The Role of Motivational Strategies in the Upper Elementary Classroom : A systematic literature review about teacher strategies that promote self-confidence, motivation and a conducive learning environment

The aim of this literature review is to investigate which strategies teachers use to motivate pupils to communicate orally in English. The literature review also investigates how these teacher strategies affect pupils. The methodology used for this investigation is a systematic literature review. Various databases have been used when searching for literature. Scientific articles and theses have been searched for. They have also been read and analyzed before they have become a part of this review. The results indicate that some teachers feel insecure when speaking English. Therefore Swedish is spoken in many language classrooms. Teachers speaking in front of the class is the traditional way of teaching, and it does not seem to be a strategy who influences pupils positively. If teachers speak the target language among pupils they often get more motivated and focused pupils who feel comfortable speaking English. Young pupils are fast learners. By exposing them to the English language in early ages they receive great opportunities to learn a foreign language and strengthen their self-confidence. Drama, songs and rhymes are preferable strategies to use when teaching young learners. What position teachers decide to take in the classroom is also a significant element when teaching foreign languages.

Flipped classroom and learning strategies

This paper seeks to answer the research question "How does the flipped classroom affect students’ learning strategies?" In e-learning research, several studies have focused on how students and teachers perceive the flipped classroom approach. In general, these studies have reported pleasing results. Nonetheless, few, if any, studies have attempted to find out the potential effects of the flipped classroom approach on how students learn. This study was based on two cases: 1) a business modelling course and 2) a research methodology course. In both cases, participating students were from information systems courses at Dalarna University in Sweden. Recorded lectures replaced regular lectures. The recorded lectures were followed by seminars that focused on the learning content of each lecture in various ways. Three weeks after the final seminar, we arranged for two focus group interviews to take place in each course, with 8 to 10 students participating in each group. We asked open questions on how the students thought they had been affected and more dedicated questions that were generated from a literature study on the effects of flipped classroom courses. These questions dealt with issues about mobility, the potential for repeating lectures, formative feedback, the role of seminars, responsibility, empowerment, lectures before seminars, and any problems encountered. Our results show that, in general, students thought differently about learning after the courses in relation to more traditional approaches, especially regarding the need to be more active. Most students enjoyed the mobility aspect and the accessibility of recorded lectures, although a few claimed it demanded a more disciplined attitude. Most students also expressed a feeling of increased activity and responsibility when participating in seminars. Some even felt empowered because they could influence seminar content. The length of and possibility to navigate in recorded lectures was also considered important. The arrangement of the seminar rooms should promote face-to-face discussions. Finally, the types of questions and tasks were found to affect the outcomes of the seminars. The overall conclusion with regard to students’ learning strategies is that to be an active, responsible, empowered, and critical student you have to be an informed student with possibilities and mandate to influence how, where and when to learn and be able to receive continuous feedback during the learning process. Flipped classroom can support such learning.

Japanese Expatriate Managers in Japanese Subsidiaries in Brazil: Why Not Learning-driven?

It has been said that international assignments are traditionally “demand- driven” (DD) that is a way of expatriation of executives focused on control, solution of problems and transference of tacit knowledge. Besides that, according to the international literature, “adjustment” is the determinant factor for success in overseas assignments and Japan usually sends their own executives to subsidiaries in other developing countries for “DD” purpose. However, according to the initial empirical study and in-depth interviews, it seems that Japanese expatriate managers in Brazil were sent to learn how to adjust the local staff to their philosophy instead of adjust themselves to a new complete scenario. This paper found that “adjustment” would not be fundamental for Japanese expatriate manager’s success in their assignments to Brazil, since they were under a special “learning-driven” type of expatriation process. This paper also highlights the challenges of the Japanese expatriate managers in Brazil and their contribution to the development of local staff under the internationalization process.

A framework for learning in humanoid simulated robots

One of the most important characteristics of intelligent activity is the ability to change behaviour according to many forms of feedback. Through learning an agent can interact with its environment to improve its performance over time. However, most of the techniques known that involves learning are time expensive, i.e., once the agent is supposed to learn over time by experimentation, the task has to be executed many times. Hence, high fidelity simulators can save a lot of time. In this context, this paper describes the framework designed to allow a team of real RoboNova-I humanoids robots to be simulated under USARSim environment. Details about the complete process of modeling and programming the robot are given, as well as the learning methodology proposed to improve robot's performance. Due to the use of a high fidelity model, the learning algorithms can be widely explored in simulation before adapted to real robots. © 2008 Springer-Verlag Berlin Heidelberg.

Effect of short training on vaginal fluid microscopy (wet mount) learning

Objective: Is it feasible to learn the basics of wet mount microscopy of vaginal fluid in 10 hours?Materials and Methods: This is a pilot project wherein 6 students with different grades of education were invited for being tested on their ability to read wet mount microscopic slides before and after 10 hours of hands-on training. Microscopy was performed according to a standard protocol (Femicare, Tienen, Belgium). Before and after training, all students had to evaluate a different set of 50 digital slides. Different diagnoses and microscopic patterns had to be scored. kappa indices were calculated compared with the expert reading. Results: All readers improved their mean scores significantly, especially for the most important types of altered flora (p < .0001). The mean increase in reading concordance (kappa from 0.64 to 0.75) of 1 student with a solid previous experience with microscopy did not reach statistical significance, but the remaining 5 students all improved their scores from poor performance (all kappa < 0.20) to moderate (kappa = 0.53, n = 1) to good (kappa > 0.61, n = 4) concordance. Reading quality improved and reached fair to good concordance on all microscopic items studied, except for the detection of parabasal cells and cytolytic flora. Conclusions: Although further improvement is still possible, a short training course of 10 hours enables vast improvement on wet mount microscopy accuracy and results in fair to good concordance of the most important variables of the vaginal flora compared to a reference reader.

Using Cooperative Learning In A Sixth Grade Math Classroom

In this action research study of my classroom of sixth grade mathematics, I investigated the impact of cooperative learning on the engagement, participation, and attitudes of my students. I also investigated the impact of cooperative learning upon my own teaching. I discovered that my students not only preferred to learn in cooperative groups, but that their levels of engagement and participation, their attitudes toward math, and their quality of work all improved greatly. My teaching also changed, and I found that I began to enjoy teaching more. As a result of this research, I plan to continue and expand the amount of cooperative group work that happens in my classroom.

An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.

The Impact of Teaching Presence in Intensive Online Courses on Perceived Learning and Sense of Community: A Mixed Methods Study

This mixed methods concurrent triangulation design study was predicated upon two models that advocated a connection between teaching presence and perceived learning: the Community of Inquiry Model of Online Learning developed by Garrison, Anderson, and Archer (2000); and the Online Interaction Learning Model by Benbunan-Fich, Hiltz, and Harasim (2005). The objective was to learn how teaching presence impacted students’ perceptions of learning and sense of community in intensive online distance education courses developed and taught by instructors at a regional comprehensive university. In the quantitative phase online surveys collected relevant data from participating students (N = 397) and selected instructional faculty (N = 32) during the second week of a three-week Winter Term. Student information included: demographics such as age, gender, employment status, and distance from campus; perceptions of teaching presence; sense of community; perceived learning; course length; and course type. The students claimed having positive relationships between teaching presence, perceived learning, and sense of community. The instructors showed similar positive relationships with no significant differences when the student and instructor data were compared. The qualitative phase consisted of interviews with 12 instructors who had completed the online survey and replied to all of the open-response questions. The two phases were integrated using a matrix generation, and the analysis allowed for conclusions regarding teaching presence, perceived learning, and sense of community. The findings were equivocal with regard to satisfaction with course length and the relative importance of the teaching presence components. A model was provided depicting relationships between and among teaching presence components, perceived learning, and sense of community in intensive online courses.

A dog at the keyboard: using arbitrary signs to communicate requests

As a consequence of domestication, dogs have a special readiness for communication with humans. We here investigate whether a dog might be able to acquire and consistently produce a set of arbitrary signs in her communication with humans, as was demonstrated in ""linguistic"" individuals of several species. A female mongrel dog was submitted to a training schedule in which, after basic command training and after acquiring the verbal labels of rewarding objects or activities, she learned to ask for such objects or activities by selecting lexigrams and pressing keys on a keyboard. Systematic records taken during spontaneous interaction with one of the experimenters showed that lexigrams were used in an appropriate, intentional way, in accordance with the immediate motivational context. The dog only utilized the keyboard in the experimenter`s presence and gazed to him more frequently after key pressing than before, an indication that lexigram use did have communicative content. Results suggest that dogs may be able to learn a conventional system of signs associated to specific objects and activities, functionally analogous to spontaneous soliciting behaviors and point to the potential fruitfulness of the keyboard/lexigram procedure for studying dog communication and cognition. This is the first report to systematically analyze the learning of arbitrary sign production in dogs.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

Motor learning, retention and transfer after virtual-reality-based training in Parkinson's disease - effect of motor and cognitive demands of games: a longitudinal, controlled clinical study

Objectives To evaluate the learning, retention and transfer of performance improvements after Nintendo Wii Fit (TM) training in patients with Parkinson's disease and healthy elderly people. Design Longitudinal, controlled clinical study. Participants Sixteen patients with early-stage Parkinson's disease and 11 healthy elderly people. Interventions Warm-up exercises and Wii Fit training that involved training motor (shifts centre of gravity and step alternation) and cognitive skills. A follow-up evaluative Wii Fit session was held 60 days after the end of training. Participants performed a functional reach test before and after training as a measure of learning transfer. Main outcome measures Learning and retention were determined based on the scores of 10 Wii Fit games over eight sessions. Transfer of learning was assessed after training using the functional reach test. Results Patients with Parkinson's disease showed no deficit in learning or retention on seven of the 10 games, despite showing poorer performance on five games compared with the healthy elderly group. Patients with Parkinson's disease showed marked learning deficits on three other games, independent of poorer initial performance. This deficit appears to be associated with cognitive demands of the games which require decision-making, response inhibition, divided attention and working memory. Finally, patients with Parkinson's disease were able to transfer motor ability trained on the games to a similar untrained task. Conclusions The ability of patients with Parkinson's disease to learn, retain and transfer performance improvements after training on the Nintendo Wii Fit depends largely on the demands, particularly cognitive demands, of the games involved, reiterating the importance of game selection for rehabilitation purposes. (C) 2012 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Automatic (data-based) learning of expressive indicators for machine failure detection : a proposal

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Aktionenlernen mit Selbstorganisierenden Karten und Reinforcement Learning

Die vorliegende Arbeit beschäftigt sich mit der Entwicklung eines Funktionsapproximators und dessen Verwendung in Verfahren zum Lernen von diskreten und kontinuierlichen Aktionen: 1. Ein allgemeiner Funktionsapproximator – Locally Weighted Interpolating Growing Neural Gas (LWIGNG) – wird auf Basis eines Wachsenden Neuralen Gases (GNG) entwickelt. Die topologische Nachbarschaft in der Neuronenstruktur wird verwendet, um zwischen benachbarten Neuronen zu interpolieren und durch lokale Gewichtung die Approximation zu berechnen. Die Leistungsfähigkeit des Ansatzes, insbesondere in Hinsicht auf sich verändernde Zielfunktionen und sich verändernde Eingabeverteilungen, wird in verschiedenen Experimenten unter Beweis gestellt. 2. Zum Lernen diskreter Aktionen wird das LWIGNG-Verfahren mit Q-Learning zur Q-LWIGNG-Methode verbunden. Dafür muss der zugrunde liegende GNG-Algorithmus abgeändert werden, da die Eingabedaten beim Aktionenlernen eine bestimmte Reihenfolge haben. Q-LWIGNG erzielt sehr gute Ergebnisse beim Stabbalance- und beim Mountain-Car-Problem und gute Ergebnisse beim Acrobot-Problem. 3. Zum Lernen kontinuierlicher Aktionen wird ein REINFORCE-Algorithmus mit LWIGNG zur ReinforceGNG-Methode verbunden. Dabei wird eine Actor-Critic-Architektur eingesetzt, um aus zeitverzögerten Belohnungen zu lernen. LWIGNG approximiert sowohl die Zustands-Wertefunktion als auch die Politik, die in Form von situationsabhängigen Parametern einer Normalverteilung repräsentiert wird. ReinforceGNG wird erfolgreich zum Lernen von Bewegungen für einen simulierten 2-rädrigen Roboter eingesetzt, der einen rollenden Ball unter bestimmten Bedingungen abfangen soll.

THE RELATIONSHIP BETWEEN PARENTING STYLES, LEARNING AUTONOMY, AND SCHOLASTIC ACHIEVEMENT IN UNDERGRADUATE COLLEGE STUDENTS

Throughout the years, the role that parents play with regard to a child’s academic achievement has been the source of considerable research. The type of parenting style employed by parents, whether it is authoritarian, authoritative, or permissive, has and continues to be a major theme in these studies. One area of particular interest that has been overlooked in these studies, however, is the influence that parents may have on a student’s learning autonomy. Learning autonomy is the idea that a student has internal motivation to learn or achieve. The purpose of this study was to investigate therelationship among the three styles of parenting, learning autonomy, perceived parental autonomy support, and scholastic achievement in undergraduate college students. Sixty-one participants were recruited at a small liberal arts college in the northeastern United States to complete questionnaires, which measured perceived parental authority of the participants’ parents, perceived parental autonomy support, and students’ own learning autonomy. The participants were also asked to list their grade point average. The results revealed positive and negative correlations between many of the variables in the study;however, simple regression analyses did not yield any statistically significant relationships between parental authority, learning autonomy, perceived autonomy support, and scholastic achievement.