5 resultados para data-driven modelling
em Brock University, Canada
Resumo:
This study occurred in 2009 and questioned how Ontario secondary school principals perceived their role had changed, over a 7 year period, in response to the increased demands of data-driven school environments. Specifically, it sought to identify principals' perceptions on how high-stakes testing and data-driven environments had affected their role, tasks, and accountability responsibilities. This study contextualized the emergence of the Education Quality and Accountability Offices (EQAO) as a central influence in the creation of data-driven school environments, and conceptualized the role of the principal as using data to inform and persuade a shift in thinking about the use of data to improve instruction and student achievement. The findings of the study suggest that data-driven environments had helped principals reclaim their positional power as instructional leaders, using data as an avenue back into the classroom. The use of data shifted the responsibilities of the principal to persuade teachers to work collaboratively to improve classroom instruction in order to demonstrate accountability.
Resumo:
Very little research has examined K–12 educational technology decision-making in Canada. This collective case study explores the technology procurement process in Ontario’s publicly funded school districts to determine if it is informed by the relevant research, grounded in best practices, and enhances student learning. Using a qualitative approach, 10 senior leaders (i.e., chief information officers, superintendents, etc.) were interviewed. A combination of open-ended and closed-ended questions were used to reveal the most important factors driving technology acquisition, research support, governance procedures, data use, and assessment and return on investment (ROI) measures utilized by school districts in their implementation of educational technology. After participants were interviewed, the data were transcribed, member checked, and then submitted to “Computer-assisted NCT analysis” (Friese, 2014) using ATLAS.ti. The findings show that senior leaders are making acquisitions that are not aligned with current scholarship and not with student learning as the focus. It was also determined that districts struggle to use data-driven decision-making to support the governance of educational technology spending. Finally, the results showed that districts do not have effective assessment measures in place to determine the efficacy or ROI of a purchased technology. Although data are limited to the responses of 10 senior leaders, findings represent the technology leadership for approximately 746,000 Ontario students. The study is meant to serve as an informative resource for senior leaders and presents strategic and research-validated approaches to technology procurement. Further, the study has the potential to refine technology decision-making, policies, and practices in K–12 education.
Resumo:
Mobile augmented reality applications are increasingly utilized as a medium for enhancing learning and engagement in history education. Although these digital devices facilitate learning through immersive and appealing experiences, their design should be driven by theories of learning and instruction. We provide an overview of an evidence-based approach to optimize the development of mobile augmented reality applications that teaches students about history. Our research aims to evaluate and model the impacts of design parameters towards learning and engagement. The research program is interdisciplinary in that we apply techniques derived from design-based experiments and educational data mining. We outline the methodological and analytical techniques as well as discuss the implications of the anticipated findings.
Resumo:
Mobile augmented reality applications are increasingly utilized as a medium for enhancing learning and engagement in history education. Although these digital devices facilitate learning through immersive and appealing experiences, their design should be driven by theories of learning and instruction. We provide an overview of an evidence-based approach to optimize the development of mobile augmented reality applications that teaches students about history. Our research aims to evaluate and model the impacts of design parameters towards learning and engagement. The research program is interdisciplinary in that we apply techniques derived from design-based experiments and educational data mining. We outline the methodological and analytical techniques as well as discuss the implications of the anticipated findings.
Resumo:
Behavioral researchers commonly use single subject designs to evaluate the effects of a given treatment. Several different methods of data analysis are used, each with their own set of methodological strengths and limitations. Visual inspection is commonly used as a method of analyzing data which assesses the variability, level, and trend both within and between conditions (Cooper, Heron, & Heward, 2007). In an attempt to quantify treatment outcomes, researchers developed two methods for analysing data called Percentage of Non-overlapping Data Points (PND) and Percentage of Data Points Exceeding the Median (PEM). The purpose of the present study is to compare and contrast the use of Hierarchical Linear Modelling (HLM), PND and PEM in single subject research. The present study used 39 behaviours, across 17 participants to compare treatment outcomes of a group cognitive behavioural therapy program, using PND, PEM, and HLM on three response classes of Obsessive Compulsive Behaviour in children with Autism Spectrum Disorder. Findings suggest that PEM and HLM complement each other and both add invaluable information to the overall treatment results. Future research should consider using both PEM and HLM when analysing single subject designs, specifically grouped data with variability.
