Policy incentives in Canadian large-scale assessment: How policy levers influence teacher decisions about instructional change

Abstract

Large-scale assessment (LSA) is a tool used by education authorities for several purposes, including the promotion of teacher-based instructional change. In Canada, all 10 provinces engage in large-scale testing across several grade levels and subjects, and also have the common expectation that the results data will be used to improve instruction in classrooms. Yet despite agreement between ministries that instructional change based on LSA results is a positive development and employs data-based decision making at its heart, there remain significant differences in the kinds of incentives written into assessment policies in Canada. It is also true that implementation of the policies is less than uniform between schools and school divisions. Using mixed methods (survey data and follow-up interviews), this study examines which policy factors have the most significant impact on teacher decisions regarding the use of data. The findings indicate that highly incentivized policies correlate well to instructional change, including aspects of 1 Due to extenuating circumstances, and with the permission of the author and the lead editor, this article was published post-peer review and revisions, but prior to a final proof by the author or article metadata translations into Spanish and Portuguese (translations may be available upon request). Vol. 25 No. 115 2 both teaching (to) the curriculum as well as teaching to the test. Since the latter will be examined as a neither an educationally defensible practice nor a stated policy goal, the statement that 'incentives work' does not fully capture the nature of these impacts. Education Policy Analysis Archives

Dimensions of teachers’ data literacy: A systematic review of literature from 1990 to 2021

“…Copp, 2017; Datnow et al, 2012;Hardy, 2015;Schildkamp et al, 2019 71. Using data to make decision Copp, 2017;Datnow et al, 2021;Dunn et al, 2013a;Hardy, 2015;Hardy, 2019;Jimerson et al, 2019;Schildkamp et al, 2019;Snodgrass Rangel et al, 2019 1 3…”

“…Making decisions for school improvement Huffman & Kalnin, 2003;Prenger & Schildkamp, 2018;Schildkamp, 2019 86. Addressing schools' accountability requirements Copp, 2017;Ebbeler et al, 2016;Farrell , 2015;Hardy, 2015;Jacobs et al, 2009;Jimerson & Wayman, 2015;Marsh et al, 2010;Omoso et al, Indicators of the present study Sources: reviewed studies Mandinach and Gummer (2016) 88. Communicating data analysis results with colleagues Datnow et al, 2013;Howley et al, 2013;Militello et al, 2013;Omoso et al, 2019 89.…”

“…Lastly, teachers' knowledge about data includes understanding of externally administered assessment data and data system, such as state-level assessment policies related to data use (Copp, 2017;Hardy, 2019;Reed, 2015) and understanding the broader state-level contexts that impact data utilization within the school (Datnow et al, 2013;Dunn et al, 2013a;Ford, 2018;Omoso et al, 2019;Powell et al, 2021). Teachers may need to have thorough knowledge of educational government policies to ensure alignment between state-level curriculum initiatives and school-level assessment policies (Anderson et al, 2010;Copp, 2017;Gelderblom et al, 2016;Hardy, 2015).…”

“…Teachers' skills in data use also involve selecting specific data types appropriate for different purposes (Anderson et al, 2010;Jimerson et al, 2016;Kanjee & Moloi, 2014), analysing multiple sources of data on student learning outcomes (Datnow et al, 2012;Vanlommel et al, 2021;von der Embse et al, 2021), and integrating multiple data sources to arrive at a holistic assessment of student progress (Brunner et al, 2005;Farley-Ripple et al, 2019;Ford, 2018;Jacobs et al, 2009;Mausethagen et al, 2018). For example, teachers may need to apply different data analytic approaches when evaluating student outcomes based on school-based versus externally administered standardized assessments (Copp, 2017;Curry et al, 2016;Omoso et al, 2019;Wardrip & Herman, 2018;Zeuch et al, 2017). Data-literate teachers may also plan data analysis for targeted purposes, such as analyzing students' social-emotional outcomes (Abrams et al, 2021;Jimerson et al, 2021;von der Embse et al, 2021;Wardrip & Herman, 2018), identifying individual students' learning needs, making recommendations for curriculum revisions, or evaluating pedagogical approaches (Dunn et al, 2013a;Snodgrass Rangel et al, 2016;Wolff et al, 2019;Young, 2006).…”