Data-driven instruction: Difference between revisions

Revision as of 21:34, 18 November 2016

Revision as of 06:45, 19 November 2016

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Because of the lack of timely feedback regarding the results plus the inability to personalize the approach, summative assessments are not readily used for data driven instruction in the classroom. Instead, a variety of information gleaned from different forms of assessments should be used to make decisions about student progress and performance within data-driven instruction. The use of multiple measures of different forms and at different times to make instructional decisions is referred to as triangulation (Boudett, City & Murname, 2013).

== Implications ==

=== For school bistricts ===

The primary implication for school districts is in ensuring high quality and relevant data is gathered and available. Beyond creating systems to gather and share the data, the school district must provide the expertise, in the form of data expert personnel and/or the access to professional development resources to ensure school building leaders are able to access and use the data (Swan and Mazur, 2011).

Another critical component of the responsibility of the district is to provide the leadership and vision to promulgate the use of information about student performance to improve teaching practice. Zavadsky and   Dolejs suggest two areas for school districts to consider:

“The first is data collection and analysis. Districts and schools must carefully consider what data they need to collect, develop instruments with which to collect the data, and make the data available as soon as possible. The second component is data use. Principals and district leaders must give teachers sufficient time and training to understand the data and learn how to respond to what the data reveal” (2006, p.  33).

While the literature shows the vital importance of the role of the district in setting the stage for data driven instruction, more of the work of connecting student performance to classroom practices happens at the school and classroom level.

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== References ==

Black, P. & Wiliam, D. (1998). Inside the Black Box: Raising Standards Through Classroom Assessment. Phi Delta Kappan, 80(2), pp.139-148 139–148.

Boudett, K. P., City, E. A., Murname, R. J. (2013). Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning. Cambridge, MA: Harvard Education Press.

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Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58.

Eagle, M., Corbett, A., Stamper, J., McLaren, B. M., Baker, R., Wagner, A., … Mitchell, A. (2016). Predicting Individual Differences for Learner Modeling in Intelligent Tutors from Previous Learner Activities (pp.  55–63). ACM Press. {{doi|10.1145/2930238.2930255}}

Elmore, R. F. (2000). Building a new structure for school leadership. Albert Shanker Institute. Retrieved from http://eric.ed.gov/?id=ED546618

Furlong-Gordon, J. M. (2009). Driving classroom instruction with data:   From the district to the teachers to the classroom (Ed.D.). Wilmington University (Delaware), United States -- DelawareStates—Delaware. Retrieved from http://search.proquest.com/docview/250914319/

Gold, S. (2005). DRIVEN by DATA. Technology & Learning, 25(11), 6,8-9.

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Mokhtari, K., Rosemary, C. A., & Edwards, P. A. (2007). Making Instructional Decisions Based on Data: What, How, and Why. Reading Teacher, 61(4), 354–359.

Moriarty, T. W. (2013). Data-driven decision making: Teachers’ use of data in the classroom (Ph.D.). University of San Diego, United States -- CaliforniaStates—California. Retrieved from http://search.proquest.com/docview/1432373944/

Neuman, S. (2016). Code Red: The Danger of Data-Driven Instruction. Educational Leadership, 74(3), pps. 24-29.

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Shanahan, T., Callison, K., Carriere, C., Duke, N. K., Pearson, P. D., Schatschneider, C., & Torgesen, J. (2010). Improving Reading Comprehension in Kindergarten through 3rd Grade: IES Practice Guide. NCEE 2010-4038. What Works Clearinghouse. Retrieved from http://eric.ed.gov/?id=ED512029

Stamper, J., Ed, Pardos, Z., Ed, Mavrikis, M., Ed, McLaren, B. M., Ed, & International Educational Data Mining Society. (2014). Proceedings of the Seventh International Conference on Educational Data Mining (EDM) (7th, London, United Kingdom, July 4-74–7, 2014). International Educational Data Mining Society. http://www.educationaldatamining.org

Swan, G., & Mazur, J. (2011). Examining data driven decision making via formative assessment: A confluence of technology, data interpretation heuristics and curricular policy. Gene, 1(1), 1.