Narrow Results

This paper contributes to research on metacognitions which focuses on cognitive processes that enable entrepreneurs to create new ventures and to think beyond existing knowledge structures to become adaptable in an entrepreneurial context. Two streams of research, entrepreneurial intentions and metacognitions, jointly inform the hypotheses. Individuals with entrepreneurial intent apply some degree of conscious consideration to the possibility of starting a new business, where these intentions are the result of metacognitions. The empirical evidence ensuing from this study indicates that only the knowledge metacognitive dimension is a significant predictor of entrepreneurial intentions. The study makes a contribution in that the articulation and testing of metacognitive dimensions provides a meaningful categorization, where there are many opportunities for educators to develop skill building exercises that target metacognitions.

The concept of metacompetencies has not much attracted entrepreneurship research though indicated having a fundamental role in successful entrepreneurial activity. The few studies have so far based on student samples only, so there is no empirical data demonstrating how this theoretical construct is practically used in entrepreneurship. Moreover, the question being in the focus of this paper, is how metacompetencies differ when comparing groups of entrepreneurs and students. This study aims to look into these gaps.

Assessing metacompetencies empirically by employing the phenomenological mode of enquiry we can explore the phenomena of metacompetencies in an entrepreneurial setting by studying thirty in-depth interviews with active entrepreneurs. It appears that metacompetencies are in frequent practical use and, more specifically this paper establishes how metacognition, meta-affection, and metaconation interact within the environment of entrepreneurship. Confronted with similar results from student reflections, the comparison reveals important discrepancies in terms of procedural knowledge and an awareness of motivation and temperament for educators to address in order to foster entrepreneurial behaviour.

We have developed a learning environment where students teach a computer agent using visual representations, and can monitor the agent's learning progress by asking her questions and having her take quizzes. The system provides self-regulated learning and metacognitive support via dialog-embedded prompts from Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning of complex science topics in middle school classrooms and facilitate development of metacognitive skills to support future learning. In this paper, we discuss methods that we have employed for detecting and characterizing students' behavior patterns from their activity sequences on the system. In particular, we discuss a method for learning hidden Markov models (HMM) from the activity logs. We demonstrate that the HMM structure corresponds to students' aggregated behavior patterns in the learning environment. Overall, the HMM technique allows us to go beyond simple frequency and sequence analyses, such as individual activity and pre-defined pattern counts, instead using exploratory methods to examine how these activities cohere in larger patterns over time. The paper outlines a study conducted in a 5th grade science classroom, presents the models derived from the students' activity sequences, interprets the model structure as aggregate patterns of their learning behaviors, and links these patterns to students' use of self-regulated learning strategies. The results illustrate that those who teach an agent demonstrate better learning performance and better use of metacognitive monitoring behaviors than students who only learn for themselves. We also observed more advanced and focused monitoring behaviors in the students who received metacognitive strategy feedback from the mentor agent while they taught the teachable agent.

GMU-BICA, the biologically-inspired self-aware cognitive architecture developed at George Mason University, continues to be a useful prototype for various intelligent artifacts, including intelligent tutoring systems, yet the underlying formalism of mental states used in its design was never described in detail. The present theoretical work aims at filling this gap, focusing on the top cognitive level (mental states) and leaving detailed description of the lower level (schemas), as well as non-declarative components, for future publications. Among the distinguishing features of the GMU-BICA mental state formalism are: (i) a subject-centered view of the world, (ii) the multiplicity of mental perspectives simultaneously represented in working memory, each playing its unique functional role, and (iii) the limited span of awareness. This model is consistent with human psychology and gives testable predictions. The work explains, through analysis of examples, how the framework can be used to build computational models of self-regulated learning, and why in this case it is expected to unleash a new for artifacts power of human-like cognition and learning.

Evolutionary and cognitive examples are used to motivate an approach to the brittleness problem and automated flexible cognition, centering on the notion of an anomaly as the key focus of processing. Ongoing investigations based on this approach are discussed, including hypotheses regarding its promise for robust versatile machines.

We describe a novel affect-inspired mechanism to improve the performance of computational systems operating in dynamic environments. In particular, we designed a mechanism that is based on aspects of the fear response in humans to dynamically reallocate operating system-level central processing unit (CPU) resources to processes as they are needed to deal with time-critical events. We evaluated this system in the MINIX^® and Linux^® operating systems and in three different testing environments (two simulated, one live). We found the affect-based system was not only able to react more rapidly to time-critical events as intended, but since the dynamic processes for handling these events did not need to use significant CPU when they were not in time-critical situations, our simulated unmanned aerial vehicle (UAV) was able to perform even non-emergency tasks at a higher level of efficiency and reactivity than was possible in the standard implementation.

Problem solving has been the central focus of the Singapore mathematics curriculum and it is supported by five inter-related components: Concepts, Skills, Processes, Metacognition and Attitude. Pólya’s Four Phases of Mathematical Problem Solving is a problem solving approach that Singapore teachers are familiar with, especially using heuristics such as ‘work backwards’, ‘look for a pattern’ to solve problems. Few teachers have related the Pólya’s approach to the development of metacognition in mathematical problem solving especially during the ‘understand the problem’ phase. This chapter discusses how this problem solving approach can enhance problem solving ability by helping students to be more conscious of their metacognitive processes such as awareness, monitoring and regulating of their thinking processes involved in problem solving.

This chapter discusses how a lesson closure, which should be present in every Mathematics lesson, can be carried out effectively to promote offline metacognition. According to the theory of constructivism, learning is achieved through making connection between the new concept and a learner’s prior knowledge, or schema. Lesson closure is crucial to provide a time and space for students to consolidate their learning and acquire the mathematical concepts. We will introduce a strategy to close a lesson in which the teacher uses students’ reflection of their learning to create a visual representation, known as a closure diagram. This activity provides a structure that allows students to either integrate the knowledge into their schema or identify gaps in their learning. Both situations involve students regulating their thinking, which is metacognitive in nature. Variations of the closure strategy for different purposes will also be discussed and illustrated with artefacts from classroom lessons.

Our empirical study explores how social entrepreneurs’ metacognition — awareness and regulation of thoughts and feelings — affects the entrepreneurial process of social and environmental value creation that supports positive socioeconomic change toward sustainability-as-flourishing (defined below). We use an inductive design that yields patterns across five German cases of social entrepreneurship at different levels of analysis. Findings at the individual level reveal social entrepreneurs’ metacognitive abilities facilitated awareness of discontent about social and environmental problems. These abilities included perspective-taking, empathy, suspending rushed judgment, seeing a bigger picture, and reducing ego-defensiveness. The discontent then activated a creative social entrepreneurial response. Findings at the enterprise level show how social entrepreneurs resolved conflicts with recalcitrant external stakeholders by calming stakeholders’ anxieties and negativity. At a societal level, social entrepreneurs were shown to co-create change by consciously investing in and empowering external stakeholders. We induce theory that depicts how the individual, enterprise, and societal levels interact, thus drawing a more complete picture of the social and environmental value creation process. By considering entrepreneurs’ metacognition, this study extends the predominant research focus from external, objective entrepreneurial phenomena to the internal, subjective dimension of social entrepreneurs. It offers insight into the process of how enterprises could potentially contribute to a flourishing future for humans and the planet.

Since the adoption of problem solving as the goal of mathematics education in the local curriculum from primary to secondary schools in the early 90's, the theme “problem solving in mathematics” has captured the research interest of many teachers and researchers. This review looks at various aspects of research in problem solving in mathematics under three broad strands: (a) problem solving approaches and tasks, (b) teachers' beliefs and practices, and (c) students' problem solving behaviours. Within each strand, related researches are described under various sub-themes. The final section draws together the key findings and highlights recommendations for future research.

Learning Support for Mathematics (LSM) provides for the early identification of and intervention for pupils who are at risk of encountering mathematics difficulties. Pupils are systematically screened in the first year of school using the School Readiness Test (Mathematics). Individualised support is provided in small groups by specially selected teachers. A holistic approach to intervention is advocated in LSM, addressing the development of cognitive and metacognitive skills and pupils' motivation to learn, and capitalising on resources in the environment and the larger community. The effectiveness of LSM will be assessed through a monitoring and evaluation framework that has been designed specifically for LSM. To ensure that LSM teachers are well-equipped for their role, they are provided with training, mentoring, and on-going professional development opportunities.

Metacognition is a capacity that allows us to represent ourselves as associated with what surrounds us, or others, draws up hypotheses about the meaning of our conduct, and behavior of others (or individually or in between them). Can also be defined as the ability to predict, explain or interpret actions of other people attributing to them beliefs, intentions, wishes. Closely related to metacognitive ability, is empathy whose more complete definition is conceived as: the capacity of identification with moods and with the thoughts of other people on the basis of understanding of their emotional signals, recruitment of their subjective perspective, sharing their sentiments. It is unclear whether metacognition and its deficits can be considered as a unitary phenomenon, or its dysfunction may depend on more basic cognitive deficits (such as attentive, learning, perceptual, memory, problem solving strategies, and executive function deficits). Proposed here is a systemic model of metacognition.