A Four Dimensional Model of Formal and Informal Learning

Learning systems focused on collaborative learning are often described in terms of formal and informal learning, however definitions of formal and informal learning vary, which makes it difficult to compare systems that may have been described using different perspectives. In this paper we present a framework for describing formality in e-learning systems, which can account for the most common perspectives: formality focused on Learning Objective, Learning Environment, Learning Activity and/or Learning Tool. Our framework can be used to compare different e-learning systems, and can also describe collaborative systems where different students can take very different roles in the activity, and the degree of formality can vary according to the role

Mobile VLE vs. Mobile PLE: How Informal is Mobile Learning?

Mobile Learning Systems are often described as supporting informal learning; as such they are a good fit to the idea of Personal Learning Environments (PLEs), software systems that users choose and tailor to fit their own learning preferences. This paper explores the question of whether existing m-learning research is more in the spirit of PLEs or Virtual Learning Environments (VLEs). To do this we survey the mobile learning systems presented at M-Learn 2007 in order to see if they might be regarded as informal or formal learning. In order to categorise the systems we present a four dimensional framework of formality, based on Learning Objective, Learning Environment, Learning Activity and Learning Tools. We use the framework to show that mobile systems tend to be informal in terms of their environment, but ignore the other factors. Thus we can conclude that despite the claims of m-learning systems to better support informal and personal learning, today’s m-learning research is actually more in the spirit of a VLE than a PLE, and that there remains a great deal of unexplored ground in the area of Mobile PLE systems

Design of a scrutable learning system

Personal Learning Environments (PLEs) refer to systems that allow individual learners to manage and control their own learning in their own space and at their own pace. In this work we explore the different ways in which a learning experience can be informal, and propose a 4D model of informal learning to characterise the informal aspects of a learning experience.The model includes dimensions for learning objectives, the learning environment, learning activities and learning tools, and reveals how much of the experience is really under the control of the learner. In an analysis of mobile tools presented in the mLearn 2008 conference we show that many emerging m-learning systems focused on informality in the environment dimension but not in the others.To solve this problem this report proposes a scrutable learning model approach that allows personal learners to take control of their learning objectives while still allowing the system to intelligently support them with appropriate learning activities and resources. In addition an experimental design is described based around a prototype of a scrutable learning system for mobile devices

The use of a water quality model to evaluate the impacts of combined sewer overflows on the lower Hudson River

CSO discharges have long been recognized as a significant source of water pollution. While many sources of water pollution have been controlled over the past 20 years, CSOs continue to be a main environmental concern in several areas, especially in old cities. In the past, most CSO research focused on the CSO control processes, including floatables and suspended solids removal. Few studies have been conducted in the area of the impacts of CSO discharge on the receiving water quality. To achieve this purpose, a powerful water-modeling tool, WASP 6.1, is utilized in this study. The Lower Hudson River is selected as a case study. Data are collected from the US EPA, USGS, NYC DEP, and NJ DEP. After calibration, the receiving water quality model can be used to study the impacts of CSO with a series of scenarios, which include the major factors that would affect the water quality of the receiving water. DO, BOD, ammonia, fecal coliform, and mercury are the reference pollutants discussed in this study. The simulation results are able to predict the effect of various CSO abatement alternatives on water quality and to be used in the water quality management and planning processes

Towards Wide Implementation of Dance Therapy in Mental Health

In recent years, Dance Movement Therapy (DMT) has been increasingly mentioned in the field of mental health counseling. Despite the progress of DMT in terms of studies showing its efficiency for various forms of mental health conditions such as depression and trauma, the implementation of DMT amongst mental health therapists is still not as widespread as other forms of therapies such as verbal and art therapies. In this project, we aim to determine the underlying causes of its low implementation by surveying a set of populations, specifically mental health patients, mental health therapists that do not specialize in dance/movement therapy, and dance/movement therapists. In our survey, we explore several factors, such as perceived usefulness, perceived feelings, popularity and difficulty in implementation, that may be causes for its low implementation. From our results, we observe that all population groups perceive that dance movement therapy has high usefulness in terms of its effectiveness in mental health sessions, which should intuitively result in a higher implementation rate. However, the impact of the other 3 factors could describe its low implementation; First, the patient group rated a negative feeling towards trying out dance/movement therapy in part due to the nervousness involved. Second, the popularity of dance/movement therapy, as viewed by all population groups, is low. Third, the dance movement therapists themselves rated a high difficulty level in implementing this therapy method. From these results, the implementation gaps and barriers in DMT can now be better understood, allowing us to recommend steps for the widespread implementation of this relatively young yet highly useful therapeutic technique

Synthesis and characterization of LPCVD boron nitride films for x-ray lithography

Boron nitride thin films were deposited on silicon and fused quartz substrates using ammonia and the liquid precursor borane-triethylamine complex (TEAB) by low pressure chemical vapor deposition over a temperature range of 300-850°C, a pressure range of 0.21-0.6 torr, and an ammonia flow rate range of 0-740 sccm. An increased in the nitrogen content in the film due to the addition of ammonia flow resulted in a pronounced improvement in the optical transmission, an increase in the film uniformity and a decrease in the depletion effect. IR spectra of the films showed an asymmetrical wide band centered around 1400 cm-1 and a sharp band around 800 cm-1. Boron-silicon nitride films were prepared with the incorporation of silicon into the BN films using liquid precursor mixtures consisting of TEAB and hexamethyldisilane (HMDS) at a given temperature, pressure, and ammonia flow rate. The addition of silicon to the films resulted in an achievement of tensile stress and showed an improvement in the mechanical strength of the films. In the IR spectra, two strong absorption peaks centered at 1300 cm-1 and 950 cm-1 appeared. Boron-silicon nitride membranes, 1 µm in thickness, were successfully made

DEVELOPMENT OF COMPOSITIONAL MODEL FOR PREDICTING VISCOSITY OF CRUDE OILS USING POLYNOMIAL NEURAL NETWORKS (PNN) INDUCED BY GROUP METHOD OF DATA HANDLING (GMDH)

Viscosity or the intemal resistance of the fluids to flow is the most important transport property that controls and influences the flow of oil through porous media and pipes. Accurate predictions of reservoir fluids are required in equation of state (EOS) based reservoir simulators. Due to time and money spent of experimental viscosity measurements, reliable viscosity models are developed for predicting crude oils viscosity. Throughout the years, although many of the common correlations were developed, laboratory measurements still cannot be replaced due to the complexities, varied composition and reservoir characteristics difference from different reservoirs. This study estimates crude oil viscosity by using a group method of data handling (GMDH) based on polynomial neural network (PNN). GMDH is an inductive algorithm for computer-based mathematical modeling using neural network with active neurons that optimizes model coefficients for predetermine mathematical equation and selects the optimal model complexity. The new model was built and tested using experimental measurements collected through literature search. The database consists of crude oils composition, viscosity, temperature and pressure from Middle East, North Sea and the others. Overall, the proposed model improved the prediction as compared to other viscosity model. 11