Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Interactive Curriculum Based on Models of Mind & Brain

What does it mean for curriculum to be interactive? It encourages student engagement and active participation in both individual and group work. It offers teachers a coherent set of materials to choose from that can enhance their classes. It is the product of on-going development and continuous improvement based on research and feedback from the field. This paper will introduce work in progress from the Center for Excellence in Education, Science, and Technology (CELEST), an NSF Science of Learning Center. Among its many goals, CELEST is developing a unique educational curriculum, an interactive curriculum based upon models of mind and brain. Teachers, administrators, and governments are naturally concerned with how students learn. Students are greatly concerned about how minds work, including how to learn. CELEST aims to introduce curricula that not only meet current U.S. standards in mathematics, science, and psychology but also influence plans to improve those standards. Software and support materials are in development and available at http://cns.bu.edu/celest/private/. Interested parties are invited to contact the author for access.

The E-learning Circle – a holistic software design tool for e-learning

The article introduces the E-learning Circle, a tool developed to assure the quality of the software design process of e-learning systems, considering pedagogical principles as well as technology. The E-learning Circle consists of a number of concentric circles which are divided into three sectors. The content of the inner circles is based on pedagogical principles, while the outer circle specifies how the pedagogical principles may be implemented with technology. The circle’s centre is dedicated to the subject taught, ensuring focus on the specific subject’s properties. The three sectors represent the student, the teacher and the learning objectives. The strengths of the E-learning Circle are the compact presentation combined with the overview it provides, as well as the usefulness of a design tool dealing with complexity, providing a common language and embedding best practice. The E-learning Circle is not a prescriptive method, but is useful in several design models and processes. The article presents two projects where the E-learning Circle was used as a design tool.

Mobile Learning – Begriff, Modelle, Forschung

Der vorliegende Übersichtsartikel betrachtet Mobile Learning aus einer pädagogisch-psychologischen und didaktischen Perspektive. Mobile Learning (M-Learning), das seit Mitte der 1990er in unterschiedlichsten Kontexten Einzug in den Bildungssektor hielt, ist ein dynamisches und interdisziplinäres Feld. Dynamisch, weil M-Learning durch die rasche Entwicklung im Bereich der Informations- und Kommunikationstechnologie, wie kaum ein anderes Forschungsfeld, einem derart großen Wandel unterworfen ist. Interdisziplinär, weil durch das Zusammentreffen von mobiler Technik und Lernen auch unterschiedliche Fachdisziplinen betroffen sind. Die verschiedenen Sichtweisen und auch die Komplexität des Feldes haben dazu geführt, dass bis heute keine einheitliche Definition des Begriffs besteht. Ziel dieses Übersichtsartikels ist es, den aktuellen Forschungsstand aus didaktischer und pädagogisch-psychologischer Sicht aufzuzeigen. Dazu werden zunächst wichtige Komponenten des M-Learning-Begriffs herausgearbeitet und daran anschließend didaktisch bedeutsame theoretische Ansätze und Modelle vorgestellt sowie kritisch betrachtet. Basierend auf dieser theoretischen Ausgangslage wird dann ein Rahmen gezeichnet, der verdeutlichen soll, wo empirische Forschung aus didaktischer und pädagogisch-psychologischer Sicht ansetzen kann. Entsprechende empirische Studien werden ebenfalls vorgestellt, um einen Eindruck des aktuellen empirischen Forschungsstandes zu geben. Dies alles soll als Ausgangspunkt für den zukünftigen Forschungsbedarf dienen.

Learning Curves of Temporary Workers in Manual Order Picking Activities

Person-to-stock order picking is highly flexible and requires minimal investment costs in comparison to automated picking solutions. For these reasons, tradi-tional picking is widespread in distribution and production logistics. Due to its typically large proportion of manual activities, picking causes the highest operative personnel costs of all intralogistics process. The required personnel capacity in picking varies short- and mid-term due to capacity requirement fluctuations. These dynamics are often balanced by employing minimal permanent staff and using seasonal help when needed. The resulting high personnel fluctuation necessitates the frequent training of new pickers, which, in combination with in-creasingly complex work contents, highlights the im-portance of learning processes in picking. In industrial settings, learning is often quantified based on diminishing processing time and cost requirements with increasing experience. The best-known industrial learning curve models include those from Wright, de Jong, Baloff and Crossman, which are typically applied to the learning effects of an entire work crew rather than of individuals. These models have been validated in largely static work environments with homogeneous work contents. Little is known of learning effects in picking systems. Here, work contents are heterogeneous and individual work strategies vary among employees. A mix of temporary and steady employees with varying degrees of experience necessitates the observation of individual learning curves. In this paper, the individual picking performance development of temporary employees is analyzed and compared to that of steady employees in the same working environment.

Moodle & Co. auf dem Weg zur Personal Learning Environment

Ausgehend von der typischen IT‐Infrastruktur für E‐Learning an Hochschulen auf der einen Seite sowie vom bisherigen Stand der Forschung zu Personal Learning Environments (PLEs) auf der anderen Seite zeigt dieser Beitrag auf, wie bestehende Werkzeuge bzw. Dienste zusammengeführt und für die Anforderungen der modernen, rechnergestützten Präsenzlehre aufbereitet werden können. Für diesen interdisziplinären Entwicklungsprozess bieten sowohl klassische Softwareentwicklungsverfahren als auch bestehende PLE‐Modelle wenig Hilfestellung an. Der Beitrag beschreibt die in einem campusweiten Projekt an der Universität Potsdam verfolgten Ansätze und die damit erzielten Ergebnisse. Dafür werden zunächst typische Lehr‐/Lern‐bzw. Kommunikations‐Szenarien identifiziert, aus denen Anforderungen an eine unterstützende Plattform abgeleitet werden. Dies führt zu einer umfassenden Sammlung zu berücksichtigender Dienste und deren Funktionen, die gemäß den Spezifika ihrer Nutzung in ein Gesamtsystem zu integrieren sind. Auf dieser Basis werden grundsätzliche Integrationsansätze und technische Details dieses Mash‐Ups in einer Gesamtschau aller relevanten Dienste betrachtet und in eine integrierende Systemarchitektur überführt. Deren konkrete Realisierung mit Hilfe der Portal‐Technologie Liferay wird dargestellt, wobei die eingangs definierten Szenarien aufgegriffen und exemplarisch vorgestellt werden. Ergänzende Anpassungen im Sinne einer personalisierbaren bzw. adaptiven Lern‐(und Arbeits‐)Umgebung werden ebenfalls unterstützt und kurz aufgezeigt.

Spike timing dependent plasticity: a consequence of more fundamental learning rules.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the "first law" of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory.

Decades of research on the cellular mechanisms of memory have led to the widely held view that memories are stored as modifications of synaptic strength. These changes involve presynaptic processes, such as direct modulation of the release machinery, or postsynaptic processes, such as modulation of receptor properties. Parallel studies have revealed that memories might also be stored by nonsynaptic processes, such as modulation of voltage-dependent membrane conductances, which are expressed as changes in neuronal excitability. Although in some cases nonsynaptic changes can function as part of the engram itself, they might also serve as mechanisms through which a neural circuit is set to a permissive state to facilitate synaptic modifications that are necessary for memory storage.

Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Models for intensive care training. A European perspective.

The diversity of European culture is reflected in its healthcare training programs. In intensive care medicine (ICM), the differences in national training programs were so marked that it was unlikely that they could produce specialists of equivalent skills. The Competency-Based Training in Intensive Care Medicine in Europe (CoBaTrICE) program was established in 2003 as a Europe-based worldwide collaboration of national training organizations to create core competencies for ICM using consensus methodologies to establish common ground. The group's professional and research ethos created a social identity that facilitated change. The program was easily adaptable to different training structures and incorporated the voice of patients and relatives. The CoBaTrICE program has now been adopted by 15 European countries, with another 12 countries planning to adopt the training program, and is currently available in nine languages, including English. ICM is now recognized as a primary specialty in Spain, Switzerland, and the UK. There are still wide variations in structures and processes of training in ICM across Europe, although there has been agreement on a set of common program standards. The combination of a common "product specification" for an intensivist, combined with persisting variation in the educational context in which competencies are delivered, provides a rich source of research inquiry. Pedagogic research in ICM could usefully focus on the interplay between educational interventions, healthcare systems and delivery, and patient outcomes, such as including whether competency-based program are associated with lower error rates, whether communication skills training is associated with greater patient and family satisfaction, how multisource feedback might best be used to improve reflective learning and teamworking, or whether increasing the proportion of specialists trained in acute care in the hospital at weekends results in better patient outcomes.

The mini-clinical evaluation exercise during medical clerkships: are learning needs and learning goals aligned?

OBJECTIVES The generation of learning goals (LGs) that are aligned with learning needs (LNs) is one of the main purposes of formative workplace-based assessment. In this study, we aimed to analyse how often trainer–student pairs identified corresponding LNs in mini-clinical evaluation exercise (mini-CEX) encounters and to what degree these LNs aligned with recorded LGs, taking into account the social environment (e.g. clinic size) in which the mini-CEX was conducted. METHODS Retrospective analyses of adapted mini-CEX forms (trainers’ and students’ assessments) completed by all Year 4 medical students during clerkships were performed. Learning needs were defined by the lowest score(s) assigned to one or more of the mini-CEX domains. Learning goals were categorised qualitatively according to their correspondence with the six mini-CEX domains (e.g. history taking, professionalism). Following descriptive analyses of LNs and LGs, multi-level logistic regression models were used to predict LGs by identified LNs and social context variables. RESULTS A total of 512 trainers and 165 students conducted 1783 mini-CEXs (98% completion rate). Concordantly, trainer–student pairs most often identified LNs in the domains of ‘clinical reasoning’ (23% of 1167 complete forms), ‘organisation/efficiency’ (20%) and ‘physical examination’ (20%). At least one ‘defined’ LG was noted on 313 student forms (18% of 1710). Of the 446 LGs noted in total, the most frequently noted were ‘physical examination’ (49%) and ‘history taking’ (21%). Corresponding LNs as well as social context factors (e.g. clinic size) were found to be predictors of these LGs. CONCLUSIONS Although trainer–student pairs often agreed in the LNs they identified, many assessments did not result in aligned LGs. The sparseness of LGs, their dependency on social context and their partial non-alignment with students’ LNs raise questions about how the full potential of the mini-CEX as not only a ‘diagnostic’ but also an ‘educational’ tool can be exploited.

Alignment between learning needs and learning goals of Mini-CEX in clerkships

Background: Defining learning goals (LG) in alignment with learning needs (LN) is one of the key purposes of formative workplace-based assessment, but studies about this topic are scarce. Summary of Work: We analysed quantitatively and qualitatively how often trainer-student pairs identified the same LN during Mini Clinical Evaluation Exercises (Mini-CEX) in clerkships and to what degree those LNs were in line with the recorded LGs. Multilevel logistic regression models were used to predict LGs by identified LNs, controlling for context variables. Summary of Results: 512 trainers and 165 students conducted 1783 Mini-CEX (98% completion rate). Concordantly, trainer-student pairs most often identified LNs in the domains ‘clinical reasoning’ (23% of 1167 complete forms), ‘organisation / efficiency’ (20%) and ‘physical examination’ (20%). At least one ‘defined’ LG was noted on 313 student forms (18% of 1710), with a total of 446 LGs. Of these, the most frequent LGs were ‘physical examination’ (49% of 446 LGs) and ‘history taking’ (21%); corresponding LNs as well as context variables (e.g. clinic size) were found to be predictors of these LGs. Discussion and Conclusions: Although trainer-student pairs often agreed in their identified LNs, many assessments did not result in an aligned LG or a LG at all. Interventions are needed to enhance the proportion of (aligned) LGs in Mini-CEX in order to tap into its full potential not only as a ‘diagnostic’ but also as an ‘educational tool’. Take-home messages: The sparseness of LGs, their dependency on context variables and their partial non-alignment with students’ LNs raise the question of how the effectiveness of Mini-CEX can be further enhanced.

Multimodal sequence learning

While sequence learning research models complex phenomena, previous studies have mostly focused on unimodal sequences. The goal of the current experiment is to put implicit sequence learning into a multimodal context: to test whether it can operate across different modalities. We used the Task Sequence Learning paradigm to test whether sequence learning varies across modalities, and whether participants are able to learn multimodal sequences. Our results show that implicit sequence learning is very similar regardless of the source modality. However, the presence of correlated task and response sequences was required for learning to take place. The experiment provides new evidence for implicit sequence learning of abstract conceptual representations. In general, the results suggest that correlated sequences are necessary for implicit sequence learning to occur. Moreover, they show that elements from different modalities can be automatically integrated into one unitary multimodal sequence.