Investigation of motivation strategies used by school teachers for workplace engagement

The purpose of this study was to explore the strategies that elementary school teachers use to be engaged in their work. Participation was solicited from a random sample of schools stratified by location (i.e., urban, inner city, and rural) of a large school board. The study used an anonymous quantitative/qualitative questionnaire. The survey tool was based upon Kahn's (1990) psychological engagement framework, which presents the foundation of availability of self, meaningfulness of work, and safety while at work. Forty-one surveys were analyzed descriptively including a subgroup of self-rated highly engaged teachers. Teachers tended to favour physical and emotional strategies compared to cognitive type strategies, with the exception of the highly engaged subgroup. The theme of preferred strategies reflected a setting outside the school/workplace, that is, a preference for horne based strategies. The study's main contribution highlights the teachers' sense of importance for physical and emotional health in a profession that is heavily focused in the cognitive domain. This may influence administrative and teacher discourse regarding workplace engagement with strategies to help reduce stress and to maintain and increase teacher engagement.

Introducing e-learning in a large multisite academic health sciences centre: a case study of e-curriculum planning and educator support /

This qualitative study investigated how a team of 7 hospital educators collaborated to develop e-curriculum units to pilot for a newly acquired learning -r management system at a large, multisite academic health sciences centre. A case study approach was used to examine how the e-Curriculum Team was structured, how the educators worked together to develop strategies to better utilize e-leaming in their ovwi practice, what e-curriculum they chose to develop, and how they determined their priorities for e-curriculum development. It also inquired into how they planned to involve other educators in using e-leaming. One set of semistructured interviews with the 6 hospital educators involved in the project, as well as minutes of team meetings and the researcher's journal, were analyzed (the researcher was also a hospital educator on the team). Project management structure, educator support, and organizational pressures on the implementation project feature prominently in the case study. This study suggests that implementation of e-leaming will be more successful if (a) educators involved in the development of e-leaming curriculum are supported in their role as change agents, (b) the pain of vmleaming current educational practice is considered, (c) the limitations of the software being implemented are recognized, (d) time is spent leaming about best practice, and (e) the project is protected as much as possible from organizational pressures and distractions.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.