Using WebCT to Support a Community of Practice

Reflecting a view of “teaching as both an intellectual and practical activity” the Queen’s University Bachelor of Education program has multi-week in-school practicum sessions separated by periods of on-campus course work. The expectation is that teacher candidates will bring together theory and practice as they reflect upon their daily classroom experiences. The reality often is that, while isolated from the university environment and caught up in the pressures of teaching, little deep reflection takes place. For reflection and critical examination of experience to occur, teacher candidates need to share and discuss on a daily basis their practice teaching experience. For the past few years, students in my secondary school mathematics curriculum course, through a WebCT based conference, have been provided, while away from campus, with a place for on-going sharing of teaching stories and dilemmas. In the Fall of 2004 eight-five percent of the class took part in the discussions, posting a total of 667 messages over a 9 week period. In an effort to increase the value of this practicum conference we have analysed the topic threads arising in the conversation, surveyed the participants concerning their impressions of the sharing experience, and conducted in-depth interviews with a sampling of the class. This session will present the results of this study and provide an opportunity to discuss ways in which an online discussion can support the building of community and the exchange of experience while students in professional programs are disbursed in practice/clinical settings.

Academic Integrity and Senior Nursing Undergraduate Clinical Practice

Background: Academic integrity (AI) has been defined as the commitment to the values of honesty, trust, fairness, respect, and responsibility with courage in all academic endeavours. The senior years of nursing studies provide an intersection for students to transition to professional roles through student clinical practice. It is essential to understand what predicts senior nursing students’ intention to behave with AI so that efforts can be directed to initiatives focused on strengthening their commitment to behaving with AI. Research Questions: To what extent do students differ on Theory of Planned Behaviour (TPB) variables? What predicts intention to behave with academic integrity among senior nursing students in clinical practice across three different Canadian Schools of Nursing? Method: The TPB framework, an elicitation (n=30) and two pilot studies (n=59, n=29) resulted in the development of a 38 question (41-item) self-report survey (Miron Academic Integrity Nursing Survey—MAINS: α>0.70) that was administered to Year 3 and 4 students (N=339). Three predictor variables (attitude, subjective norm, perceived behavioural control) were measured with students’ intention to behave with AI in clinical. Age, sex, year of study, program stream, students’ understanding of AI policies, and locations where students accessed AI information were also measured. Results: Hierarchical multiple regression analyses revealed that background, site, and TPB variables explained 32.6% of the variance in intention to behave with academic integrity. The TPB variables explained 26.8% of the variance in intention after controlling for background and site variables. In the final model, only the TPB predictor variables were statistically significant with Attitude having the highest beta value (beta=0.35, p<0.001), followed by Subjective Norm (beta=0.21, p<0.001) and Perceived Behavioural Control (beta=0.12, p<0.02). Conclusion: Student attitude is the strongest predictor to intention to behave with AI in clinical practice and efforts to positively influence students’ attitudes need to be a focus for schools, curricula, and clinical educators. Opportunities for future research should include replicating the current study with students enrolled in other professional programs and intervention studies that examine the effectiveness of specific endeavours to promote AI in practice.

IMPROVING THE PERFORMANCE OF DATABASE-CENTRIC APPLICATIONS THROUGH PROGRAM ANALYSIS

Modern software applications are becoming more dependent on database management systems (DBMSs). DBMSs are usually used as black boxes by software developers. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use nowadays. Using ORM, objects in Object-Oriented languages are mapped to records in the database, and object manipulations are automatically translated to SQL queries. As a result of such conceptual abstraction, developers do not need deep knowledge of databases; however, all too often this abstraction leads to inefficient and incorrect database access code. Thus, this thesis proposes a series of approaches to improve the performance of database-centric software applications that are implemented using ORM. Our approaches focus on troubleshooting and detecting inefficient (i.e., performance problems) database accesses in the source code, and we rank the detected problems based on their severity. We first conduct an empirical study on the maintenance of ORM code in both open source and industrial applications. We find that ORM performance-related configurations are rarely tuned in practice, and there is a need for tools that can help improve/tune the performance of ORM-based applications. Thus, we propose approaches along two dimensions to help developers improve the performance of ORM-based applications: 1) helping developers write more performant ORM code; and 2) helping developers configure ORM configurations. To provide tooling support to developers, we first propose static analysis approaches to detect performance anti-patterns in the source code. We automatically rank the detected anti-pattern instances according to their performance impacts. Our study finds that by resolving the detected anti-patterns, the application performance can be improved by 34% on average. We then discuss our experience and lessons learned when integrating our anti-pattern detection tool into industrial practice. We hope our experience can help improve the industrial adoption of future research tools. However, as static analysis approaches are prone to false positives and lack runtime information, we also propose dynamic analysis approaches to further help developers improve the performance of their database access code. We propose automated approaches to detect redundant data access anti-patterns in the database access code, and our study finds that resolving such redundant data access anti-patterns can improve application performance by an average of 17%. Finally, we propose an automated approach to tune performance-related ORM configurations using both static and dynamic analysis. Our study shows that our approach can help improve application throughput by 27--138%. Through our case studies on real-world applications, we show that all of our proposed approaches can provide valuable support to developers and help improve application performance significantly.