Let's do it: A framework to investigate the affordances of experimental learning environments

BACKGROUND Experimental learning, traditionally conducted in on-campus laboratory venues, is the cornerstone of science and engineering education. In order to ensure that engineering graduates are exposed to ‘real-world’ situations and attain the necessary professional skill-sets, as mandated by course accreditation bodies such as Engineers Australia, face-to-face laboratory experimentation with real equipment has been an integral component of traditional engineering education. The online delivery of engineering coursework endeavours to mimic this with remote and simulated laboratory experimentation. To satisfy student and accreditation requirements, the common practice has been to offer equivalent remote and/or simulated laboratory experiments in lieu of the ones delivered, face-to face, on campus. The current implementations of both remote and simulated laboratories tend to be specified with a focus on technical characteristics, instead of pedagogical requirements. This work attempts to redress this situation by developing a framework for the investigation of the suitability of different experimental educational environments to deliver quality teaching and learning. PURPOSE For the tertiary education sector involved with technical or scientific training, a research framework capable of assessing the affordances of laboratory venues is an important aid during the planning, designing and evaluating stages of face-to-face and online (or cyber) environments that facilitate student experimentation. Providing quality experimental learning venues has been identified as one of the distance-education providers’ greatest challenges. DESIGN/METHOD The investigation draws on the expertise of staff at three Australian universities: Swinburne University of Technology (SUT), Curtin University (Curtin) and Queensland University of Technology (QUT). The aim was to analyse video recorded data, in order to identify the occurrences of kikan-shido (a Japanese term meaning ‘between desks instruction’ and over-the-shoulder learning and teaching (OTST/L) events, thereby ascertaining the pedagogical affordances in face-to-face laboratories. RESULTS These will be disseminated at a Master Class presentation at this conference. DISCUSSION Kikan-shido occurrences did reflect on the affordances of the venue. Unlike with other data collection methods, video recorded data and its analysis is repeatable. Participant bias is minimised or even eradicated and researcher bias tempered by enabling re-coding by others. CONCLUSIONS Framework facilitates the identification of experiential face-to-face learning venue affordances. Investigation will continue with on-line venues.

Reflecting on Reggio: An evaluation of design intent in an early childhood learning environment

Reggio Emilia is an educational philosophy that encourages teachers, students and their parents to collaborate and actively engage with the environment. This study investigates how the Reggio Emilia design approach was translated architecturally for a kindergarten in an Australian context, and provides insights into the operation of this Reggio kindergarten and the impact that it is now having on the occupants. It evaluates the original architectural design intent of the Reggio Emilia early childhood learning environment against its spatial provision. The relationship that the Reggio Emilia approach facilitates between students and the environment, and the contribution that this approach has on their learning, are also explored. Several key themes emerging from the Reggio values were identified in the literature. These were then used to inform an exploration of the kindergarten spaces and places.. Architects, teachers and a sustainability manager of the kindergarten were interviewed with their experiences constituting the primary data of this study. Using a Grounded Theory methodology, systematic data coding and analysis were then conducted. Themes and concepts that emerged from this process include: differing interpretations of the Reggio Emilia philosophy; motivations for neglect of traditional external structures and play equipment; the impact of education for sustainability; and the positive effects that Reggio Emilia is having on the rest of the institution’s development.

Student perception of a new integrated anatomy practical program: Does students' prior learning make a difference?

While there is evidence that science and non-science background students display small differences in performance in basic and clinical sciences, early in a 4-year, graduate entry medical program, this lessens with time. With respect to anatomy knowledge, there are no comparable data as to the impact previous anatomy experience has on the student perception of the anatomy practical learning environment. A study survey was designed to evaluate student perception of the anatomy practical program and its impact on student learning, for the initial cohort of a new medical school. The survey comprised 19 statements requiring a response using a 5-point Likert scale, in addition to a free text opportunity to provide opinion of the perceived educational value of the anatomy practical program. The response rate for a total cohort of 82 students was 89%. The anatomy practical program was highly valued by the students in aiding their learning of anatomy, as indicated by the high mean scores for all statements (range: 4.04-4.7). There was a significant difference between the students who had and had not studied a science course prior to entering medicine, with respect to statements that addressed aspects of the course related to its structure, organization, variety of resources, linkage to problem-based learning cases, and fairness of assessment. Nonscience students were more positive compared to those who had studied science before (P levels ranging from 0.004 to 0.035). Students less experienced in anatomy were more challenged in prioritizing core curricular knowledge. © 2011 Wiley-Liss, Inc.

Very Low Birth Weight Infants as Young Adults : Focus on aspects of cognition, behavior and sleep

Major advances in the treatment of preterm infants have occurred during the last three decades. Survival rates have increased, and the first generations of preterm infants born at very low birth weight (VLBW; less than 1500 g) who profited from modern neonatal intensive care are now in young adulthood. The literature shows that VLBW children achieve on average lower scores on cognitive tests, even after exclusion of individuals with obvious neurosensory deficits. Evidence also exists for an increased risk in VLBW children for various neuropsychiatric disorders such as attention-deficit hyperactivity disorder (ADHD) and related behavioral symptoms. Up till now, studies extending into adulthood are sparse, and it remains to be seen whether these problems persist into adulthood. The aim of this thesis was to study ADHD-related symptoms and cognitive and executive functioning in young adults born at VLBW. In addition, we aimed to study sleep disturbances, known to adversely affect both cognition and attention. We hypothesized that preterm birth at VLBW interferes with early brain development in a way that alters the neuropsychological phenotype; this may manifest itself as ADHD symptoms and impaired cognitive abilities in young adulthood. In this cohort study from a geographically defined region, we studied 166 VLBW adults and 172 term-born controls born from 1978 through 1985. At ages 18 to 27 years, the study participants took part in a clinic study during which their physical and psychological health was assessed in detail. Three years later, 213 of these individuals participated in a follow-up. The current study is part of a larger research project (The Helsinki Study of Very Low Birth Weight Adults), and the measurements of interest for this particular study include the following: 1) The Adult Problem Questionnaire (APQ), a self-rating scale of ADHD-related symptoms in adults; 2) A computerized cognitive test battery designed for population studies (CogState®) which measures core cognitive abilities such as reaction time, working memory, and visual learning; 3) Sleep assessment by actigraphy, the Basic Nordic Sleep Questionnaire, and the Morningness-Eveningness Questionnaire. Actigraphs are wrist-worn accelerometers that separate sleep from wakefulness by registering body movements. Contrary to expectations, VLBW adults as a group reported no more ADHD-related behavioral symptoms than did controls. Further subdivision of the VLBW group into SGA (small for gestational age) and AGA (appropriate for gestational age) subgroups, however, revealed more symptoms on ADHD subscales pertaining to executive dysfunction and emotional instability among those born SGA. Thus, it seems that intrauterine growth retardation (for which SGA served as a proxy) is a more essential predictor for self-perceived ADHD symptoms in adulthood than is VLBW birth as such. In line with observations from other cohorts, the VLBW adults reported less risk-taking behavior in terms of substance use (alcohol, smoking, and recreational drugs), a finding reassuring for the VLBW individuals and their families. On the cognitive test, VLBW adults free from neurosensory deficits had longer reaction times than did term-born peers on all tasks included in the test battery, and lower accuracy on the learning task, with no discernible effect of SGA status over and above the effect of VLBW. Altogether, on a group level, even high-functioning VLBW adults show subtle deficits in psychomotor processing speed, visual working memory, and learning abilities. The sleep studies provided no evidence for differences in sleep quality or duration between the two groups. The VLBW adults were, however, at more than two-fold higher risk for sleep-disordered breathing (in terms of chronic snoring). Given the link between sleep-disordered breathing and health sequelae, these results suggest that VLBW individuals may benefit from an increased awareness among clinicians of this potential problem area. An unexpected finding from the sleep studies was the suggestion of an advanced sleep phase: The VLBW adults went to bed earlier according to the actigraphy registrations and also reported earlier wake-up times on the questionnaire. In further study of this issue in conjunction with the follow-up three years later, the VLBW group reported higher levels of morningness propensity, further corroborating the preliminary findings of an advanced sleep phase. Although the clinical implications are not entirely clear, the issue may be worth further study, since circadian rhythms are closely related to health and well-being. In sum, we believe that increased understanding of long-term outcomes after VLBW, and identification of areas and subgroups that are particularly vulnerable, will allow earlier recognition of potential problems and ultimately lead to improved prevention strategies.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Utilización de la plataforma virtual eKasi en la docencia de la tecnología farmacéutica

[ES] Una de las aplicaciones más interesantes de las nuevas tecnologías en la docencia, es la utilización de plataformas virtuales accesibles por el alumno a través de Internet. eKASI es una plataforma informática para el apoyo a la docencia presencial desarrollada en la Universidad del Pais Vasco, que permite la gestión de los documentos y la gestión de los estudiantes de un curso, a la vez que facilita el aprendizaje del alumno. Es una herramienta de distribución gratuita y de fácil manejo. Durante el curso 2005/2006, se ha utilizado esta plataforma como apoyo a la docencia de la asignatura Tecnología Farmacéutica I de 4º curso de la Licenciatura en Farmacia de la Universidad del País Vasco. La plataforma está accesible en la dirección de Internet http://ekasi.ehu.es mediante la introducción de una clave facilitada por el administrador del sistema. En la sección correspondiente al aula virtual de la plataforma, el alumno tiene a su disposición la información y documentos relacionados con la asignatura (plan docente, presentaciones utilizadas en las clases, cuestionarios de autoevaluación, enlaces de interés a paginas web, materiales multimedia, etc.). Por otra parte, esta plataforma permite la colaboración y discusión on line de los materiales estudiados, a través del foro y del correo electrónico y posibilita al profesor tutorizar y realizar un seguimiento del progreso de los estudiantes, mediante la realización de tests y de las diferentes tareas propuestas al grupo de alumnos. La plataforma eKASI ha supuesto un instrumento de gran utilidad como apoyo a la docencia presencial tal como se deduce de los resultados de la encuesta realizada a los alumnos al finalizar el curso académico.

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.

Effects of the Santa Barbara, Calif., Oil Spill on the Apparent Abundance of Pelagic Fishery Resources

Many studies have been made of the effects of oil on marine invertebrates, plants (marine algae and phytoplankton), and vertebrates such as seabirds and marine mammals. An excellent review of these findings, which includes some references to fish and pathological effects of aromatic hydrocarbons, has been published by the Royal Society, London (Clark, 1982). That review dealt with the environmental effects of such major oil spills or releases such as those by the tankers Torry Canyon (119,000 t) on the south coast of England, Metula (50-56,000 t) in the Straits of Magellan, Argo Merchant (26,000 t) off Cape Cod, and the super tanker Amoco Cadiz (223,000 t) on the coast of northern Brittany. Those spills were studied to determine their effect on living resources. In contrast there are few references on the impact of oil spills on pelagic fishery resources.

Climate impacts on U. S. living marine resources: National Marine Fisheries Services concerns, activities and needs

With the increasing recognition that climate change is occurring and having large impacts on living marine resources, a sound ecosystem approach to management of those resources requires both understanding how climate affects ecosystems and integration of that understanding into management processes. The National Marine Fisheries Service (NMFS) must identify how changing climatic conditions will impact its mission and must be prepared to adapt to these changes. This document identifies the climate related ecosystem concerns in the regional marine ecosystems for which NMFS has living marine resource management responsibilities, what NMFS is currently doing to address these concerns, what NMFS must do going forward to address these concerns, and what climate information is needed to integrate climate into resource management. The regional ecosystems included in this analysis are: the Northeast U.S. Continental Shelf; the Southeast U.S. Continental Shelf, Gulf of Mexico, and U.S. Caribbean; the California Current Ecosystem; the Alaskan Ecosystem Complex; the Pacific Island Ecosystem Complex; the Eastern Tropical Pacific; North Pacific Highly Migratory Species; and the Antarctic.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Evaluating SWICE-R (South West Information for Clinical Effectiveness - Rural). Final report for South West Workforce Development Confederations (Knowledge Resources Development Unit)

Yeoman, A., Durbin, J. & Urquhart, C. (2004). Evaluating SWICE-R (South West Information for Clinical Effectiveness - Rural). Final report for South West Workforce Development Confederations, (Knowledge Resources Development Unit). Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: South West WDCs (NHS)

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.

A Neural Network Architecture for Autonomous Learning, Recognition, and Prediction in a Nonstationary World

In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.