Data-driven instruction: Difference between revisions

Revision as of 00:13, 4 March 2020

Revision as of 15:38, 26 April 2020

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== Criticisms ==

A major criticism of data driven instruction is that it focuses too much on test scores, and that not enough attention is given to the results of classroom assessments. Data driven instruction should serve as a “road map through assessment” that helps “teachers plan instruction to meet students’ needs, leading to better achievement”.<ref>Neuman, S. (2016). Code Red: The Danger of Data-Driven Instruction. ''Educational Leadership'', 74(3), pps. 24-29</ref> Summative assessments should not be used to inform the day-to-day teaching and learning that is supported by data-driven instruction. Additional problems associated with perceptions of data driven instruction include the limitations of quantitative data to represent student learning, not considering the social and emotional needs or the context of the data when making instructional decisions, and a hyperfocus on the core areas of literacy and mathematics while ignoring the encore, traditionally high-interest areas such as the arts and humanities.

== Citations ==

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== Additional References ==

* Black, P. & Wiliam, D. (1998). Inside the Black Box: Raising Standards Through Classroom Assessment. ''Phi Delta Kappan'', 80(2), pp.&nbsp;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.

Boudett* Dana, KN. PF., City,& E. A., MurnameYendol-Hoppey, R. JD. (20132014). DataThe Wise:Reflective A Step-by-StepEducator’s Guide to UsingClassroom AssessmentResearch: ResultsLearning to ImproveTeach and Teaching andto Learn LearningThrough Practitioner Inquiry (3rd Ed.). CambridgeThousand Oaks, MACA: Harvard Education PressCorwin.

* Datnow, A., & Park, V. (2014). Data-Driven Leadership. San Francisco, CA: Jossey-Bass.

* 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.

Dana, N. F. & Yendol-Hoppey, D. (2014). The Reflective Educator’s Guide to Classroom Research: Learning to Teach and Teaching to Learn Through Practitioner Inquiry (3rd Ed.). Thousand Oaks, CA: Corwin.

* 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.&nbsp;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

Datnow, A., & Park, V. (2014). Data-Driven Leadership. San Francisco, CA: Jossey-Bass.

* 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—Delaware. Retrieved from http://search.proquest.com/docview/250914319/

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

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.

* Halverson, R., Grigg, J., Prichett, R., & Thomas, C. (2007). The New Instructional Leadership: Creating Data-Driven Instructional Systems in School. Journal of School Leadership, 17(March), 159–194.

* Hamilton et al. - 2009 - Using student achievement data to support instruct.pdf. (n.d.). Retrieved from http://files.eric.ed.gov/fulltext/ED506645.pdf

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.&nbsp;55–63). ACM Press. {{doi|10.1145/2930238.2930255}}

* Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., Wayman, J. C., ... Steele, J. L. (2009). Using student achievement data to support instructional decision making. Retrieved from http://repository.upenn.edu/gse_pubs/279/

* Hattie, J. (2012). Visible Learning for Teachers: Maximizing Impact on Learning. New York: Routledge.

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

* Heppen, J., Faria, A.-M., Thomsen, K., Sawyer, K., Townsend, M., Kutner, M., ... Casserly, M. (2010). Using Data to Improve Instruction in the Great City Schools: Key Dimensions of Practice. Urban Data Study. Council of the Great City Schools. Retrieved from http://eric.ed.gov/?id=ED536737

* Johnson, L. (2009). Randi Weingarten, President Antonia Cortese, Secretary-Treasurer. Retrieved from http://eric.ed.gov/?id=ED511575

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—Delaware. Retrieved from http://search.proquest.com/docview/250914319/

Zavadsky,* H., & DolejsJones, A. (20062005). DATA: NotThe JustMyths Anotherof FourData-LetterDriven WordSchools. Principal Leadership, Middle Level Ed., 76(2), 32–3637–39.

* Kennedy, B. L., & Datnow, A. (2011). Student Involvement and Data-Driven Decision Making Developing a New Typology. Youth & Society, 43(4), 1246–1271. {{doi|10.1177/0044118X10388219}}

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

* Larocque, M. (2007). Closing the Achievement Gap: The Experience of a Middle School. Clearing House, 80(4), 157–162.

* Melucci, L. (2013, August). TEACHER PERCEPTIONS AND USE OF DATA-DRIVEN INSTRUCTION.pdf. Capella University.

Halverson, R., Grigg, J., Prichett, R., & Thomas, C. (2007). The New Instructional Leadership: Creating Data-Driven Instructional Systems in School. Journal of School Leadership, 17(March), 159–194.

* 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—California. Retrieved from http://search.proquest.com/docview/1432373944/

Hamilton et al. - 2009 - Using student achievement data to support instruct.pdf. (n.d.). Retrieved from http://files.eric.ed.gov/fulltext/ED506645.pdf

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

* Pella, S. (2012). What Should Count as Data for Data-Driven Instruction? Toward Contextualized Data-Inquiry Models for Teacher Education and Professional Development. Middle Grades Research Journal, 7(1), 57–75.

Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., Wayman, J. C., ... Steele, J. L. (2009). Using student achievement data to support instructional decision making. Retrieved from http://repository.upenn.edu/gse_pubs/279/

* Rogers, L. N., & Tyndall, P. D. (2001). Teachers’ Perspectives: Developing Instructional Leadership through Classroom Inquiry. Retrieved from http://eric.ed.gov/?id=ED465596

* Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. (2015). The Role of Schooling in Perpetuating Educational Inequality An International Perspective. Educational Researcher, 44(7), 371–386.

Hattie, J. (2012). Visible Learning for Teachers: Maximizing Impact on Learning. New York: Routledge.

* Schmoker, M. (1996). Results: the key to continuous school improvement. Alexandria, VA: Association for Supervision and Curriculum Development.

Heppen* Shanahan, JT., FariaCallison, A.-MK., ThomsenCarriere, KC., SawyerDuke, N. K., TownsendPearson, MP. D., KutnerSchatschneider, MC., ...& CasserlyTorgesen, MJ. (2010). UsingImproving DataReading to Improve InstructionComprehension in theKindergarten Greatthrough City3rd SchoolsGrade: KeyIES DimensionsPractice of PracticeGuide. UrbanNCEE Data Study2010-4038. Council of the GreatWhat CityWorks SchoolsClearinghouse. Retrieved from http://eric.ed.gov/?id=ED536737ED512029

* 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 4–7, 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.

Johnson, L. (2009). Randi Weingarten, President Antonia Cortese, Secretary-Treasurer. Retrieved from http://eric.ed.gov/?id=ED511575

* Wiliam, D. (2011). Embedded Formative Assessment. Bloomington, IN: Solution Tree.

Jones* Zavadsky, H., & Dolejs, A. (20052006). TheDATA: Not MythsJust ofAnother DataFour-DrivenLetter SchoolsWord. Principal Leadership, Middle Level Ed., 67(2), 37–3932–36.

Kennedy, B. L., & Datnow, A. (2011). Student Involvement and Data-Driven Decision Making Developing a New Typology. Youth & Society, 43(4), 1246–1271. {{doi|10.1177/0044118X10388219}}

Larocque, M. (2007). Closing the Achievement Gap: The Experience of a Middle School. Clearing House, 80(4), 157–162.

Melucci, L. (2013, August). TEACHER PERCEPTIONS AND USE OF DATA-DRIVEN INSTRUCTION.pdf. Capella University.

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—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.

Pella, S. (2012). What Should Count as Data for Data-Driven Instruction? Toward Contextualized Data-Inquiry Models for Teacher Education and Professional Development. Middle Grades Research Journal, 7(1), 57–75.

Rogers, L. N., & Tyndall, P. D. (2001). Teachers’ Perspectives: Developing Instructional Leadership through Classroom Inquiry. Retrieved from http://eric.ed.gov/?id=ED465596

Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. (2015). The Role of Schooling in Perpetuating Educational Inequality An International Perspective. Educational Researcher, 44(7), 371–386.

Schmoker, M. (1996). Results: the key to continuous school improvement. Alexandria, VA: Association for Supervision and Curriculum Development.

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 4–7, 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.

Wiliam, D. (2011). Embedded Formative Assessment. Bloomington, IN: Solution Tree.

Zavadsky, H., & Dolejs, A. (2006). DATA: Not Just Another Four-Letter Word. Principal Leadership, Middle Level Ed., 7(2), 32–36.

[[Category:Education terminology]]