-solving strategies students used were significant predictors of problem solving performance when learner characteristics had been controlled. Results of the current study are discussed in light of previous research, and implications of the study for educators and for problem-solving researchers are presented

Remembering as a mental action

Many philosophers consider that memory is just a passive information retention and retrieval capacity. Some information and experiences are encoded, stored, and subsequently retrieved in a passive way, without any control or intervention on the subject’s part. In this paper, we will defend an active account of memory according to which remembering is a mental action and not merely a passive mental event. According to the reconstructive account, memory is an imaginative reconstruction of past experience. A key feature of the reconstructive account is that given the imperfect character of memory outputs, some kind of control is needed. Metacognition is the control of mental processes and dispositions. Drawing from recent work on the normativity of automaticity and automatic control, we distinguish two kinds of metacognitive control: top-down, reflective control, on the one hand, and automatic, intuitive, feeling-based control on the other. Thus, we propose that whenever the mental process of remembering is controlled by means of intuitive or feeling-based metacognitive processes, it is an action

Active Learning: Effects of Core Training Design Elements on Self-Regulatory Processes, Learning, and Adaptability

This research describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches, their effects on learning and transfer, and the core training design elements (exploration, training frame, emotion-control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for developing an integrated theory of active learning, learner-centered design, and research extensions are discussed