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In recent decades, higher education institutions around the world have come to depend on complex digital infrastructures. In addition to registration, financial, and other operations platforms, digital classroom tools with built-in learning analytics capacities underpin many course delivery options. Taken together, these intersecting digital systems collect vast amounts of data from students, staff, and faculty. Educators' work environments--and knowledge about their work environments--have been shifted by this rise in pervasive datafication. In this paper, we overview the ways faculty in a variety of institutional status positions and geographic locales understand this shift and make sense of the datafied infrastructures of their institutions. We present findings from a comparative case study (CCS) of university educators in six countries, examining participants' knowledge, practices, experiences, and perspectives in relation to datafication, while tracing patterns across contexts. We draw on individual, systemic, and historical axes of comparison to demonstrate that in spite of structural barriers to educator data literacy, professionals teaching in higher education do have strong and informed ethical and pedagogical perspectives on datafication that warrant greater attention. Our study suggests a distinction between the understandings educators have of data processes, or technical specifics of datafication on campuses, and their understanding of big picture data paradigms and ethical implications. Educators were found to be far more knowledgeable and comfortable in paradigm discussions than they were in process ones, partly due to structural barriers that limit their involvement at the process level.

This paper is a narrative of our experience in analyzing and merging data files provided to us by the Education Quality and Accountability Office (EQAO). In the paper, we propose a scheme of merging data files by means of Structured Query Language (SQL, pronounced as "sequel"). Although, the narrative of our experiences using this merging scheme could have been extended to any number of data files, the aim of this work was to merge only three EQAO data files. Via this merge process, we were able to gain meaningful information and facilitate the analysis of EQAO data to answer our research questions. By using SQL queries, our approach was not only to analyze the available data files but also to construct a narrative about viewing and handling data contained in the files.

In the quest to improve measured educational outcomes national governments across the The Organisation for Economic Co-operation and Development (OECD) and beyond have instituted large-scale assessment (LSA) policies in their public schools. Controversy almost universally follows the implementation of such testing, related to such topics as: a) the uncertain quality of the tests themselves as psychometrics measures; b) the uses to which the data can and should be put; c) the unintended consequences of test-preparation activities and resulting score inflation; and d) the effects of high-stakes tests on students. Debates of this nature naturally involve and impact the attitudes and opinions of teachers related to their collection and use of these data. This paper examines the impact of these attitudes using both the qualitative and quantitative data from a large-scale research study on Canadian provincial assessment. Data were collected from nation-wide teacher surveys as well as interviews with teachers, administrators and district-level staff. Results show that teacher attitudes about these assessments are strongly correlated to classroom-level instructional change. Three attitudinal factors have significant effects on teaching (to) the provincial curricula, yet none significantly affects the use of less constructive instructional strategies also known as "teaching to the test." Specifically, the belief that large-scale assessment data have more appropriate uses and the belief that these data could lead to school improvement were significant factors in facilitating change. The implications of these findings are profound in that large-scale assessment policy cannot succeed even by its own standards without more buy in from teaching professionals.

Collaborative inquiry (CI) has emerged as a dominant structure for educators' professional learning in the 21st century. The purpose of this paper is to analyze publicly available documents and policies related to CI in Ontario in order to better understand the documentary scope and spread of this professional learning model in the province. We begin by defining the parameters of CI as a dominant professional learning model before detailing our methodology for selecting and analyzing CI policies and documents at both ministry and school board levels. In our subsequent analysis, we enumerate emergent themes and findings and offer three sample case studies that illustrate how school boards in the province are documenting their experiences with CI. Finally, we conclude with a discussion of potential tensions within current CI policies as a basis for future research and policy development.

This paper updates earlier work in which we defined three generations of distance education pedagogy. We then describe emerging technologies that are most conducive to instructional designs that evolve with each generation. Finally we discuss matching the pedagogies with learning outcomes. (Contains 3 figures.)

Canadian institutions of higher education are major players on the international arena for educating future generations and producing leaders around the world in various fields. In the last decade, Canadian universities have seen an influx in their incoming international students, who contribute over $3.5 billion to the Canadian economy (Madgett & Belanger 2008, p. 195). Research in Canadian post-secondary institutions is booming, especially in education (SSHRC, 2011)--for the academic year 2010-2011, of the 12 subject areas, the total SSHRC funding for projects in education, ranked fourth, exceeding $27 million. All of these variables place Canadian higher education in a leading and strategic position in several educational research fields. One can imagine the wealth of knowledge about trends in higher education that could be revealed if the large amount of data generated by Canadian universities were systematically analyzed and handled using techniques such as data mining. However, not much can be achieved from the unharnessed knowledge accumulated on a daily basis, as the advancement of data mining research that would provide the ultimate tool to learn about trends and changes in Canadian institutions is often held back by inadequate data warehousing, as well as by privacy, confidentiality, and copyright regulations. In this paper, we engage in a critical discussion/analysis of the interface between data mining research in higher education and the legal implications of such a tool. (Contains 4 figures and 4 footnotes.)

This paper reports on an interpretive study in which 25 elementary principals were asked about their assessment knowledge, the use of large-scale assessments in their schools, and principals' perceptions on their roles with respect to large-scale assessments. Principals in this study suggested that the current context of large-scale assessment and accountability has resulted in their roles being redefined. In particular, they suggested that it is the principal's role to teach other staff members about assessment, to advocate for assessment, and to organize and manage data. We argue that these emerging roles have the potential to redefine instructional leadership in schools.

Self-regulated learning processes have a potential to enhance the motivation of knowledge workers to take part in learning and reflection about learning, and thus contribute to the resolution of an important research challenge in workplace learning. An equally important research challenge for the successful completion of each step of a self-regulatory process is to enable learners to be aware of the characteristics of their organizationally-embedded learning context. In this paper, we describe how a combination of pedagogy and Semantic Web-based technologies can be utilized to address the above two challenges. Specifically, we demonstrate the proposed solution through the Learn-B tool that leverages ontologies to support self-regulation in organizational learning. (Contains 3 tables and 2 figures.)

Learning analytics offers higher education valuable insights that can inform strategic decision-making regarding resource allocation for educational excellence. Research demonstrates that learning management systems (LMSs) can increase student sense of community, support learning communities and enhance student engagement and success, and LMSs have therefore become core enterprise component in many universities. We were invited to undertake a current state analysis of enterprise LMS use in a large research-intensive university, to provide data to inform and guide an LMS review and strategic planning process. Using a new e-learning analytics platform, combined with data visualization and participant observation, we prepared a detailed snapshot of current LMS use patterns and trends and their relationship to student learning outcomes. This paper presents selected data from this "current state analysis" and comments on what it reveals about the comparative effectiveness of this institution's LMS integration in the service of learning and teaching. More critically, it discusses the reality that the institutional planning process was nonetheless dominated by technical concerns, and made little use of the intelligence revealed by the analytics process. To explain this phenomenon we consider theories of change management and resistance to innovation, and argue that to have meaningful impact, learning analytics proponents must also delve into the socio-technical sphere to ensure that learning analytics data are presented to those involved in strategic institutional planning in ways that have the power to motivate organizational adoption and cultural change. (Contains 2 tables and 4 figures.)

Poverty is generally defined as income or expenditure insufficiency, but the economic condition of a household also depends on its real and financial asset holdings. This paper investigates measures of poverty that rely on indicators of household net worth. We review and assess two main approaches followed in the literature: income-net worth measures and asset-poverty. We provide fresh cross-national evidence based on data from the Luxembourg Wealth Study. (Contains 3 figures, 6 tables, and 16 footnotes.)

Data on student achievement are increasingly being used to support effective policy and practice, and to move education systems towards more evidence-informed approaches to large-scale improvement. In this paper, we outline strategies used in Ontario, Canada to create, enhance and apply a range of data to support educational improvement. These strategies were intended to integrate the collection of data and its use at the three levels of school, district, and province. The strategy also included improving educator capacity to use data and the development of better analytic tools to understand data in context.

Design prescriptions to create web-based courses and sites that are dynamic, easy-to-use, interactive and data-driven, emerge from a "how to do it" approach. Unfortunately, the theory behind these methods, prescriptions, procedures or tools, is rarely provided and the important terms, such as "easy-to-use", to which these prescriptions refer are not defined. The empirical results reported here bring lighting on the meaning of several design prescriptions that contain qualitative attributes. This paper aims at clarifying the meaning of several web-based course design prescriptions found in the literature in the context of two music web-based courses. Two examples are presented and the results of the students' assessment regarding several design prescriptions are given. First, what we learned while producing the first release of the web part of an undergraduate music course entitled Teaching and Music Technology is presented. Then, what else we learned when the second release was assessed by students is detailed. The next part concerns what we used while developing the undergraduate music course French-Canadian folk which gives access to several music files and scores. Again the results of the students' assessment are presented. The list of the various technologies that must be highly mastered to produce such musical content is given.

As firms accumulate more data, users' data-sharing decisions may polarize. Some users may share all data, whereas others may share no data, becoming "digital hermits." When users share multidimensional data about themselves with a firm, the firm learns about the correlations between different dimensions of user data. We incorporate this type of learning into a model of a data market in which a firm acquires data from users with privacy concerns. Each user can share no data, only nonsensitive data, or their full data with the firm. As the firm collects more data and becomes better at drawing inferences about a user's privacy-sensitive data from their nonsensitive data, the share of new users who share no data ("digital hermits") grows. This growth of digital hermits occurs even though the firm offers higher compensation for a user's nonsensitive data and a user's full data as its ability to draw inferences improves. At the same time, the share of new users who share their full data also grows. The model thus predicts a polarization of users' data-sharing choices away from nonsensitive data sharing to no sharing and full sharing. Our model suggests that recent privacy policies, which are focused on control of data rather than inferences, may be misplaced. History: Anthony Dukes served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: Partial financial support was received from the Social Sciences and Humanities Research Council of Canada [Grant 435-2023-0492]. [ABSTRACT FROM AUTHOR]

This paper aimed to establish preliminary normative data scores of nasalance value for the Arab Emirati speakers, and to compare them to other Arabic speakers, particularly the Saudi, Jordanian and Egyptian speakers. Design was a crosssectional study where nasality scores (nasalance percentages) were obtained under oral and vowel passage tasks. Participants were 104 Emirati individuals (54 males, age range 18-27, and 50 females, age range 18-27). Each participant was asked to extend the /a:/ vowel and read a passage in Arabic. A nasometer model II, 6450 (KayPentax, Canada) was utilized for nasalance scores computation. The ANOVA revealed no significant differences between the female and male Emirati speakers; nasalance scores in both the vowel (males= 26.35, females= 23.3) and the oral passage tasks (males= 15, females= 15.1). The Emirati speakers had higher nasalance scores than the Saudi speakers in both tasks, and in the /a:/vowel task compared to the Egyptian and Jordanian speakers. Language and dialect are two important variables in determining the nasalance normative scores. [ABSTRACT FROM AUTHOR]

Health inequalities amplified by the COVID-19 pandemic have disproportionately affected racialized and equity-deserving communities across Canada. In the Municipality of Peel, existing data, while limited, illustrates that individuals from racialized and equity-deserving communities continue to suffer, receive delayed care, and die prematurely. In response to these troubling statistics, grassroots community advocacy has called on health systems leaders in Peel to work with community and non-profit organizations to address the critical data and infrastructure gaps that hinder addressing the social determinants of health in the region. To support these advocacy efforts, we used a community-based participatory research approach to understand how we might build a data collection ecosystem across sectors, alongside community residents and service providers, to accurately capture the data about the social determinants of health. This approach involved developing a community engagement council, defining the problem with the community, mapping what data is actively collected and what is excluded, and understanding experiences of sociodemographic data collection from community members and service providers. Guided by community voices, our study focused on sociodemographic data collection in the primary care context and identified which service providers use and collect these data, how data are used in their work, the facilitators and barriers to data use and collection. Additionally, we gained insight into how sociodemographic data collection could be respectful, safe, and properly governed from the perspectives of community members. From this study, we identify a set of eight recommendations for sociodemographic data collection and highlight limitations. This foundational community-based work will inform future research in establishing data governance in partnership with diverse and equity-deserving communities. [ABSTRACT FROM AUTHOR]

• Rural broadband strategies in Canada face data and mapping challenges. • Research shows challenges with being deemed "served" or "underserved". • In southwestern Ontario rural Internet quality levels may fall below the government's aspirational targets. • We recommend provider reported data cross-referenced with user experience data and detailed mapping. This paper examines challenges to evidence-based decision-making in the design and implementation of rural broadband investment programs. Our focus is on Canada, and the apparent need for further intra-rural broadband research and better data and mapping for informing public investment decisions, but similar challenges are evident in the international literature. Based on proprietary telecommunication provider datasets, the Canadian Radio-television and Telecommunication Commission (CRTC) estimates that broadband services with advertised speeds that meet its basic universal service targets (50 Mbps download and 10 Mbps upload) are available to 87.4 percent of households in Canada. In rural areas however, services that meet CRTC's speed targets are available to 45.6 percent of households. Moreover, effective speeds and service quality levels that suppliers deliver and users experience tend to fall well below the government's aspirational targets. In response to demand for better broadband, a variety of initiatives are directing public investment to the deployment of regional and rural broadband networks, which are typically owned and operated by private companies. There remains a serious lack of relevant data and its effective use in creating rural broadband strategies and managing public investment projects. Evidence from the literature suggest that this affects the degree and quality of geo-spatial and econometric analysis that results in a limited empirical basis to allocate scarce public investments, aggregate demand of consumers/communities, and assess the outcomes of rural broadband initiatives ex post. This paper provides a historical overview of rural broadband development in Canada and questions if the body of knowledge to inform public investment initiatives has grown sufficiently to ensure their effectiveness and sustainability. With a regional case from southwestern Ontario, Canada, we discuss the findings of the literature review, characterize the broadband data challenge, and discuss the importance of proprietary provider data cross-referenced with Internet user experience data. [ABSTRACT FROM AUTHOR]

The article focuses on Canada's Employment and Social Development Canada (ESDC) and its approach to evaluating the impact of Active Labour Market Policies (ALMPs). It highlights Canada's strategy of utilizing rich administrative data to conduct internal evaluations, supported by external peer reviewers, to assess the effectiveness of various ALMPs in improving employment outcomes.

The article focuses on Canada's use of administrative data to evaluate the impact of active labour market policies (ALMPs). It discusses the shift from traditional survey-based evaluations to leveraging administrative data from Employment and Social Development Canada (ESDC) and the Canada Revenue Agency (CRA). It states that by integrating these datasets, Canada aims to improve the accuracy, efficiency, and cost-effectiveness of policy impact assessments.

Abstract: Digital data collected for forensics analysis often contain valuable information about the suspects’ social networks. However, most collected records are in the form of unstructured textual data, such as e-mails, chat messages, and text documents. An investigator often has to manually extract the useful information from the text and then enter the important pieces into a structured database for further investigation by using various criminal network analysis tools. Obviously, this information extraction process is tedious and error-prone. Moreover, the quality of the analysis varies by the experience and expertise of the investigator. In this paper, we propose a systematic method to discover criminal networks from a collection of text documents obtained from a suspect’s machine, extract useful information for investigation, and then visualize the suspect’s criminal network. Furthermore, we present a hypothesis generation approach to identify potential indirect relationships among the members in the identified networks. We evaluated the effectiveness and performance of the method on a real-life cybercrimine case and some other datasets. The proposed method, together with the implemented software tool, has received positive feedback from the digital forensics team of a law enforcement unit in Canada. [Copyright &y& Elsevier]

• Strategies to model opponent effects in the analysis of severity of crashes involving two-parties. • A data workflow is presented for preparing data for modelling party interactions. • Single-level and hierarchical models are compared. • Full-sample and sub-sample models are compared. • The results provide information useful to select a modelling strategy for crash severity. Road crashes impose an important burden on health and the economy. Numerous efforts have been undertaken to understand the factors that affect road collisions in general, and the severity of crashes in particular. In this literature several strategies have been proposed to model interactions between parties in a crash, including the use of variables regarding the other party (or parties) in the collision, data subsetting, and estimating models with hierarchical components. Since no systematic assessment has been conducted of the performance of these strategies, they appear to be used in an ad-hoc fashion in the literature. The objective of this paper is to empirically evaluate ways to model party interactions in the context of crashes involving two parties. To this end, a series of models are estimated using data from Canada's National Collision Database. Three levels of crash severity (no injury/injury/fatality) are analyzed using ordered probit models and covariates for the parties in the crash and the conditions of the crash. The models are assessed using predicted shares and classes of outcomes, and the results highlight the importance of considering opponent effects in crash severity analysis. The study also suggests that hierarchical (i.e., multi-level) specifications and subsetting do not necessarily perform better than a relatively simple single-level model with opponent-related factors. The results of this study provide insights regarding the performance of different modelling strategies, and should be informative to researchers in the field of crash severity. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article offers information on how the data contained in the tables in the "Canadian Economic Observer" are presented. The data are seasonally adjusted and retrieved from the Canadian Socio-Economic Information Management System (CANSIM), the Statistics Canada computerized data bank. The identification numbers found at the start of the data serve as the CANSIM Identifier for the unadjusted or seasonally adjusted data.

The article informs that data found in the cited tables are as available on February 8, 2008, while items contained in this publication are seasonally adjusted. It mentions that the publication's data were recollected from the Official Mark of Statistics Canada for its Canadian Socio-Economic Information Management System (CANSIM). Moreover, it notes that the identification numbers found at the beginning of each row of the mentioned data are referred to the CANSIM Identifier.

The article focuses on the concern by the health researchers in Canada over the move by the Canadian government to replace the mandatory long-form census of the country with a voluntary survey. Researchers claim that the population data collected from such approach will be biased and will restrain research into the health's social determinants. Moreover, the Canadian Institute for Health and Information and the Canadian Institutes of Health Research declined to comment on the issue.

Copyright of Canadian Public Policy is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a second step in the development of the Canadian Regional Data Assimilation System following Fillion et al., this study extends the approach to the four-dimensional variational data assimilation (4D-Var) context. Emphasis is first put on illustrating the importance of controlling lateral boundary conditions (LBCs). The use in the minimization of a horizontal grid over a domain exceeding the horizontal grid of the high-resolution nonlinear model is then proposed. The authors examine the performance of this 4D-Var formulation as an upcoming upgrade to the currently operational regional three-dimensional variational data assimilation (3D-Var) system. Forecast verifications against radiosonde data for 118 winter cases and 118 summer cases were performed. Results indicate a slight positive impact up to 48 h against North American radiosondes, but with a significant positive impact (especially for winds) at mid- and high latitudes during the summer. Accumulated precipitation scores over 24 h, whether during the first or second day of the forecasts, are slightly improved. The regional 4D-Var analysis system described in this study can run within current real-time 'regional run' allocation for operations at the Canadian Meteorological Center (CMC). Future improvements of this system are briefly mentioned especially regarding the upcoming computer upgrade at CMC. [ABSTRACT FROM AUTHOR]

This article evaluates the forecasting performance of dynamic factor models for Canadian inflation. We find that factor models are as good as more conventional forecasting models, while a model estimated using only US data, is at least as useful as a model that incorporates Canadian data. This suggests that the United States is a source of data that could be beneficial to its trading partners for forecasting purposes. [ABSTRACT FROM AUTHOR]