Instructional Leaders and Understanding Data: The Status and Prevalence of Research and Statistics Courses in a Midwestern State’s Educator Preparation Programs

Dr. Erasmus Chirume^1,

¹Central State University, 1400 Brush Row Road, Wilberforce, OH 45384

Cite this paper:
Dr. Erasmus Chirume. Instructional Leaders and Understanding Data: The Status and Prevalence of Research and Statistics Courses in a Midwestern State’s Educator Preparation Programs. American Journal of Educational Research. 2018; 6(7):997-1004. doi: 10.12691/education-6-7-16

Abstract

The study investigated the extent to which courses in statistics and research (other than in STEM majors) were prevalent in the educator preparation programs (EPPs) for preservice teachers. A chi-square goodness of fit test, which compared the counts of these courses objectively identified through a content analysis of the EPPs’ course descriptions, was conducted. The chi-square (2) p = .003, (p < .05) reflects that the hypothesis that courses in statistics and in research were as equally prevalent as assessment courses was rejected. For the decisions of instructors to be robust, they should be based on multi-sourced data.

Keywords:
accreditation assessment critical theory teacher educator preparation school/teacher effectiveness

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Thomas, K., & Huffman, D. (2016). Navigating the challenges of helping teachers use data to inform educational decisions. Administrative Issues Journal, 1(2), 94-102.
[2]	Cook, L. (2015). U.S. education: Still separate and unequal. US News & World Report. Retrieved from http://www.usnews.com/news.
[3]	Noguera, P. (2015). Race, equity, and education: Sixty years from brown. New York, NY: ASCD.
[4]	U.S. Department of Education, & National Center for Education Statistics. (2017). The condition of education 2017 (NCES 2017-144). Status dropout rates. Washington, D.C.: NCES.
[5]	Mandinach, E. B., & Gummer, E. S. (2016a). Data and educator preparation programs: Data for programmatic continuous improvement and data literacy for teachers. Keynote Presentation at Council for Accreditation of Educator Preparation Conference. Washington Hilton Hotel, Washington, D.C.
[6]	Allen, M., Coble, C., & Crowe, E. (2014). Building an evidence-based system for teacher preparation. Washington, DC: Teacher Preparation Analytics.
[7]	Council of Chief State School Officers (CCSSO). (2016). The 2016 legislative conference workbook. Washington, DC: CCSSO.
[8]	Wilson, F., & Ferrini-Mundy, J. (2001). Teacher preparation research: Current knowledge, gaps and recommendations. Washington, DC: Center for the Study of Teaching and Policy.
[9]	Shepperson, T. L. (2013). Prevalence of evaluation method courses in education leader doctoral preparation. International Journal of Educational Leadership Preparation, 8(1), 1-14.
[10]	Council for Accreditation of Teacher Education (CAEP). (2016). CAEP accreditation handbook. Washington, DC: CAEP.
[11]	Darling-Hammond, L., Meyerson, D., LaPointe, M., & Orr, M. T. (2007). Preparing principals for a changing world: Lessons from effective school leadership programs. Palo Alto, CA: Stanford Educational Leadership Institute.
[12]	Lauer, P. A. (2006). What principals need to know about education research. Principal, 85(5), 12-17.
[13]	Pont, B., Nusche, D., & Moorman, H. (2008). Improving school leadership: Policy and practice (Vol. 1). Paris: OECD.
[14]	Kizlik, B. (2017). Measurement, assessment, and evaluation in education. Retrieved from http://www.adprima.com/measurement.htm.
[15]	Garfield, J., & Ben-Zvi, D. (2008). Developing students’ statistical reasoning: Connecting research and teaching practice. Berlin: Springer.
[16]	Garfield, J. (2011). Statistical literacy, reasoning, and thinking. In M. Lovric (Ed.), International encyclopedia of statistical science (pp. 1439-1442). Berlin: Springer.
[17]	Dyer, K. (2014). Data literacy – what it is and how it differs from assessment literacy. Retrieved from https://www.nwea.org/.
[18]	Roth, W.-M. (2014). On understanding variability in data: A study of graph interpretation in an advanced experimental biology laboratory. Educational Studies in Mathematics, 86(3), 359-376.
[19]	Mandinach, E. B., & Gummer, E. S. (2016b). Data literacy for educators: Making it count in teacher preparation and practice. New York, NY: Teachers College Press.
[20]	Doerr, H. M. (2000). How can i find a pattern in this random data? The convergence of multiplicative and probabilistic reasoning. Journal of Mathematical Behavior, 18(4), 431-454.
[21]	King, G. (2011). Ensuring the data-rich future of the social sciences. Science, 331(6018), 719-721.
[22]	Deans for Impact. (2016). From chaos to coherence: A policy agenda for accessing and using outcomes data in educator preparation. Austin, TX: Deans for Impact.
[23]	Datnow, A., & Hubbard, L. (2016). Teacher capacity for and beliefs about data-driven decision making: A literature review of international research. Journal of Educational Change, 17(1), 7-28.
[24]	Bryk, A. S. (2015). Learning to improve how America's schools can get better at getting better. Cambridge, MA: Harvard Education Press.
[25]	Marsh, J. A., Kerr, K. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: Actions, outcomes, and lessons from three urban districts. American Journal of Education, 112(4), 496-520.
[26]	Supovitz, J. A., & Klein, V. (2003). Mapping a course for improved student learning: How innovative schools systematically use student performance data to guide improvement. Philadelphia, PA: The Consortium for Policy Research in Education.
[27]	Bastian, K. C., Henry, G. T., Pan, Y., & Lys, D. (2016). Teacher candidate performance assessments: Local scoring and implications for teacher preparation program improvement. Teaching and Teacher Education, 59(Supplement C), 1-12.
[28]	Bustamante, R. M., & Combs, J. P. (2011). Research courses in educational leadership programs: Relevance in an era of accountability. International Journal of Education Policy and Leadership, 6(3), 1-11.
[29]	Huck, S. W. (2008). Reading statistics and research. Boston, MA: Pearson Education Inc.
[30]	Shepperson, T., & Fierro, L. A. (2010). Teaching evaluation: Cross-disciplinary research on graduate training in public health and educational administration. In proceedings of the International Conference on the Social Sciences, 2010. Honolulu, Hawaii.
[31]	Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools–teacher access, supports and use. Menlo Park, CA: SRI International.
[32]	Friel, S.N., Frances R. Curcio, R., Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education. 32 (2) pp. 124-158.
[33]	Mann, S. (2010). A critical review of qualitative interviews in applied linguistics. Applied Linguistics, 32(1), 6-24.
[34]	Shannon, S. E., & Hsieh, H. (2005). Three approaches to qualitative content analysis. Retrieved from www.researchdesignreview.com.
[35]	Potter, W. J., & Levine‐Donnerstein, D. (1999). Rethinking validity and reliability in content analysis. Journal of Applied Communication Research, 27(3), 258-284.
[36]	Roller, M. R. (2016). Social constructionism & qualitative research design. Retrieved from http://www.rollerresearch.com/MRR.
[37]	Krippendorff, K. (2013). Content analysis: An introduction to its methodology (3rd ed.). Thousand Oaks, CA: Sage
[38]	Roller, M. R. & Lavrakas P. J. (2015). Applied Qualitative Research Design: A Total Quality Framework Approach. New York, NY: The Guilford Press
[39]	Kangai, C. (2009). Social research methods in higher education: A critical analysis of methodological issues and emerging trends at the Zimbabwe open university. Retrieved from http://cdn.intechopen.com/.
[40]	Mueller, D. J. (1986). Measuring social attitudes: A handbook for researchers and practitioners. New York: Teachers’ College Press.
[41]	Best, J. B., & Kahn, J. V. (2006). Research in education (10th Ed.). Boston, MA: Pearson Education Inc.
[42]	Gall, M. D., Gall, J. P., & Borg, W. R. (2003). Educational research: An introduction. Boston, MA: Pearson Education Inc.
[43]	Brase, C. H.and Brase, C. P. (2012). Understandable Statistics: Concepts and Methods, (10th Ed.). Boston, MA: Cengage Learning
[44]	Scheaffer, R. L. (2011). Statistics education. In M. Lovric (Ed.), International encyclopedia of statistical science (pp. 1482-1484). Berlin: Springer.
[45]	Chepko, S. (2016). Teacher-prep accreditation group seeks to regain traction. Retrieved from https://www.edweek.org/ew/articles.
[46]	English, L. D. (2012). Data modelling with first-grade students. Educational Studies in Mathematics, 81(1), 15-30.
[47]	Peck, C. A. & McDonald, M. A. (2014). What is a culture of evidence? How do you get one? … Should you want one? Teachers College Record 116 (3)
[48]	Mertler, C. A. (2014). The data-driven classroom: How do i use student data to improve my instruction? Retrieved from www.ascd.org
[49]	Dorow, P. F., Dávila, G., Varvakis, G., & Vallejos, R. V. (2015). Generation of ideas, ideation and idea management. NAVUS-Revista de Gestão e Tecnologia, 5(2), 51-59.
[50]	Jones, R. C. (2013). The instructor’s challenge: Moving students beyond opinions to critical thinking. Madison, WI: Faculty Focus, Magna Publications.
[51]	Sandercook, I. (2015). Strategies for providing effective and efficient instructor feedback. Retrieved from https://teachonline.asu.edu/author/irma/.
[52]	Alwehaibi, H. (2012). Novel program to promote critical thinking among higher education students: Empirical study from Saudi Arabia. Asian Social Science, 8(11), 193-204.
[53]	Carmichael, E., & Farrell, H. (2012). Evaluation of the effectiveness of online resources in developing student critical thinking: Review of literature and case study of a critical thinking online site. Journal of University Teaching and Learning Practice, 9(1), 1-17.
[54]	Garfield, J., & Ben‐Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372-396.
[55]	Dynarski, M. (2015). Using research to improve education under the Every Student Succeeds Act. Retrieved from https://www.brookings.edu.