Effective Pre-school, Primary and Secondary Education 3-14 Project (EPPSE 3-14): influences on students’ dispositions in Key Stage 3: exploring enjoyment of school, popularity, anxiety, citizenship values and academic self-concept in Year 9

The Effective Pre-school, Primary and Secondary Education Project (EPPSE) has investigated the academic and social-behavioural development of approximately 3,000 children from the age of 3+ years since 1997. This Report and Research Brief reports on students’ dispositions when they were age 14 (Year 9) in six main areas: ‘enjoyment of school’, ‘academic self concept’ (English and maths), ‘popularity’, ‘citizenship values’ and ‘anxiety’. It examines how these dispositions have changed during Key Stage 3 (KS3) and the relationships between dispositions and a range of individual student, family, home, pre-, primary and secondary school measures. It shows how school experiences help to shape dispositions, and also explores the relationships between dispositions to school and students’ academic and social-behavioural outcomes. The findings highlight the importance of the ‘student voice’ and provides an insight into the experiences of teenagers in the first decade of the 21st Century

Effective Pre-school, Primary and Secondary Education 3-14 Project (EPPSE 3-14): influences on students’ dispositions in Key Stage 3: exploring enjoyment of school, popularity, anxiety, citizenship values and academic self-concept in Year 9

The Effective Pre-school, Primary and Secondary Education Project (EPPSE) has investigated the academic and social-behavioural development of approximately 3,000 children from the age of 3+ years since 1997. This Report and Research Brief reports on students’ dispositions when they were age 14 (Year 9) in six main areas: ‘enjoyment of school’, ‘academic self concept’ (English and maths), ‘popularity’, ‘citizenship values’ and ‘anxiety’. It examines how these dispositions have changed during Key Stage 3 (KS3) and the relationships between dispositions and a range of individual student, family, home, pre-, primary and secondary school measures. It shows how school experiences help to shape dispositions, and also explores the relationships between dispositions to school and students’ academic and social-behavioural outcomes. The findings highlight the importance of the ‘student voice’ and provides an insight into the experiences of teenagers in the first decade of the 21st Century

Creative Goal Modeling for Innovative Requirements

Context: When determining the functions and qualities (a.k.a. requirements) for a system, creativity is key to drive innovation and foster business success. However, creative requirements must be practically operationalized, grounded in concrete functions and system interactions. Requirements Engineering (RE) has produced a wealth of methods centered around goal modeling, in order to graphically explore the space of alternative requirements, linking functions to goals and dependencies. In parallel work, creativity theories from the social sciences have been applied to the design of creative requirements workshops, pushing stakeholders to develop innovative systems. Goal models tend to focus on what is known, while creativity workshops are expensive, require a specific skill set to facilitate, and produce mainly paper-based, unstructured outputs. Objective: Our aim in this work is to explore beneficial combinations of the two areas of work in order to overcome these and other limitations, facilitating creative requirements elicitation, supported by a simple extension of a well-known and structured requirements modeling technique. Method: We take a Design Science approach, iterating over exploratory studies, design, and summative validation studies. Results: The result is the Creative Leaf tool and method supporting creative goal modeling for RE. Conclusion: We support creative RE by making creativity techniques more accessible, producing structured digital outputs which better match to existing RE methods with associated analysis procedures and transformations

Lack of Association between the Reasons for and Time Spent Doing Physical Activity

Low levels of Physical Activity (PA) and sedentarism are associated with the onset of different pathologies and health problems. Regular physical activity has been linked with being beneficial to the health of the general population. Within this framework of analysis, the aim of the present study was to analyze the association between the time spent doing physical activity and the expressed motives for doing so, from which the innovative aspect of the paper emerges: the use of the time spent doing PA as a study variable of the phenomenon. The data analyzed come from the latest special Eurobarometer survey about the sport and physical activity done in Europe. Using an exploratory factorial analysis and a structural equations model, a six-dimensional factorial model was found that explains the reasons for doing PA, demonstrating that there is no relationship between the reasons for and time spent doing PA. The motivation is not a variable that explains the time spent doing PA, and another type of variable must be used to explain the phenomenon if PA is to be incentivized. Weaknesses of the study are that it works with individuals as a group and that the fundamental dependence on age is not introduced, which could determine interest in practicing PA. Similarly, the impact of the conditions of implementing PA, education, and family history should also be introduced into the model

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.Siirretty Doriast

Systemic classification of concern-based design methods in the context of enterprise architecture

Enterprise Architecture (EA) is a relatively new domain that is rapidly developing. "The primary reason for developing EA is to support business by providing the fundamental technology and process structure for an IT strategy” [TOGAF]. EA models have to model enterprises facets that span from marketing to IT. As a result, EA models tend to become large. Large EA models create a problem for model management. Concern-based design methods (CBDMs) aim to solve this problem by considering EA models as a composition of smaller, manageable parts—concerns. There are dozens of different CBDMs that can be used in the context of EA: from very generic methods to specific methods for business modeling or IT implementations. This variety of methods can cause two problems for those who develop and use innovative CBDMs in the field of Enterprise Architecture (EA). The first problem is to choose specific CBDMs that can be used in a given EA methodology: this is a problem for researchers who develop their own EA methodology. The second problem is to find similar methods (with the same problem domain or with similar frameworks) in order to make a comparative analysis with these methods: this is a problem of researchers who develop their own CBDMs related to a specific problem domain in EA (such as business process modeling or aspect oriented programming). We aim to address both of these problems by means of a definition of generic Requirements for CBDMs based on the system inquiry. We use these requirements to classify twenty CBDMs in the context of EA. We conclude with a short discussion about trends that we have observed in the field of concern-based design and modelin

Psychosocial, Socioeconomic, Behavioral, and Environmental Risk Factors for BMI and Overweight Among 9-to-11-Year Old Children

This study explored the risk factors for higher BMI and overweight in 9- to 11-year-old children using the 2007 California Children’s Healthy Eating and Exercise Practices Survey. A total of 741 children completed a two-day food and activity diary. Of these, 299 children participated in the follow-up telephone interview, reporting attitudes and beliefs. Linear regressions identified risk factors related to BMI z-scores; logistic regressions were used for binomial overweight status. Independent variables included children’s diet, activity, screen time, food modeling, family norms/rules, home environment, poverty, and parent education, adjusting for race/ethnicity. Parent education was the strongest risk factor with a clear gradient towards reduced risk as parent education improved. Children were .3 BMI z points lower and one-third less likely to be overweight as education level rose. Each serving of fried vegetables consumed was related to .3 point increase in BMI z. Children were 1.2-1.3 times more likely to be overweight with each increase in school lunch participation. Low-cost overweight prevention efforts targeting children with less parent education, school lunches, and consumption of fried vegetables may reduce BMI and help prevent childhood overweight. Additional investigation should determine the underlying factors contributing to the relationship between eating school lunch and overweight