Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis

Practical Assessment, Research and Evaluation

Volumn 15, Issue 7, 2010, Pages

  Bowers, Alex J ^a  

^a UNIVERSITY OF TEXAS AT SAN ANTONIO   (United States)

Author keywords

[No Author keywords available]

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EID: 79960794317     PISSN: None     EISSN: 15317714     Source Type: Journal    
DOI: None     Document Type: Article

References (71)

1. Alexander, K. L., Entwisle, D. R., & Kabbani, N. S. (2001). The dropout process in life course perspective: Early risk factors at home and school. The Teachers College Record, 103(5), 760-822.
2. Allensworth, E. M. (2005). Graduation and dropout trends in Chicago: A look at cohorts of students from 1991 through 2004. Retrieved July 7, 2006, from www.consortium-chicago.org/publications/p75.html
3. Allensworth, E. M., & Easton, J. Q. (2005). The on-track indicator as a predictor of High School graduation: Consortium on Chicago School Research at the University of Chicago.
4. Allensworth, E. M., & Easton, J. Q. (2007). What matters for staying on-track and graduating in Chicago public high schools: A close look at course grades, failures, and attendance in the freshman year. Chicago: Consortium on Chicago School Research.
5. Anderberg, M. R. (1973). Cluster analysis for applications. New York: Academic Press.
6. Balfanz, R., Herzog, L., & MacIver, D. J. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Identification and effective interventions. Educational Psychologist, 42(4), 223-235.
7. Bernhardt, V. (2004). Data analysis for continuous school improvement. Larchmont: Eye on Education.
8. Bowers, A. J. (2007). Grades and data driven decision making: Issues of variance and student patterns. Michigan State University, East Lansing.
9. Bowers, A. J. (2008). Promoting Excellence: Good to great, NYC's district 2, and the case of a high performing school district. Leadership and Policy in Schools, 7(2), 154-177.
10. Bowers, A. J. (2009). Reconsidering grades as data for decision making: More than just academic knowledge. Journal of Educational Administration, 47(5), 609-629.
11. Bowers, A. J. (2010). Grades and Graduation: A Longitudinal Risk Perspective to Identify Student Dropouts. Journal of Educational Research, 103(3), 191-207.
12. Brookhart, S. M. (1991). Grading practices and validity. Educational Measurement: Issues and Practice, 10(1), 35-36.
13. Carr, J., & Farr, B. (2000). Taking steps toward standards-based report cards. In E. Trumbull & B. Farr (Eds.), Grading and reporting student progress in an age of standards (pp. 185-208). Norwood: Christopher-Gordon Publishers.
14. Cizek, G. J. (2000). Pockets of resistance in the assessment revolution. Educational Measurement: Issues and Practice, 19(2), 16-33.
15. Cizek, G. J., Fitzgerald, S. M., & Rachor, R. E. (1995-1996). Teachers' assessment practices: Preparation, isolation and the kitchen sink. Educational Assessment, 3(2), 159-179.
16. Cleator, S., & Ashworth, A. (2004). Molecular profiling of breast cancer: Clincial implications. British Journal of Cancer, 90, 1120-1124.
17. Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17(1), 1-25.
18. Copland, M. A., Knapp, M. S., & Swinnerton, J. A. (2009). Principal leadership, data, and school improvement. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook of data-based decision making in education (pp. 153-172). New York, NY: Routledge.
19. Creighton, T. B. (2001a). Data analysis and the principalship. Principal Leadership, 1(9), 52-57.
20. Creighton, T. B. (2001b). Schools and data: The educator's guide for using data to improve decision making. Thousand Oaks: Corwin Press.
21. Cross, L. H., & Frary, R. B. (1999). Hodgepodge grading: Endorsed by students and teachers alike. Applied Measurement in Education, 12(1), 53-72.
22. D'haeseleer, P. (2005). How does gene expression clustering work? Nature Biotechnology, 23, 1499-1501.
23. DeHoon, M. J. L., Imoto, S., Nolan, J., & Miyano, S. (2004). Open source clustering software. Bioinformatics, 20(9), 1453-1454.
24. Dolled-Filhart, M., Ryden, L., Cregger, M., Jirstrom, K., Harigopal, M., Camp, R. L., et al. (2006). Classification of breast cancer using genetic algorithms and tissue microarrays. Clinical Cancer Research, 12, 6458-6459.
25. Ein-Dor, L., Zuk, O., & Domany, E. (2006). Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proceedings of the National Academy of Sciences, 103(15), 5923-5928.
26. Eisen, M. B., & DeHoon, M. (2002). Cluster 3.0 Manual. Palo Alto, CA: Stanford University.
27. Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, 95, 14863-14868.
28. Farr, B. P. (2000). Grading practices: An overview of the issues. In E. Trumbull & B. Farr (Eds.), Grading and reporting student progress in an age of standards (pp. 1-22). Norwood: Christopher-Gordon Publishers.
29. Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59(2), 117-142.
30. Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972-976.
31. Gleason, P., & Dynarski, M. (2002). Do we know whom to serve? Issues in using risk factors to identify dropouts. Journal of Education for Students Placed at Risk, 7(1), 25-41.
32. Guskey, T. R. (2007). Multiple sources of evidence: An analysis of stakeholder perceptions of various indicators of student learning. Educational Measurement: Issues and Practice, 26(1), 19-27.
33. Halverson, R. R., Grigg, J., Prichett, R., & Thomas, C. (2007). The new instructional leadership: Creating data-driven instructional systems in school. Journal of School Leadership, 17(2), 159-194.
34. Hamilton, L., Halverson, R. R., Jackson, S., Mandinach, E., Supovitz, J. A., & Wayman, J. C. (2009). Using student achievement data to support instructional decision making (No. NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
35. Hammond, C., Linton, D., Smink, J., & Drew, S. (2007). Dropout risk factors and exemplary programs: A technical report. Clemson, S.C.: National Dropout Prevention Center.
36. Hargis, C. H. (1990). Grades and grading practices: Obstacles to improving education and helping at-risk students. Springfield: Charles C. Thomas.
37. Honig, M. I., & Coburn, C. E. (2008). Evidence-based decision making in school district central offices. Educational Policy, 22(4), 578-608.
38. Hubert, L. J., Köhn, H.-F., & Steinley, D. L. (2009). Cluster analysis: A toolbox for MATLAB. In R. E. Millsap & A. Maydeu-Olivares (Eds.), The SAGE handbook of quantitative methods in psychology (pp. 444-512). Thousand Oaks, CA: SAGE Publications Inc.
39. Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the "data-driven" mantra: Different conceptions of data-driven decision making. In P. A. Moss (Ed.), Evidence and decision making: The 106th yearbook of the National Society for the Study of Education, Part 1 (pp. 105-131). Malden, Mass: Blackwell Publishing.
40. Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Englewood Cliffs, NJ: Prentice Hall.
41. Janosz, M., LeBlanc, M., Boulerice, B., & Tremblay, R. E. (2000). Predicting different types of school dropouts: A typological approach with two longitudinal samples. Journal of Educational Psychology, 92(1), 171-190.
42. Jimerson, S. R., Egeland, B., Sroufe, L. A., & Carlson, B. (2000). A prospective longitudinal study of high school dropouts examining multiple predictors across development. Journal of School Psychology, 38(6), 525-549.
43. Kallioniemi, A. (2002). Molecular signatures of breast cancer -Predicting the future. New England Journal of Medicine, 347(25), 2067-2068.
44. Kirschenbaum, H., Napier, R., & Simon, S. B. (1971). Wad-ja-get? The grading game in American education. New York City: Hart Publishing Company.
45. Kohonen, T. (1997). Self-organizing maps. New York: Springer.
46. Konold, T. R., & Kauffman, J. M. (2009). The No Child Left Behind Act: Making decisions without data or other reality checks. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook of data-based decision making in education (pp. 72-86). New York, NY: Routledge.
47. Lekholm, A. K., & Cliffordson, C. (2008). Discrepancies between school grades and test scores at individual and school level: effects of gender and family background. Educational Research and Evaluation, 14(2), 181-199.
48. Lorr, M. (1983). Cluster analysis for social scientists: Techniques for analyzing and simplifying complex blocks of data. San Francisco: Jossey-Bass, Inc.
49. Lu, J., Getz, G., Miska, E. A., Alvarez-Saaverda, E., Lamb, J., Peck, D., et al. (2005). Micro-RNA expression profiles classify human cancers. Nature, 435(9), 834-838.
50. March, J. G. (1997). Understanding how decisions happen in organizations. In Z. Shapira (Ed.), Organizational decision making (pp. 9-32). Cambridge: Cambridge University Press.
51. McMillan, J. H. (2001). Secondary teachers' classroom assessment and grading practices. Educational Measurement: Issues and Practice, 20(1), 20-32.
52. NCES. (2006). Common Core of Data. Retrieved December 18, 2006, from http://nces.ed.gov/ccd/
53. O'Connell, M., & Sheikh, H. (2009). Non-cognitive abilities and early school dropout: Longitudinal evidence from NELS. Educational Studies, 35(4), 475-479.
54. Park, V., & Datnow, A. (2009). Co-constructing distributed leadership: District and school connections in data-driven decision-making. School Leadership and Management, 29(5), 477-494.
55. Quackenbush, J. (2006). Microarray analysis and tumor classification. The New England Journal of Medicine, 354(23), 2463-2475.
56. Raths, J., Kotch, S. A., & Carrino-Gorowara, C. (2009). Research on teachers using data to make decisions. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook of data-based decision making in education (pp. 207-221). New York, NY: Routledge.
57. Rencher, A. C. (2002). Methods in multivariate analysis (2nd ed.). Hoboken: John Wiley & Sons, Inc.
58. Romesburg, H. C. (1984). Cluster analysis for researchers. Belmont, CA: Lifetime Learning Publications.
59. Rumberger, R. W. (1995). Dropping out of middle school: A multilevel analysis of students and schools. American Educational Research Journal, 32(3), 583-625.
60. Rumberger, R. W., & Palardy, G. J. (2005). Test scores, dropout rates, and transfer rates as alternative indicators of high school performance. American Educational Research Journal, 42(1), 3-42.
61. Shen, R., Ghosh, D., Chinnaiyan, A., & Meng, Z. (2006). Eigengene-based linear discriminant model for tumor classification using gene expression microarray data. Bioinformatics, 22(21), 2635-2642.
62. Shepard, L. A. (2006). Classroom assessment. In R. L. Brennan (Ed.), Educational measurement (Fourth ed., pp. 623-646). Westport, CT: Praeger Publishers.
63. Sireci, S. G., Robin, F., & Patelis, T. (1999). Using cluster analysis to facilitate standard setting. Applied Measurement in Education.
64. Sneath, P. H. A., & Sokal, R. R. (1973). Numerical taxonomy: The principles and practice of numerical classification San Francisco: W.H. Freeman.
65. Van'tveer, L. J., Dai, H., vandeVijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., et al. (2002). Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer. Nature, 415, 530-536.
66. Vandevijver, M. J., He, Y. D., van'tVeer, L. J., Dai, H., Hart, A. A. M., Voskuil, D. W., et al. (2002). A gene-expression signature as a predictor of survival in breast cancer. The New England Journal of Medicine, 347(25), 1999-2009.
67. Vilo, J. (2003). Expression profiler - EPCLUST. 2005, from http://ep.ebi.ac.uk/EP/EPCLUST/
68. Wayman, J. C., Cho, V., & Johnston, M. T. (2007). The data-informed district: A district-wide evaluation of data use in the Natrona county school district. Austin: The University of Texas at Austin.
69. Wayman, J. C., & Stringfield, S. (2006a). Data use for school improvement: School practices and research perspectives. American Journal of Education, 112(4), 463-468.
70. Wayman, J. C., & Stringfield, S. (2006b). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112(4), 549-571.
71. Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, K. W., et al. (1997). An information-intensive approach to the molecular pharmacology of cancer. Science, 275(5298), 343-349.