An attempt through the use of the Group Rorschach test to identify personality characteristics associated with achievement in a school of nursing

Thesis (M.S.)--Boston Universit

The Effectiveness of Reciprocal Scaffolding Treatment in Anomic Aphasia

Reciprocal Scaffolding Treatment (RST) uses an apprenticeship model of learning that occurs between novices and a skilled partner. This project examined the effect of RST on improvement of word retrieval and conversational content for an individual with anomic aphasia. Novices were graduate student clinicians and the skilled partner was an individual with aphasia, who demonstrated facilitative communication techniques during conversational group treatment conducted by the novices. The individual with aphasia made positive changes in word fluency, correct information units and type-token ratio. Novice clinicians acquired training in facilitating conversational skills from a knowledgeable individual with aphasia

Developing world MOOCs: A curriculum view of the MOOC landscape

MOOCs offer opportunities but are also pose the danger of further exacerbating existing educational divisions and deepening the homogeneity of global knowledge systems. Like many universities globally, South African university leaders and those responsible for course, curriculum, and learning technology development are coming to grips with the implications and possibilities of online and open education for their own institutions. What opportunities do they offer to universities, especially from the point of view of research-focused campus-based institutions which have not yet engaged with MOOCs and have little history with online courses? Given the complexities of the MOOC-scape, this paper provides a means for contextualising the options within an institutional landscape of educational provision as possibilities for MOOC creation, use and adaptation. This takes into account what is currently available and identifies what new opportunities can be explored. Refining this further, a categorisation of existing MOOCs is provided that maps to broad institutional interests. The notion of courses offered by universities as being either primarily ‘inward’ or ‘outward’ facing is explained. Five categories of MOOCs are described: Category One, Teaching Showcase; Category Two, Gateway Skills; Category Three, Graduate Skills; Category Four, Professional Skills and Category Five, Research Showcase. These are elaborated on and examples provided. This taxonomy provides a nuanced way of understanding MOOCs and MOOC type courses in order for educators to strategically prioritise and decision makers to support the full gamut of emergent opportunities

Learning through engagement: MOOCs as an emergent form of provision

Massive open online courses (MOOCs) are a new form of educational provision occupying a space between formal online courses and informal learning. Adopting measures used with formal online courses to assess the outcomes of MOOCs is often not informative because the context is very different. The particular affordances of MOOCs shaping learning environments comprise scale (in terms of numbers of students) and diversity (in terms of the types of students). As learning designers, we focus on understanding the particular tools and pedagogical affordances of the MOOC platform to support learner engagement. Drawing on research into learner engagement conducted in the broader field of online learning, we consider how learner engagement in a MOOC might be designed for by looking at three pedagogical aspects: teacher presence, social learning, and peer learning

Designing a framework for making use of MOOCs

The presentation covers the University of Cape Town (UCT) Massive Open Online Course (MOOCs) project, including designing a framework for use, licensing, new course modes, MOOC materials re-use, and learning from other creators and teachers’ experiences. It also provides examples of existing MOOCs at UCT. MOOCs are a prominent form of free or low-cost course offerings, not necessarily within the traditional vision for open education

Learning through engagement : MOOCs as an emergent form of provision

Drawing on research in the broader field of online learning, this paper considers how learner engagement in Massive Open Online Courses (MOOCs) might be designed for by looking at three pedagogical aspects: teacher presence, social learning and peer learning. A small core group of learners actively and deeply participated in all these sharing practices, and their behaviour enabled ‘vicarious’ learning, enhancing the learning experience for the entire community. However, the vast majority of signups have low participation, and some characteristics of these people were identified. This may be a feature of a cohort based learning design model at scale

Approaches from the literature: Activity Theory, new tools and changing educators' practices

For a study being undertaken to investigate if and how educator practices change through MOOC development and engagement with open education, Activity Theory provides a heuristic to observe contradictions and changing educator practices after the addition of new tools to a learning environment. Ours is a longitudinal study with cross case analysis of lead educators in 3-4 MOOCs, examining themes and contradictions emerging from the semi-structured data analysis to observe change in practices. In this poster we explore the question: how and why has activity theory been used to examine the introduction of new tools/mediating artefacts into the learning environment. A fuller version of our literature review is available at http://bit.ly/1jwyit3; our study’s design amalgamates the three approaches below. Our study is conducted by Laura Czerniewicz and the MOOC team at the Centre for Innovation in Learning and Teachin

Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters

Randomized Trial of Four Financial-Incentive Programs for Smoking Cessation

Background Financial incentives promote many health behaviors, but effective ways to deliver health incentives remain uncertain. Methods We randomly assigned CVS Caremark employees and their relatives and friends to one of four incentive programs or to usual care for smoking cessation. Two of the incentive programs targeted individuals, and two targeted groups of six participants. One of the individual-oriented programs and one of the group-oriented programs entailed rewards of approximately $800 for smoking cessation; the others entailed refundable deposits of$150 plus $650 in reward payments for successful participants. Usual care included informational resources and free smoking-cessation aids. Results Overall, 2538 participants were enrolled. Of those assigned to reward-based programs, 90.0% accepted this assignment, as compared with 13.7% of those assigned to deposit-based programs (P Conclusions Reward-based programs were much more commonly accepted than deposit-based programs, leading to higher rates of sustained abstinence from smoking. Group-oriented incentive programs were no more effective than individual-oriented programs