Survey on Relationship between Goal Orientation and Learning Strategies with Academic Stress in University Students

AbstractThis study examine the relationship between goal orientation and learning strategies with academic stress in university students(150 male and 150 female) which were selected among 4 college by cluster sampling method. Goal orientation questionnaire (Button, Mathieu and Zajac, 1996) and learning strategies questionnaire (Pintrich and Degroot, 1990) and academic stress questionnaire (Gadzella, 1991) were used as the research instrument. Finding show that there was a positive meaningful relationship between learning goal orientation with learning strategies (cognitive and met cognitive) and academic stress. Also there was negative meaningful correlation between performance goal orientation and learning strategies (cognitive and Meta cognitive). These finding also reveals that learning strategies (cognitive and Meta cognitive) have negative meaningful correlation with academic stress. Finally findings displays that academic stress is predictable through linear regression of goal orientation component and learning strategies. So totally these variables were explained 9/6 percent of the variance of academic stress

Determinants of high electrical energy demand in UK homes: Appliance ownership and use

This paper provides an analysis of the appliance ownership and use factors contributing to high electrical energy demand in UK homes. The data were collected during a large-scale, city-wide survey, carried out in Leicester, UK, in 2009–2010. Annual electricity consumption and appliance ownership and use were established for 183 dwellings and an odds ratio analysis used to identify the factors that led to high electricity consumption. Many of the appliance ownership and use factors have not previously been studied for the UK domestic sector. The results of this study should be of key interest to government policy makers and energy supply companies interested in the underlying drivers of the highly positively skewed distribution of UK domestic electricity use. The study identifies those appliances that could be targeted for technical improvements or subjected to campaigns to encourage more energy efficient use in order to reduce electricity consumption among high demand households. This paper builds on earlier work by the current authors which identified the households (socio-demographic and dwelling characteristics) most likely to be high electricity consumers. The current work provides the basis for advice and guidance to those households that would enable them to, over time, reduce their electricity use

The impact of occupants’ behaviours on building energy analysis: A research review

Over the past 15 years, the evaluation of energy demand and use in buildings has become increasingly acute due to growing scientific and political pressure around the world in response to climate change. The estimation of the use of energy in buildings is therefore a critical process during the design stage. This paper presents a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation. It has been widely acknowledged in the literature that there is an alarming performance gap between the predicted and actual energy consumption of buildings (sometimes this has been up to 300% difference). Analysis of the impact of occupants’ behaviour has been largely overlooked in building energy performance analysis. In short, energy simulation tools utilise climatic data and physical/ thermal properties of building elements in their calculations, and the impact of occupants is only considered through means of fixed and scheduled patterns of behaviour. This research review identified a number of areas for future research including: larger scale analysis (e.g. urban analysis); interior design, in terms of space layout, and fixtures and fittings on occupants’ behaviour; psychological cognitive behavioural methods; and the integration of quantitative and qualitative research findings in energy simulation tools to name but a few

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Enquiries into undergraduate students\u27 understanding of combinatorial structures

Philosophers and educational researchers have pondered and studied understanding for many years. From an educational perspective, illuminating how understanding is formed and improved can provide valuable theoretical and pedagogical insights. Combinatorics is an important field of research, with vast applications in mathematics and other fields of science. It is also included in many undergraduate curricula. In this thesis, I examine students\u27 understanding of combinatorics based on two specific research questions: How do students understand a new definition? How do students improve their understanding of a concept when it is challenged? In one study, I examined students\u27 initial understanding of a new definition and the corresponding concept image. I presented students with a new definition, trization, and a set of tasks carefully chosen to reveal different aspects of their concept image of the new definition. For this purpose I considered example generation, use of formulae, use of representations, and connections the students make to their existing knowledge and concept images. The study revealed that most students did not generate examples; however, they expected examples to be presented to them. Many students could find the appropriate formula for counting the number of possible trizations, after they were exposed to a few related tasks. Finding the formula helped them make a connection with their previous knowledge. Although a few students made this connection, they did not make any other anticipated connections. Many students\u27 first attempt to understand trization was to create a pictorial representation, which, they did not consider significant in their understanding. However, algebraic representation was seen as a necessary and often sufficient form of understanding. In the other study, I developed a methodology, mediated successive refinement, helping students change an inappropriate concept image. This methodology was based on learner-generated examples, and it encouraged students not only to reflect on their own examples, but also to reflect and modify their peers\u27. This study identified the different scenarios that can occur when a student\u27s concept image is changing. It established mediated successive refinement as a methodological tool for providing valuable research data, and a pedagogical tool for helping students improve their understanding

Trip Wire Detection for Land Mines

this report, we will concentrate primarily on the Radon transform method, since it was the most successful of the three. When tested on several images, some supplied by ITRES and other generated artificially during the Workshop, it proved fairly effective in detecting hidden wires. The algorithm was implemented in Matlab and proved rather slow. However, we believe that our implementation is far from optimal (indeed, there is a difference between what Matlab claims it can do from the manual and what it does in practice!), and a dedicated implementation will certainly run much faster. We also believe that there is potential for a much faster implementation of the Radon transform that makes use of the fact that the image is updated one line at a time. 1.3 Identifying Linear Features using the Radon Transform