Educational Technology Decision-Making: A Case Study on Technology Acquisition for 746,000 Ontario Students

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.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.