An inquisitive investigation into the effects of self-regulated learning

The purposes of this inquisitive investigation were to (a) gain an insight into the self-regulation strategies that third grade students naturally engage in, (b) to teach specific self-regulation strategies to these students, and to (c) explore the effects of utilizing such strategies on different aspects of academic performance. Throughout the study students demonstrated a wide range of strategy use, before and after teaching about self-regulation. The majority of the data collected illustrates several positive impacts on both test grades and homework completion for the 14 participants in the study, who are in the third grade, when multiple self-regulation strategies are used to study or complete assignments at home. Data in the form of pre and post surveys, group discussions, interviews, test grades, and homework assignments are analyzed. The results of the study illustrate several patterns related to self-regulation strategy use and academic performance. Essentially the data shows that when students use different self-regulation strategies in and out of the classroom, there are positive effects. Such positive effects, discussed in more detail in chapter 4, include higher grades on tests, more accurate and thorough homework completion, and a greater level of independence and personal accountability during independent work time. Results of the study and implications for future teachers or researchers interested in the topic, which includes holding a workshop for other faculty members about self-regulation and perhaps teaching these skills to your students, are subsequently discussed in more detail

Clashes in the Infosphere, General Intelligence, and Metacognition: Final project report

Humans confront the unexpected every day, deal with it, and often learn from it. AI agents, on the other hand, are typically brittle—they tend to break down as soon as something happens for which their creators did not explicitly anticipate. The central focus of our research project is this problem of brittleness which may also be the single most important problem facing AI research. Our approach to brittleness is to model a common method that humans use to deal with the unexpected, namely to note occurrences of the unexpected (i.e., anomalies), to assess any problem signaled by the anomaly, and then to guide a response or solution that resolves it. The result is the Note-Assess-Guide procedure of what we call the Metacognitive Loop or MCL. To do this, we have implemented MCL-based systems that enable agents to help themselves; they must establish expectations and monitor them, note failed expectations, assess their causes, and then choose appropriate responses. Activities for this project have developed and refined a human-dialog agent and a robot navigation system to test the generality of this approach

Metacognitive Decision Making Framework for Multi-UAV Target Search Without Communication

This paper presents a new Metacognitive Decision Making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of "which-sensor-to-use", "when-to-switch-sensor", and "how-to-search". Each sensor possesses inverse characteristics for the sensing attributes like sensing range and accuracy. Based on the information gathered by multiple sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching. The lower levels of search aim to localize the search space for the possible presence of a target (detection) with different sensors. The highest level of a search exploits the search space for target confirmation using the sensor with the highest accuracy among all sensors. The performance of the MDM framework with two sensors having low accuracy with wide range sensor for detection and increased accuracy with low range sensor for confirmation is evaluated through Monte-Carlo simulations and compared with six multi-UAV stochastic search algorithms (three self-cognitive searches and three self and social-cognitive based search). The results indicate that the MDM framework is efficient in detecting and confirming targets in an unknown environment.Comment: 12 pages, 9 figures, 9 table

Developing adult second language learner autonomy through the use of self-reflection activities within literature circles

214 Páginas.Este proyecto de investigación indaga acerca del impacto que el uso de actividades de auto-reflexión implementadas dentro de círculos de lectura ejerce en la autonomía de los adultos aprendices de una segunda lengua. En este sentido, la información fue recolectada por medio de dos entrevistas semi-estructuradas para comparar el entendimiento de los aprendices acerca de los círculos de lectura, la auto-reflexión y la autonomía del aprendiz al inicio y al final de la intervención pedagógica, seis cuestionarios para orientar a los aprendices sobre como evaluar su desempeño en los círculos de lectura, un diario para los estudiantes para obtener el análisis general de los estudiantes acerca de sus áreas de dificultad, fortalezas y estrategias y seis video grabaciones de cada sesión para analizar las prácticas de aprendizaje autónomo. La información recogida y posteriormente analizada demostró que el uso de actividades de auto-reflexión contribuyen positivamente al desarrollo de un aprendizaje más autónomo en los aprendices adultos de una segunda lengua. En el caso específico de esta implementación de círculos de lectura, el uso de actividades de auto-reflexión motivó a los participantes a auto-regular (monitorear, evaluar y retroalimentar) su desempeño permitiéndoles identificar y abordar sus fortalezas y desafíos, y ser conscientes del impacto de la toma de decisiones en el desempeño de los demás aprendices. En consecuencia, los aprendices adultos de una segunda lengua fueron provistos de la autonomía necesaria para continuar su aprendizaje en otros contextos

Metacognition for Self-Regulated Learning in a Dynamic Environment

This paper describes a self-regulated learning system that uses metacognition to decide what to learn, when to learn and how to learn in order to succeed in a dynamic environment. Metacognition provides the system the ability to monitor anomalies and to dynamically change its behavior to fix or work around them. The dynamic environment for the system is an air traffic control domain that has six approach vectors for planes to land. The system has access to three basic approach strategies for choosing a landing terminal: Nearest Terminal, Free Terminal and Queued Terminal. In addition, the system has access to a supervised-learning algorithm that can be used to create new strategies. The system has the ability to generate its own training data sets to train the supervisedlearner. The metacognitive component of the system monitors various expectations; anomalies in the environment cause expectation violations. These expectation violations act as indicators for what, when and how to learn. For instance, if an expectation violation occurs because aircraft are not being assigned approach vectors within a given time threshold, the system automatically triggers a change in landing strategies. Examples of anomalies that cause expectation violations include closing one or more of the six approach vectors or changing all of their geographical locations simultaneously. In either case, the system will respond to the situation by assigning the planes to one of the currently active approach vectors