The Development of Coaching Knowledge

Coaches are known to fulfill many different roles including leader, psychologist, friend, teacher, personnel manager, administrator, fundraiser and role model. The papers presented in this special issue emphasize these different roles by highlighting how coaches learn and how they foster an optimal learning environment. In the first section of this discussion article, I will briefly summarize the main issues covered in the five target papers. I will then propose that the learning environment of coaches needs to be put into a larger conceptual framework that would allow one to account for the variability of experiences that coaches go through before becoming a coach. The third section of this paper will describe three different settings in which coaches learn their skills. Finally, I will offer some concluding remarks and briefly outline directions for future studies.

Considering Efficacy of an Outdoor Environmental Education Program: Facilitating Elementary Students’ Environmental Knowledge, Attitudes, and Behaviours

Various sources have sought to consider the educational interventions that foster changes in perception of and attitudes toward nature, with the ultimate intent of understanding how education can be used to encourage environmentally responsible behaviours. With these in mind, the current study identified an outdoor environmental education program incorporating these empirically supported interventions, and assessed its ability to influence environmental knowledge, attitudes, and behaviours. Specifically, this study considered the following research questions: 1) To what degree can participation in this outdoor education program foster environmental knowledge and encourage pro-environmental attitudes and self-reported pro-environmental behaviours? 2) How is this effect different among students of different genders, and those who have different prior experiences in nature? Two motivational frameworks guided inquiry in the current study: the Value-Belief-Norm Model of Environmentalism (VBN) and the Theory of Planned Behaviour (TPB). The study employed a quantitative survey methodology, combining contemporary data measuring knowledge, attitudes, and behaviours with archived data collected by program staff, reflecting frequency of environmentally responsible behaviour. Further, a single qualitative item was included for which students provided “the first three words that [came] to mind when [they] think of the word nature.” Terms provided before and after the program were compared for differences in theme to detect subtle or underlying changes. Quantitative results indicated no significant change in student knowledge or attitudes through the outdoor environmental education program. However, a significant change in self-reported behaviour was identified from both the contemporary and archived data. This agreement in positive findings across the two data sets, collected using different measures and different participants, lends evidence of the program’s ability to encourage self-reported pro-environmental behaviour. Further, qualitative results showed some change in students’ perceptions of nature through the program, providing direction for future research. These findings suggest that this particular outdoor education program was successful in encouraging students’ self-reported environmentally responsible behaviour. This change was achieved without significant change in knowledge or environmental attitudes, suggesting that external factors not measured in this study might have played a role in affecting behaviour.

Improving the Performance of Database-Centric Applications Through Program Analysis

Thesis (Ph.D, Computing) -- Queen's University, 2016-09-30 09:55:51.506

Code pattern analysis of object-oriented programming languages

Code patterns, including programming patterns and design patterns, are good references for programming language feature improvement and software re-engineering. However, to our knowledge, no existing research has attempted to detect code patterns based on code clone detection technology. In this study, we build upon the previous work and propose to detect and analyze code patterns from a collection of open source projects using NiPAT technology. Because design patterns are most closely associated with object-oriented languages, we choose Java and Python projects to conduct our study. The tool we use for detecting patterns is NiPAT, a pattern detecting tool originally developed for the TXL programming language based on the NiCad clone detector. We extend NiPAT for the Java and Python programming languages. Then, we try to identify all the patterns from the pattern report and classify them into several different categories. In the end of the study, we analyze all the patterns and compare the differences between Java and Python patterns.