Session Information
11 SES 01 B, Exploitation of Research and Evaluation Data to Improve Education
Paper Session
Contribution
Internationally, there’s an increasing expectation for teachers to inform educational decisions with data, since a growing body of evidence indicates that decisions based on data have a positive impact on student results (Campbell & Levin, 2009; Carlson, Borman, & Robinson, 2011;Lai,McNaughton,Timperley, &Hsiao,2009). Teachers themselves, however, often have the feeling that they do not need such data, as they see their extensive experience as a solid and sufficient basis for decision making (Schildkamp & Kuiper, 2010). This paper sets out to explore how to get teachers motivated to use data and discusses the impact of the quality of motivation on data use. The following research questions are set forward: What is the impact of teachers’ motivation for data use on the use of data? What is the impact of decision making style and school factors on the motivation to use data?
Data in the context of schools can have a broad definition, it covers all the information – quantitative as well as qualitative - that is collected en arranged to study certain aspects within the school (Robinson & Lai, 2006). This paper sets out from a well-defined data source to prevent conceptual confusion with teachers. The schools involved in the research yearly use the same standardized tests, afterwards school advisory services provide them with a feedback report. The feedback report contains the Data: results of standardized test, on the individual (student) level and school level. Data use is conceptualized as the extent to which teachers use these feedback reports to underpin their decisions at the classroom level. Motivation for data use is studied using the Self-Determination Theory (SDT) (Gagné & Deci, 2000), that enables to elucidate which personal and contextual factors influence the motivation of teachers and its impact on data use behavior and attitude. SDT emphasizes the quality, instead of the quantity of motivation (Vansteenkiste, Lens, & Deci, 2006) and describes different sorts of motivation over a continuum of controlled to autonomous motivation. Additionally, the concept of efficacy is used in this study, a critical component of motivation (Bandura, 1997). Teachers have efficacy beliefs about themselves as well as about their entire school, so called collective efficacy (Goddard, Hoy, & Woolfolk Hoy, 2004). Hoy, Tarter and Woolfolk (2006) state that collective efficacy is tightly woven together with academic emphasis (on high achievement) and faculty trust. These 3 properties come together in a positive academic environment characterized by the label ‘academic optimism’ that shapes school norms and behavioural expectations. Further, the study also takes the decision making style of teachers into account. An intuitive style is seen as unconscious, strongly related with feelings, fast and triggered automatically. A rational style on the other hand is rather slow, conscious, controlled and can be made explicit (Epstein, 2008). This study investigates the relation between these individual and school features and motivation for data use.
Method
Expected Outcomes
References
Bandura, A. (1997). Self-efficacy: the exercise of control. New York: W.H. Freeman. Byrne, B. M. (2001). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming. Mahwah, New Jersey: Lawrence Erlbaum Associates. Campbell, C., & Levin, B. (2009). Using data to support educational improvement Educational Assessment, Evaluation and Accountability, 21(1), 47-65. Carlson, D., Borman, G., & Robinson, M. (2011). A multistate district-level cluster randomized trial of the impact of data-driven reform on reading and mathematics achievement. Education and Evaluation and Policy Analysis, 33(3), 378-398. Deci, E., & Ryan, R. (2000). The “what” en “why” of goal pursuits: human needs and the self-determination of behavior. Psychological Inquiry, 11, 227-268. Deci, E., & Ryan, R. (Eds.). (2002). Handbook of self-determination research. Rochester, NY: University of Rochester Press. Epstein, S. (2008). Intuition from the perspective of cognitive experiental self-theory. New York: Erlbaum. Hoy, A. W., & Tschannen-Moran, M. (2003). The conceptualization and measurement of faculty trust in schools.: Greenwich. Hoy, W., & Miskel, C. (2001). Educational Administration: Theory, Research and Practice. Boston: McGraw-Hill. Hoy, W. K., Tarter, C. J., & Woolfolk, A. (2006). Academic Optimism of Schools: A Force for Student Achievement. American Educational Research Journal, 43(3), 425-446. Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. Lai, M. K., McNaughton, S., Timperley, H., & Hsiao, S. (2009). Sustaining continued acceleration in reading comprehension achievement following an intervention. Educational Assessment, Evaluation and Accountability, 21(1), 81-100. Robinson, M., & Lai, M. K. (2006). Practitioner research for educators: A guide to improving classrooms and schools. California. Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482-496. Vanhoof, J., Vanlommel, K., Thijs, S., & Vanderlocht, H. (2014). Data use by Flemish School Principals: Impact of Attitude, Self-efficacy and External Expectations Educational Studies, 40(1), 48-62. Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic Versus Extrinsic Goal Contents in Self-Determination Theory: Another Look at the Quality of Academic Motivation. Educational Psychologist, 41(1), 19-31.
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