Pedagogical techniques and student outcomes in applied instrumental lessons taught by experienced and pre-service American music teachers.

The purpose of the study was to examine the effect of teacher experience on student progress and performance quality in an introductory applied lesson. Nine experienced teachers and 15 pre-service teachers taught an adult beginner to play ‘Mary Had a Little Lamb’ on a wind instrument. The lessons were videotaped for subsequent analysis of teaching behaviors and performance achievement. Following instruction, a random sample of teachers was interviewed about their perceptions of the lesson. A panel of adjudicators rated final pupil performances. No significant difference was found between pupils taught by experienced and pre-service teachers in the quality of their final performance. Systematic observation of the videotaped lessons showed that participant teachers provided relatively frequent and highly positive reinforcement during the lessons. Pupils of experienced teachers talked significantly more during the lessons than did pupils of pre-service teachers. Pre-service teachers modeled significantly more on their instruments than did experienced teachers.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

(Lifelong) learning in your pocket?

Der Beitrag fokussiert die Entwicklung, den Einsatz und die Nutzung von innovativen Technologien zur Unterstützung von Bildungsszenarien in Schule, Hochschule und Weiterbildung. Ausgehend von den verschiedenen Phasen des Corporate Learning, Social Learning, Mobile Learning und Intelligent Learning wird in einem ersten Abschnitt das Nutzungsverhalten von Technologien durch Kinder, Jugendliche und (junge) Erwachsene in Schule, Studium und Lehre betrachtet. Es folgt die Darstellung technologischer Entwicklungen auf Basis des Technology Life Cycle und die Konsequenzen von unterschiedlichen Entwicklungszuständen und Reifegraden von Technologien wie Content Learning Management, sozialen Netzwerken, mobilen Endgeräten, multidimensionalen und -modalen Räumen bis hin zu Anwendungen augmentierter Realität und des Internets der Dinge, Dienste und Daten für den Einsatz und die Nutzung in Bildungsszenarien. Nach der Darstellung von Anforderungen an digitale Technologien hinsichtlich Inhalte, Didaktik und Methodik wie etwa hinsichtlich der Erstellung von Inhalten, deren Wiederverwendung, Digitalisierung und Auffindbarkeit sowie Standards werden methodische Hinweise zur Nutzung digitaler Technologien zur Interaktion von Lernenden, von Lehrenden, sozialer Interaktion, kollaborativem Autorieren, Kommentierung, Evaluation und Begutachtung gegeben. Abschließend werden - differenziert für Schule und Hochschule - Erkenntnisse zu Rahmenbedingungen, Einflussgrößen, hemmenden und fördernden Faktoren sowie Herausförderungen bei der Einführung und nachhaltigen Implementation digitaler Technologien im schulischen Unterricht, in Lehre, Studium und Weiterbildung im Überblick zusammengefasst.

Learning in Virtual Worlds: Research and Applications

Three-dimensional (3D) immersive virtual worlds have been touted as being capable of facilitating highly interactive, engaging, multimodal learning experiences. Much of the evidence gathered to support these claims has been anecdotal but the potential that these environments hold to solve traditional problems in online and technology-mediated education—primarily learner isolation and student disengagement—has resulted in considerable investments in virtual world platforms like Second Life, OpenSimulator, and Open Wonderland by both professors and institutions. To justify this ongoing and sustained investment, institutions and proponents of simulated learning environments must assemble a robust body of evidence that illustrates the most effective use of this powerful learning tool. In this authoritative collection, a team of international experts outline the emerging trends and developments in the use of 3D virtual worlds for teaching and learning. They explore aspects of learner interaction with virtual worlds, such as user wayfinding in Second Life, communication modes and perceived presence, and accessibility issues for elderly or disabled learners. They also examine advanced technologies that hold potential for the enhancement of learner immersion and discuss best practices in the design and implementation of virtual world-based learning interventions and tasks. By evaluating and documenting different methods, approaches, and strategies, the contributors to Learning in Virtual Worlds offer important information and insight to both scholars and practitioners in the field. AU Press is an open access publisher and the book is available for free in PDF format as well as for purchase on our website: http://bit.ly/1W4yTRA

Ethics in science: enhancing the democratisation of knowledge production in transdisciplinary research partnerships for sustainable development

Better access to knowledge and knowledge production has to be reconsidered as key to successful individual and social mitigation and adaptation strategies for global change. Indeed, concepts of sustainable development imply a transformation of science towards fostering democratisation of knowledge production and the development of knowledge societies as a strategic goal. This means to open the process of scientific knowledge production while simultaneously empowering people to implement their own visions for sustainable development. Advocates of sustainability science support this transformation. In transdisciplinary practice, they advance equity and accountability in the access to and production of knowledge at the science–society interface. UNESCO points to advancements, yet Northern dominance persists in knowledge production as well as in technology design and transfer. Further, transdisciplinary practice remains experimental and hampered by inadequate and asymmetrically equipped institutions in the North and South and related epistemological and operational obscurity. To help identify clear, practicable transdisciplinary approaches, I recommend examining the institutional route – i.e., the learning and adaptation process – followed in concrete cases. The transdisciplinary Eastern and Southern Africa Partnership Programme (1998–2013) is a case ripe for such examination. Understanding transdisciplinarity as an integrative approach, I highlight ESAPP’s three key principles for a more democratised knowledge production for sustainable development: (1) integration of scientific and “non-scientific” knowledge systems; (2) integration of social actors and institutions; and (3) integrative learning processes. The analysis reveals ESAPP’s achievements in contributing to more democratic knowledge production and South ownership in the realm of sustainable development.

Community College Student Participation in Undergraduate Research: An Explanatory Case Study for Faculty and Research Mentors

This study adapted the current model of science undergraduate research experiences (URE's) and applied this novel modification to include community college students. Numerous researchers have examined the efficacy of URE's in improving undergraduate retention and graduation rates, as well as matriculation rates for graduate programs. However, none have detailed the experience for community college students, and few have employed qualitative methodologies to gather relevant descriptive data from URE participants. This study included perspectives elicited from both non-traditional student participants and the established laboratory community. The purpose of this study was to determine the effectiveness of the traditional model for a non-traditional student population. The research effort described here utilized a qualitative design and an explanatory case study methodology. Six non-traditional students from the Maine Community College System participated in this study. Student participants were placed in six academic research laboratories located throughout the state. Student participants were interviewed three times during their ten-week internship and asked to record their personal reflections in electronic format. Participants from the established research community were also interviewed. These included both faculty mentors and other student laboratory personnel. Ongoing comparative analysis of the textual data revealed that laboratory organizational structure and social climate significantly influence acculturation outcomes for non-traditional URE participants. Student participants experienced a range of acculturation outcomes from full integration to marginalization. URE acculturation outcomes influenced development of non-traditional students? professional and academic self-concepts. Positive changes in students? self-concepts resulted in greater commitment to individual professional goals and academic aspirations. The findings from this study suggest that traditional science URE models can be successfully adapted to meet the unique needs of a non-traditional student population – community college students. These interpretations may encourage post-secondary educators, administrators, and policy makers to consider expanded access and support for non-traditional students seeking science URE opportunities.

Cognition and Student Learning Through the Arts

An increasing number of recent research studies suggest connections between cognition, social and emotional development, and the arts. Some studies indicate that students in schools where the arts are an integral part of the academic program tend to do better in school than those students where that is not the case. This study examines home/school factors that contribute most to variance in student learning and achievement and the arts from over 8,000 students in grade 5. The findings suggest in-school arts programs may have less of an impact on student achievement than proposed by previous research.

Student Performance in Traditional vs. Online Format: Evidence from an MBA Level Introductory Economics Class

All previous studies comparing online and face-to-face format for instruction of economics compared courses that were either online or face-to-face format and regressed exam scores on selected student characteristics. This approach is subject to the econometric problems of self-selection omitted unobserved variables. Our study uses two methods to deal with these problems. First we eliminate self-selection bias by using students from a course that uses both instruction formats. Second, we use the exam questions as the unit of observation, and eliminate omitted variable bias by using an indicator variable for each student to capture the effect of differences in unobserved student characteristics on learning outcomes. We report the finding that students had a significantly greater chance of answering a question correctly if it came from a chapter covered online.

Active Learning in Nursing Education

ALINE is a pedagogical model developed to aid nursing faculty transition from passive to active learning. Based on constructionist theory, ALINE serves as a tool for organizing curriculum for online and classroom based interaction and permits positioning the student as the active player and the instructor, the facilitator to nursing competency.

Project Management Learning in a Collaborative Distant Learning Context - An Actual On-going Experience

The goal of this paper is to show the results of an on-going experience on teaching project management to grade students by following a development scheme of management related competencies on an individual basis. In order to achieve that goal, the students are organized in teams that must solve a problem and manage the development of a feasible solution to satisfy the needs of a client. The innovative component advocated in this paper is the formal introduction of negotiating and virtual team management aspects, as different teams from different universities at different locations and comprising students with different backgrounds must collaborate and compete amongst them. The different learning aspects are identified and the improvement levels are reflected in a rubric that has been designed ad hoc for this experience. Finally, the effort frameworks for the student and instructor have been established according to the requirements of the Bologna paradigms. This experience is developed through a software-based support system allowing blended learning for the theoretical and individual?s work aspects, blogs, wikis, etc., as well as project management tools based on WWW that allow the monitoring of not only the expected deliverables and the achievement of the goals but also the progress made on learning as established in the defined rubric

Linear Bayes policy for learning in contextual-bandits

Machine and Statistical Learning techniques are used in almost all online advertisement systems. The problem of discovering which content is more demanded (e.g. receive more clicks) can be modeled as a multi-armed bandit problem. Contextual bandits (i.e., bandits with covariates, side information or associative reinforcement learning) associate, to each specific content, several features that define the “context” in which it appears (e.g. user, web page, time, region). This problem can be studied in the stochastic/statistical setting by means of the conditional probability paradigm using the Bayes’ theorem. However, for very large contextual information and/or real-time constraints, the exact calculation of the Bayes’ rule is computationally infeasible. In this article, we present a method that is able to handle large contextual information for learning in contextual-bandits problems. This method was tested in the Challenge on Yahoo! dataset at ICML2012’s Workshop “new Challenges for Exploration & Exploitation 3”, obtaining the second place. Its basic exploration policy is deterministic in the sense that for the same input data (as a time-series) the same results are obtained. We address the deterministic exploration vs. exploitation issue, explaining the way in which the proposed method deterministically finds an effective dynamic trade-off based solely in the input-data, in contrast to other methods that use a random number generator.

Applying concept mapping strategies to improve students’ lifelong learning in physical chemistry

Este artículo ofrece una reflexión sobre el papel de los mapas conceptuales en el actual escenario de la educación In the present paper, we carry out the application of concept mapping strategies to learning Physical Chemistry, in particular, of all aspect of Corrosion. This strategy is an alternative method to supplement examinations: it can show the teacher how much the students knew and how much they didn´t know; and the students can evaluate their own learning. Before giving tile matter on Corrosion, the teachers evaluated the previous knowledge of the students in the field and explained to the students how create the conceptual maps with Cmap tools. When the subject is finished, teachers are assessed the conceptual maps developed by students and therefore also the level of the students learning. Teachers verified that the concept mapping is quite suitable for complicated theorics as Corrosion and it is an appropriate tool for the consolidation of educational experiences and for improvement affective lifelong learning. By using this method we demonstrated that the set of concepts accumulated in the cognitive structure of every student in unique and every student has therefore arranged the concepts from top to bottom in the mapping field in different ways with different linking" phrases, although these are involved in the same learning task.

Machine Learning in Scientometrics

El aprendizaje automático y la cienciometría son las disciplinas científicas que se tratan en esta tesis. El aprendizaje automático trata sobre la construcción y el estudio de algoritmos que puedan aprender a partir de datos, mientras que la cienciometría se ocupa principalmente del análisis de la ciencia desde una perspectiva cuantitativa. Hoy en día, los avances en el aprendizaje automático proporcionan las herramientas matemáticas y estadísticas para trabajar correctamente con la gran cantidad de datos cienciométricos almacenados en bases de datos bibliográficas. En este contexto, el uso de nuevos métodos de aprendizaje automático en aplicaciones de cienciometría es el foco de atención de esta tesis doctoral. Esta tesis propone nuevas contribuciones en el aprendizaje automático que podrían arrojar luz sobre el área de la cienciometría. Estas contribuciones están divididas en tres partes: Varios modelos supervisados (in)sensibles al coste son aprendidos para predecir el éxito científico de los artículos y los investigadores. Los modelos sensibles al coste no están interesados en maximizar la precisión de clasificación, sino en la minimización del coste total esperado derivado de los errores ocasionados. En este contexto, los editores de revistas científicas podrían disponer de una herramienta capaz de predecir el número de citas de un artículo en el fututo antes de ser publicado, mientras que los comités de promoción podrían predecir el incremento anual del índice h de los investigadores en los primeros años. Estos modelos predictivos podrían allanar el camino hacia nuevos sistemas de evaluación. Varios modelos gráficos probabilísticos son aprendidos para explotar y descubrir nuevas relaciones entre el gran número de índices bibliométricos existentes. En este contexto, la comunidad científica podría medir cómo algunos índices influyen en otros en términos probabilísticos y realizar propagación de la evidencia e inferencia abductiva para responder a preguntas bibliométricas. Además, la comunidad científica podría descubrir qué índices bibliométricos tienen mayor poder predictivo. Este es un problema de regresión multi-respuesta en el que el papel de cada variable, predictiva o respuesta, es desconocido de antemano. Los índices resultantes podrían ser muy útiles para la predicción, es decir, cuando se conocen sus valores, el conocimiento de cualquier valor no proporciona información sobre la predicción de otros índices bibliométricos. Un estudio bibliométrico sobre la investigación española en informática ha sido realizado bajo la cultura de publicar o morir. Este estudio se basa en una metodología de análisis de clusters que caracteriza la actividad en la investigación en términos de productividad, visibilidad, calidad, prestigio y colaboración internacional. Este estudio también analiza los efectos de la colaboración en la productividad y la visibilidad bajo diferentes circunstancias. ABSTRACT Machine learning and scientometrics are the scientific disciplines which are covered in this dissertation. Machine learning deals with the construction and study of algorithms that can learn from data, whereas scientometrics is mainly concerned with the analysis of science from a quantitative perspective. Nowadays, advances in machine learning provide the mathematical and statistical tools for properly working with the vast amount of scientometrics data stored in bibliographic databases. In this context, the use of novel machine learning methods in scientometrics applications is the focus of attention of this dissertation. This dissertation proposes new machine learning contributions which would shed light on the scientometrics area. These contributions are divided in three parts: Several supervised cost-(in)sensitive models are learned to predict the scientific success of articles and researchers. Cost-sensitive models are not interested in maximizing classification accuracy, but in minimizing the expected total cost of the error derived from mistakes in the classification process. In this context, publishers of scientific journals could have a tool capable of predicting the citation count of an article in the future before it is published, whereas promotion committees could predict the annual increase of the h-index of researchers within the first few years. These predictive models would pave the way for new assessment systems. Several probabilistic graphical models are learned to exploit and discover new relationships among the vast number of existing bibliometric indices. In this context, scientific community could measure how some indices influence others in probabilistic terms and perform evidence propagation and abduction inference for answering bibliometric questions. Also, scientific community could uncover which bibliometric indices have a higher predictive power. This is a multi-output regression problem where the role of each variable, predictive or response, is unknown beforehand. The resulting indices could be very useful for prediction purposes, that is, when their index values are known, knowledge of any index value provides no information on the prediction of other bibliometric indices. A scientometric study of the Spanish computer science research is performed under the publish-or-perish culture. This study is based on a cluster analysis methodology which characterizes the research activity in terms of productivity, visibility, quality, prestige and international collaboration. This study also analyzes the effects of collaboration on productivity and visibility under different circumstances.