Digital Systems for Open Access to Formal and Informal Learning

Today, Digital Systems and Services for Technology Supported Learning and Education are recognized as the key drivers to transform the way that individuals, groups and organizations “learn” and the way to “assess learning” in 21st Century. These transformations influence: Objectives - moving from acquiring new “knowledge” to developing new and relevant “competences”; Methods – moving from “classroom” based teaching to “context-aware” personalized learning; and Assessment – moving from “life-long” degrees and certifications to “on-demand” and “in-context” accreditation of qualifications. Within this context, promoting Open Access to Formal and Informal Learning, is currently a key issue in the public discourse and the global dialogue on Education, including Massive Open Online Courses (MOOCs) and Flipped School Classrooms. This volume on Digital Systems for Open Access to Formal and Informal Learning contributes to the international dialogue between researchers, technologists, practitioners and policy makers in Technology Supported Education and Learning. It addresses emerging issues related with both theory and practice, as well as, methods and technologies that can support Open Access to Formal and Informal Learning. In the twenty chapters contributed by international experts who are actively shaping the future of Educational Technology around the world, topics such as: - The evolution of University Open Courses in Transforming Learning - Supporting Open Access to Teaching and Learning of People with Disabilities - Assessing Student Learning in Online Courses - Digital Game-based Learning for School Education - Open Access to Virtual and Remote Labs for STEM Education - Teachers’ and Schools’ ICT Competence Profiling - Web-Based Education and Innovative Leadership in a K-12 International School Setting are presented. An in-depth blueprint of the promise, potential, and imminent future of the field, Digital Systems for Open Access to Formal and Informal Learning is necessary reading for researchers and practitioners, as well as, undergraduate and postgraduate students, in educational technology.

E-Learning Systems, Environments and Approaches

The volume consists of twenty-five chapters selected from among peer-reviewed papers presented at the CELDA (Cognition and Exploratory Learning in the Digital Age) 2013 Conference held in Fort Worth, Texas, USA, in October 2013 and also from world class scholars in e-learning systems, environments and approaches. The following sub-topics are included: Exploratory Learning Technologies (Part I), e-Learning social web design (Part II), Learner communities through e-Learning implementations (Part III), Collaborative and student-centered e-Learning design (Part IV). E-Learning has been, since its initial stages, a synonym for flexibility. While this dynamic nature has mainly been associated with time and space it is safe to argue that currently it embraces other aspects such as the learners’ profile, the scope of subjects that can be taught electronically and the technology it employs. New technologies also widen the range of activities and skills developed in e-Learning. Electronic learning environments have evolved past the exclusive delivery of knowledge. Technology has endowed e-Learning with the possibility of remotely fomenting problem solving skills, critical thinking and team work, by investing in information exchange, collaboration, personalisation and community building.

Rahmenwerk für zielgruppenorientiertes Blended E-Learning im MINT-Bereich im Kontext des Lebenslangen Lernens

Das heutige Leben der Menschen ist vom Internet durchdrungen, kaum etwas ist nicht „vernetzt“ oder „elektronisch verfügbar“. Die Welt befindet sich im Wandel, die „Informationsgesellschaft“ konsumiert in Echtzeit Informationen auf mobilen Endgeräten, unabhängig von Zeit und Ort. Dies gilt teilweise auch für den Aus- und Weiterbildungssektor: Unter „E-Learning“ versteht man die elektronische Unterstützung des Lernens. Gelernt wird „online“; Inhalte sind digital verfügbar. Zudem hat sich die Lebenssituation der sogenannten „Digital Natives“, der jungen Individuen in der Informationsgesellschaft, verändert. Sie fordern zeitlich und räumlich flexible Ausbildungssysteme, erwarten von Bildungsinstitutionen umfassende digitale Verfügbarkeit von Informationen und möchten ihr Leben nicht mehr Lehr- und Zeitplänen unterordnen – das Lernen soll zum eigenen Leben passen, lebensbegleitend stattfinden. Neue „Lernszenarien“, z.B. für alleinerziehende Teilzeitstudierende oder Berufstätige, sollen problemlos möglich werden. Dies soll ein von der europäischen Union erarbeitetes Paradigma leisten, das unter dem Terminus „Lebenslanges Lernen“ zusammengefasst ist. Sowohl E-Learning, als auch Lebenslanges Lernen gewinnen an Bedeutung, denn die (deutsche) Wirtschaft thematisiert den „Fachkräftemangel“. Die Nachfrage nach speziell ausgebildeten Ingenieuren im MINT-Bereich soll schnellstmöglich befriedigt, die „Mitarbeiterlücke“ geschlossen werden, um so weiterhin das Wachstum und den Wohlstand zu sichern. Spezielle E-Learning-Lösungen für den MINT-Bereich haben das Potential, eine schnelle sowie flexible Aus- und Weiterbildung für Ingenieure zu bieten, in der Fachwissen bezogen auf konkrete Anforderungen der Industrie vermittelt wird. Momentan gibt es solche Systeme allerdings noch nicht. Wie sehen die Anforderungen im MINT-Bereich an eine solche E-Learning-Anwendung aus? Sie muss neben neuen Technologien vor allem den funktionalen Anforderungen des MINTBereichs, den verschiedenen Zielgruppen (wie z.B. Bildungsinstitutionen, Lerner oder „Digital Natives“, Industrie) und dem Paradigma des Lebenslangen Lernens gerecht werden, d.h. technische und konzeptuelle Anforderungen zusammenführen. Vor diesem Hintergrund legt die vorliegende Arbeit ein Rahmenwerk für die Erstellung einer solchen Lösung vor. Die praktischen Ergebnisse beruhen auf dem Blended E-Learning-System des Projekts „Technische Informatik Online“ (VHN-TIO).

Governing individual learning in the transition phase of software maintenance offshoring: a dynamic perspective

Prior studies suggest that clients need to actively govern knowledge transfer to vendor staff in offshore outsourcing. In this paper, we analyze longitudinal data from four software maintenance offshore out-sourcing projects to explore why governance may be needed for knowledge transfer and how governance and the individual learning of vendor engineers inter-act over time. Our results suggest that self-control is central to learning, but may be hampered by low levels of trust and expertise at the outset of projects. For these foundations to develop, clients initially need to exert high amounts of formal and clan controls to enforce learning activities against barriers to knowledge sharing. Once learning activities occur, trust and expertise increase and control portfolios may show greater emphases on self-control.

Learning software-maintenance tasks in offshoring projects: a cognitive-load perspective

Individual learning is central to the success of the transition phase in software mainte-nance offshoring projects. However, little is known on how learning activities, such as on-the-job training and formal presentations, are effectively combined during the tran-sition phase. In this study, we present and test propositions derived from cognitive load theory. The results of a multiple-case study suggest that learning effectiveness was highest when learning tasks such as authentic maintenance requests were used. Con-sistent with cognitive load theory, learning tasks were most effective when they imposed moderate cognitive load. Our data indicate that cognitive load was influenced by the expertise of the onsite coordinator, by intrinsic task complexity, by the degree of specifi-cation of tasks, and by supportive information. Cultural and semantic distances may in-fluence learning by inhibiting supportive information, specification, and the assignment of learning tasks.

Learning Software-Maintenance Tasks in the Transition Phase of Offshore Outsourcing Projects: Two Learning-Theoretical Perspectives

The increasing practice of offshore outsourcing software maintenance has posed the challenge of effectively transferring knowledge to individual software engineers of the vendor. In this theoretical paper, we discuss the implications of two learning theories, the model of work-based learning (MWBL) and cognitive load theory (CLT), for knowledge transfer during the transition phase. Taken together, the theories suggest that learning mechanisms need to be aligned with the type of knowledge (tacit versus explicit), task characteristics (complexity and recurrence), and the recipients’ expertise. The MWBL proposes that learning mechanisms need to include conceptual and practical activities based on the relative importance of explicit and tacit knowledge. CLT explains how effective portfolios of learning mechanisms change over time. While jobshadowing, completion tasks, and supportive information may prevail at the outset of transition, they may be replaced by the work on conventional tasks towards the end of transition.

Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Advances in Teaching and Learning 2005 Webcast - Introductions and Expert Panel

Expert Panel: Documenting Teaching Scholarship for Promotion and Tenure Lemuel Moye, School of Public Health Miguel daCunha, School of Nursing William Tate, Dental School Katherine Loveland, Medical School

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Patient satisfaction and nursing staff work satisfaction in an urban public teaching hospital

This study examined the level of patient satisfaction and nursing staff work satisfaction at an urban public hospital in the Southwestern United States. The primary objectives of this study were to determine: (1) the level of overall patient satisfaction and satisfaction with specific dimensions of hospital care; (2) the differences in patient satisfaction according to demographic characteristics (age, gender, ethnicity, and education completed) and predispositional factors (perceived health status, perceived level of pain, prior contact with the hospital, and hospital image) and the relative importance of each variable on patient satisfaction; (3) the level of overall work satisfaction and satisfaction with specific dimensions of work experienced by the medical/surgical nursing staff; (4) the differences in work satisfaction experienced by the nursing staff based on demographic variables (age, gender, ethnicity, and marital status) and professional factors (education completed, staff position, the number of years employed with the hospital, and number of years employed in nursing) and the relative importance of each variable on work satisfaction; and (5) to determine the effect of the nursing work milieu on patient and staff satisfaction.^ The study findings showed that patients experienced a moderate to low level of satisfaction with the dimensions of hospital care (admission process, daily care, information, nursing care, physician care, other hospital staff, living arrangements, and overall care). Of the eight dimensions of care, patients reported a relatively positive level of satisfaction (75 percent or better) with only one dimension: physician care. Ethnicity, perceived health status, and hospital image were significantly related to patient satisfaction. Hispanic patients, those who were in good health, and those who felt the hospital had a good image in their community were most satisfied with hospital care. Patients also reported areas of hospital care that needed the most improvement. Responses included: rude staff, better nursing care, and better communication.^ Findings from the nursing satisfaction survey indicated a low level of satisfaction with the dimensions of work (autonomy, pay, professional status, interaction, task requirements, and organizational policies). Only one dimension of work, professional status, received a mean satisfaction score in the positive range. Additionally, staff members were unanimously dissatisfied with their salaries. Frequently mentioned work-related problems reported by the staff included: staffing shortages, heavy patient loads. and excessive paperwork.^ The nursing milieu appeared to have had a significant effect on the satisfaction levels of patients nursing staff employees. The nursing staff were often short staffed, which increased the patient-to-nurse ratio. Consequently, patients did not receive the amount of attention and care they expected from the nursing staff. Crowded patient rooms allowed for little personal space and privacy. Dissatisfaction with living conditions served to influence patients' attitudes and satisfaction levels. These frustrations were often directed toward their primary caregivers, the nursing staff. Consequently, the nursing milieu appeared to directly affect and influence the satisfaction levels of both patients and staff. (Abstract shortened by UMI). ^

DAT1 polymorphism determines L-DOPA effects on learning about others' prosociality.

Despite that a wealth of evidence links striatal dopamine to individualś reward learning performance in non-social environments, the neurochemical underpinnings of such learning during social interaction are unknown. Here, we show that the administration of 300 mg of the dopamine precursor L-DOPA to 200 healthy male subjects influences learning about a partners' prosocial preferences in a novel social interaction task, which is akin to a repeated trust game. We found learning to be modulated by a well-established genetic marker of striatal dopamine levels, the 40-bp variable number tandem repeats polymorphism of the dopamine transporter (DAT1 polymorphism). In particular, we found that L-DOPA improves learning in 10/10R genoype subjects, who are assumed to have lower endogenous striatal dopamine levels and impairs learning in 9/10R genotype subjects, who are assumed to have higher endogenous dopamine levels. These findings provide first evidence for a critical role of dopamine in learning whether an interaction partner has a prosocial or a selfish personality. The applied pharmacogenetic approach may open doors to new ways of studying psychiatric disorders such as psychosis, which is characterized by distorted perceptions of others' prosocial attitudes.

The Chemical Space of Flavours

The flavour of foods is determined by the interaction of taste molecules with receptors in the mouth, and fragrances or aroma with receptors in the upper part of the nose. Here, we discuss the properties of taste and fragrance molecules, from the public databases Superscent, Flavornet, SuperSweet and BitterDB, taken collectively as flavours, in the perspective of the chemical space. We survey simple descriptor profiles in comparison with the public collections ChEMBL (bioactive small molecules), ZINC (commercial drug-like molecules) and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). A global analysis of the chemical space of flavours is also presented based on molecular quantum numbers (MQN) and SMILES fingerprints (SMIfp). While taste molecules span a very broad property range, fragrances occupy a narrow area of the chemical space consisting of generally very small and relatively nonpolar molecules distinct of standard drug molecules. Proximity searching in the chemical space is exemplified as a simple method to facilitate the search for new fragrances.