Stakeholders' Perceptions of Culinary Programs in Ontario Community Colleges

The hospitality industry in Canada is growing. With that growth is a demand for qualified workers to fill available positions within all facets of the hospitality industry, one ofthem being cooks. To meet this labour shortage, community colleges offering culinary arts programs are ramping up to meet the needs of industry to produce workplace-ready graduates. Industry, students, and community colleges are but three of the several stakeholders in culinary arts education. The purpose of this research project was to bring together a cross-section of stakeholders in culinary arts education in Ontario and qualitatively examine the stakeholders' perceptions of how culinary arts programs and the current curriculum are taught at community colleges as mandated by the Ministry of Training, Colleges and Universities (MTCU) in the Culinary Program Standard. A literature review was conducted in support of the research undertaking. Ten stakeholders were interviewed in preliminary and follow-up sessions, after which the data were analyzed using a grounded theory research design. The findings confirmed the existence of a disconnect amongst stakeholders in culinary arts education. Parallel to that was the discovery of the need for balance in several facets of culinary arts education. The discussions, as found in Chapter 5 of this study, addressed the themes of Becoming a Chef, Basics, Entrenchment, Disconnect, and Balance. The 8 recommendations, also found in Chapter 5, which are founded on the research results of this study, will be of interest to stakeholders in culinary education, particularly in the province of Ontario.

The Impact of a Text-Reader Support Program on Intermediate Students' Reading Comprehension

This study examined the efficacy of providing four Grade 7 and 8 students with reading difficulties with explicit instruction in the use of reading comprehension strategies while using text-reader software. Specifically, the study explored participants' combined use of a text-reader and question-answering comprehension strategy during a 6-week instructional program. Using a qualitative case study methodology approach, participants' experiences using text-reader software, with the presence of explicit instruction in evidence-based reading comprehension strategies, were examined. The study involved three phases: (a) the first phase consisted of individual interviews with the participants and their parents; (b) the second phase consisted of a nine session course; and (c) the third phase consisted of individual exit interviews and a focus group discussion. After the data collection phases were completed, data were analyzed and coded for emerging themes, with-quantitativ,e measures of participants' reading performance used as descriptive data. The data suggested that assistive technology can serve as an instructional "hook", motivating students to engage actively in the reading processes, especially when accompanied by explicit strategy instruction. Participants' experiences also reflected development of strategy use and use of text-reader software and the importance of social interactions in developing reading comprehension skills. The findings of this study support the view that the integration of instruction using evidence-based practices are important and vital components in the inclusion oftext-reader software as part of students' educational programming. Also, the findings from this study can be extended to develop in-class programming for students using text-reader software.

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.