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THE UNIVERSITY OF MONTANA

PHYLLIS J. WASHINGTON

COLLEGE OF EDUCATION AND HUMAN SCIENCES

DEPARTMENT OF EDUCATIONAL LEADERSHIP

EDLD 519

Measurement and Analysis of Educational Data

July 2- July 20, 2012

3:00 – 6:00 PM

(Please turn off cell phones while in class.)

Class Location: Room 322

Instructor: Patty Kero, Ed.D. Office Room: 206

Office Phone Number: 243-5623 patty.kero@mso.umt.edu

Office Hours: By appointment

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Measurement and Analysis of Educational Data Course Purpose The purpose of this course is to present the understanding of measurement and analysis theory necessary to ensure that the student of educational leadership is capable of making measurements consistent with the nature of educational data, submitting these data to appropriate analysis, and drawing constructive conclusions from the analysis. Course Objectives To help the student:

1. understand measurement and analysis concepts and terminology, 2. become a critical reader of research, 3. grasp the significance and importance of course work in research methods, 4. grasp the significance and implications of measurement and analysis of educational data in the

process of improving schools, 5. use computer technology in numerous components of research, 6. view research as a means to integrate curricula, 7. critically evaluate educational data, 8. critique and utilize research methods, 9. understand the relevance of research to practice, 10. interpret and analyze assessment data, 11. present data-driven improvement plans in a manner easily understandable by stakeholders, 12. utilize research as a means to build a personal knowledge base, and 13. utilize research to contribute to an appropriate knowledge base.

Instructional Methods Instructional methods will utilize “hands on” as the primary means of learning. This course will provide a number of teaching and learning formats to promote individual personal and professional growth, and to advance group interactions with all participants. Formats will include large class presentations, small class process groups, reflective and synthesis writing, problem-solving case study activities, cooperative learning activities, structured book discussions, individual/group research, guest speakers, lectures, and independent reading. Computer technology will be used in the class. Evaluation Criteria and Course Requirements

REQUIRED ASSIGNMENTS # Unit Value

Total Points

Percent of Total

Your Score

Class attendance, participation, and quizzes 14 3 42 42 % DDIIP and presentation 1 30 30% Research Review Article 1 20 20% Exams: Mid-term and Final Statistics Tests 2 5 5% Final Exam: Concept Map 1 2 2% Random act of kindness 1 1 1% Field Experience(not graded but required) Total 100 100 %

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Missing assignments will result in a grade of Incomplete (I). A late assignment will receive a reduction in points. Course Required Assignments

1. Daily attendance and participation: Attendance records start with the first day of class. One unexcused absence results in your final course grade being lowered ½ letter grade. Two unexcused absences result in your final course grade being lowered one full letter grade, etc.

Because of the intensive schedule of the class, student attendance at all class meetings is essential. You will be expected to attend all classes, interact verbally, and develop discussions beyond the level of the text and/or presentations. The value of class attendance and participation is immeasurable. Please make arrangements with another student to take notes for you if you must miss class.

Discussion Rubric: Excellent (3 points) Good (2 points) Fair (1 point) Poor (0 points) Student actively participates in class by asking questions, and participates in discussions in a thorough, meaningful way that stimulates rich conversation.

Student actively participates in class by asking questions, and participates in discussions in a thorough way that stimulates conversation.

Student rarely participates in class by asking questions, and participates in discussions in a cursory manner.

Student never participates in class by asking questions, or participates in discussions.

Cites one of the textbooks and an outside reading

Cites one of the textbooks Cites one of the textbooks Cites no textbooks

2. Daily quiz: Complete quiz during the first few minutes of class. It is used to get the mind thinking about the information covered in the previous class. Some class periods we will complete the quiz during the last few minutes of class. It is used at the conclusion of the class to help students synthesize and evaluate their understanding of the topic at hand. The point is to get you writing immediately about the topic you have learned.

3. Daily assignments: Complete assignments and be prepared for next class. Most often you will

be assigned weekly tasks to complete before the next class, which you will be expected to bring with you to class.

4. Data-Driven Instructional Improvement Plan (DDIIP) (Appendix F, G, and Rubric):

Prepare a five chapter research paper in which you discuss a research problem, data collection, null hypothesis, and statistical procedures as covered in class. You will then generate dummy data, run a statistical analysis of the data, report the findings, make a decision regarding your hypothesis, and formulate your conclusion. Examples of exemplary assignments will be presented in class. Assignments are to reflect very high quality of thought and content. Written assignments must be presented in APA format. This assignment must be turned in to the professor on the date that has been assigned. All work must be completed by the deadlines indicated. Reduction of credit will be given to assignments submitted after due dates. (Please see Appendix F and G.) DO NOT START THIS ASSIGNMENT UNTIL WE COMPLETE ONE IN CLASS.

5. Brief presentation: Prepare a brief presentation regarding your five chapter DDIIP.

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6. Research review: Find and critically review a published quantitative research article/paper that

should have never been published and prepare a written report that specifically identifies statistical, methodological, logical, and/or other research errors. If possible discuss barriers to more effective measurement and analysis of educational data. Examples of exemplary assignments will be presented in class when we discuss how to lie with statistics and the worst statistics. Assignments are to reflect very high quality of thought and content. Written assignments must be presented in APA format. This assignment must be turned in to the professor on the date that has been assigned. (Please see Appendix E.)

7. Random Act of Kindness: Do a random act of kindness for someone you do not know. A

summary paper, one page in length, will be completed and turned in. Include in the paper a summary of what you did, why you did it, and your reactions after the kindness was rendered.

a. No act of kindness, no matter how small, is ever wasted. ~Aesop

b. About the book titled, Aging with Grace: What the Nun Study Teaches Us About Leading Longer, Healthier, and More Meaningful Lives by David Snowdon, Ph.D:

“There are lessons for all of us in this moving account of the School Sisters of Notre Dame and their commitment to helping us find the causes of Alzheimer’s disease. I came away with a new respect for the power of faith as well as the beauty and complexity of the human brain.” Virginia Bell, M.S.W.

8. Mid-term and Final Exams: The mid-term examination will be given during the midway

point of the course. Examinations are required to be taken in class. There will be NO make-up exams for any reason. All exams must be taken when scheduled. Any student with a 60% or lower score must make corrections and return to Dr. Kero within one week. The student must also schedule an appointment to discuss the course with Dr. Kero. The final examination will be on the last day of class. Examinations are required to be taken in class. There will be NO make-up exams for any reason. There may be a take-home section handed out a week or two before the last class. Your graded final will be available in the EDLD office (Room 213) the following semester.

9. Final Exams: Concept Map

The final exam will be taken on the last day of class. It may be taken on the computer (using bubbl.us) or on large poster paper (using pens and markers). Students will create a concept map of the course. Concept maps (sometimes called mind maps) are graphical tools for organizing and representing knowledge and understanding. The maps should include statistic concepts, usually enclosed in circles or boxes of some type, and then relationships between the concepts indicated by a connecting line linking two concepts. (As an in-class project, we will conduct a preliminary test during the first or second week as cooperative learning experience.) The statistic exam will be comprehensive. More details will be discussed in class.

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10. Fieldwork Experience: The fieldwork project is designed to foster applied learning with best

practices in the field. This project provides you with the opportunity to bring together leadership theory and practice in actual K-12 educational environments. Please review the fieldwork requirements on the EDLD website.

Required Texts and Readings Popham, W.J. (2010). Everything school leaders need to know about assessment. Thousand Oaks, CA: Corwin, A Sage Company. Salkind, N.J. (2013). Statistics for People Who (Think They) Hate Statistics. Excel 2010 Edition. Thousand Oaks, CA: Corwin, A Sage Company Readings (Optional) Boudett, K.P., City, E.A., and Murnane, R.J., (2005). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Education Press. Picciano, A.G. (2006). Data-Driven Decision Making for Effective School Leadership. Upper Saddle River, NJ: Pearson. Additional Assigned Readings May Include: American Association of School Administrators. (December 2002). The School

Administrator. Data-Driven districts: Applying statistical proof to multiple purposes. American Psychological Association (2009). Publication manual of the American Psychological Association (6th ed.). Washington, D.C.: APA.

Association for Supervision and Curriculum Development. (Dec. 2008/Jan. 2009). Educational Leadership. Data: Now what? Bernhardt, V. L., (1998). Data analysis for comprehensive schoolwide improvement. Larchmont, NY: Eye On Education. Blink, R. (2007). Data-driven instructional leadership. Larchmont, NY: Eye On Education. Picciano, A. G. (2006). Data-driven decision making for effective school leadership. Upper Saddle River, NJ: Pearson Education, Inc. Masters of Education Culmininating Portfolio The Masters of Education degree in Educational Leadership requires a culminating portfolio. As part of this portfolio, students will submit a benchmark assignment from each of the required M.Ed. courses. The benchmark assignments for this course are the Data-Driven Instructional Improvement Plan (DDIIP) and the Analysis of National and State Accountability and Achievement Data.

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Important Notice Students enrolled in this course are expected to demonstrate regular and consistent class participation that promotes a scholarly environment where diverse ideas are tolerated and discussion is supported by informed opinion. Students may work together or independently on assignments. However, all work turned in must be original. Assignments that are duplicates or, in my judgment, clones, will be returned without credit or grade. No work may be plagiarized. If you are quoting another source, you must cite the source. Much of what is to be learned in this class is learned by attending class and participating in discussions. It is important to attend all classes if you are working for an A. Please let me know if you must miss class. All students must practice academic honesty. Academic misconduct is subject to academic penalty by the course instructor and/or disciplinary sanction by the University. Students are required to be familiar with the Student Conduct Code. The Student Conduct Code is available for review online at http://www.umt.edu/SA/VPSA/index.cfm/page/1321 Written assignments will reflect the individual’s original work and, when appropriate, follow the style articulated in the Publication Manual of the American Psychological Association (APA). All references to works by other authors must be properly cited. In addition, students are required to be current in the assigned reading for the course and to submit and/or present required assignments in a timely manner. Late assignments will be accepted only by prior consent of the instructor. Please turn off cell phones. Course Outcomes and Standards for School Leaders The Department of Educational Leadership (EDLD) has adopted the Interstate School Leaders Licensure Consortium (ISLLC) Standards for School Leaders. The ISLLC Standards were developed by the Council of Chief State School Officers and member states in 1996 and later revised in the spring of 2008. The ISLLC Standards are used to guide courses in educational leadership. The six standards are either directly or indirectly addressed in this course. For a detailed explanation of the ISLLC Standards, visit the web site for the Council of Chief State School Officers at http://www.ccsso.org/content/pdfs/elps_isllc2008.pdf. (Please see Appendix C.) Useful Websites: American Educational Research Association www.aera.net Buros Institute of Mental Measurements http://www.unl.edu/buros/ Census Data Results for 2010 http://2010.census.gov/2010census/ Education Research Reports http://www.ed.gov/pubs/OR/ResearchRpts/ ERIC Clearinghouse on Assessment and Evaluation http://ericae.net/ Journal of Educational Policy Analysis http://olam.ed.asu.edu/epaa/ Montana Office of Public Instruction http://opi.mt.gov National Center for Education Statistics http://nces.ed.gov/ No Child Left Behind www.nochildleftbehind.org No Child Left Behind Research Standards http://www.ed.gov/about/offices/list/ies/news.html#guide

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Tentative Class Topics and Assignment Due Dates

Date Topic Assignment Day 1: July 2

Introduction to Measurement and Analysis Introductions Pre-test Syllabus Discussion Introduction to Measurement Assessment, Measurement, Evaluation Introduction to Educational Data websites: www.census.gov U.S. Census www.opi.state.mt.us Montana OPI www.nces.ed.gov NCES http://www.babynamewizard.com http://opi.mt.gov/ Montana OPI http://www.gapminder.org Stats-worldwide Data Driven Decision Making 1. College and career readiness: Student Performance

Baseline, Resource Alignment, Student Outcomes, Programs and Practices http://www.data-first.org 2. Keeping kids in school: Using Data

Warning Signs Statistics Introduction to Research Definitions

Day 2: July 3

Data Driven Decision Making Introduction to Data-Driven Decision Making Date First Video Guiding questions: “How are we doing?” Compared to Standards, Ourselves, & Others Why Teachers Matter? Statistics Definitions Levels of Measurement

Readings: Picciano: Chapter 1

July 4 No class! Holiday

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Day 3: July 5

Data Driven Decision Making Planning and Developing Information Resources Data First video: The Data Cycle and Growth Models and color coding Data Collection: Who, What, Where, When, Why, and How

• Demographic Data • Student Learning Data • School Process Data • Perceptions Data

Statistics Frequencies Stem and Leaf Box and Plot Charts and Graphs Measures of Central Tendencies

Readings: Picciano: Chapter 2

Day 4: July 6

Data Driven Decision Making Hardware, Software, and People Statistics Measures of Central Tendencies Data First video: Stacked Columns: Measures of Variability Standard Deviation

Readings: Picciano: Chapter 3

Day 5: July 9

Data Driven Decision Making Educational Research Methods and Tools Data First video: Leading and Lagging Indicators Statistics Standard Scores (z-scores) Standard Scores and the Normal Curve Validity: Assessment’s Cornerstone Test Reliability

Readings: Picciano: Chapter 4

Day 6: July 10

Data Driven Decision Making Teachers and Administrators as Researchers Data First video: Scatter plots Statistics Correlation Scattergrams Pearson r Coefficient of Determination Linear Regression

Readings: Picciano: Chapter 5 Due: Research review

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Day 7: July 11

Summer Seminar at the Park This summer seminar will focus on professional learning communities (PLC). We will review the foundations of PLCS, and how as school leaders to create, facilitate, and support the development and continual growth of a PLC. We will read a couple excerpts from current literature discussing what are PLCs and how to support them. At our meeting, we will collaboratively discuss and walk through some possible scenarios in creating PLCS.

Articles Assigned

Day 8: July 12

Data Driven Decision Making School and the Community Statistics Populations and Samples Introduction to Probability

Readings: Picciano: Chapter 6 and7

Day 9: July 13

Data Driven Decision Making Financial Management and Budgeting Data First: Graduation Rates Statistics Probability and the Normal Curve Standard Error of the Mean

Readings: Picciano: Chapter 8

Day 10: July 16

Data Driven Decision Making Supporting Teaching and Learning Statistics Conference Interval for the Mean Null Hypothesis

Readings: Picciano: Chapter 9

Day 11: July 17

Data Driven Decision Making Supporting Teachers and their Professional Development Statistics t-test

Readings: Picciano: Chapter 10

Day 12: July 18

Data Driven Decision Making Review of Stats Internet Statistics t-test

Readings: Picciano: Chapter A and E

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Day 13: July 19

Data Driven Decision Making Review of Stats Internet Presentations Statistics chi-square

Due: Presentations Due: ALL ASSIGNMENTS

Day 14 July 20

Data Driven Decision Making Presentations Statistics Exams: Concept Map and Final

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Suggested Reading

American Psychological Association. (2001). Publication manual of the American

Psychological Association. Washington, DC: Author. Bernhardt, Victoria L., “No Schools Left Behind,” Educational Leadership, Vol. 60, No. 5, 2003,

pp. 26–30.

Black, Paul, and Dylan William, “Assessment and Classroom Learning,” Assessment in Education: Principles, Policy, and Practice, Vol. 5, No.1, 1998, pp. 7–74.

Booher-Jennings, J., “Below the Bubble: ‘Educational Triage’ and the Texas Accountability System,”

American Educational Research Journal, Vol. 42, 2005, pp. 231–268. Boston, C., The Concept of Formative Assessment, ERIC Digest, ED470206, College Park, Md.: ERIC Clearinghouse on Assessment and Evaluation, 2002. Online at http://www.ericdigests.org/2003- 3/concept.htm. Brunner, C., Fasca, C., Heinze, J., Honey, M., Light, D., Mandinach, E., & Wexler, D. (2005).

Linking data and learning: The Grow Network study. Journal of Education for students at Risk, 10(3), 241-267. Retrieved August 22, 2008 from http://cct.edc.org/admin/publications/report/LinkingData_GrowStudy.pdf

Burnett, E. (2007, Fall). Applying an holistic decision-making model to priorities in school reform. Catalyst for Change, 35(1), 30-42. Retrieved August 22, 2008 from

http://vnweb.hwwilsonweb.com/hww/results/external_link_maincontentframe.jhtml?_D ARGS=/hww/results/results_common.jhtml.29

Cawelti, G. (1995). Handbook of research on improving student achievement. Arlington, VA:

Educational Research Service. Creighton, T.B. (2001). Schools and data: The educator’s guide for using data to improve decision

making. Thousand Oaks, CA: Corwin. Celio, M. B., and J. Harvey, Buried Treasure: Developing an Effective Management Guide from

Mountains of Educational Data, Seattle, Wash.: Center on Reinventing Public Education, 2005. Chen, Eva, Margaret Heritage, and John Lee, “Identifying and Monitoring Students’ Learning Needs with Technology,” Journal of Education for Students Placed at Risk, Vol. 10, No. 3, 2005, pp. 309–332. Choppin, Jeffrey, “Data Use in Practice: Examples from the School Level,” paper presented at the Annual Conference of the American Educational Research Association, New Orleans, La., April

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2002. Coburn, C., M. I. Honig, and M. K. Stein, What’s the Evidence on Districts’ Use of Evidence? Chapter

prepared for conference volume, sponsored by the MacArthur Network on Teaching and Learning, 2005.

Copland, Michael, “Leadership of Inquiry: Building and Sustaining Capacity for School Improvement,”

Educational Evaluation and Policy Analysis, Vol. 25, No. 4, 2003, pp. 375–395. Cowan, G. (2007). Understanding and conducting research in education: A user-friendly approach. Dubuque, IA: Kendall/Hunt Publishing. Creighton, T. B. (2007). Schools and data: The educator’s guide for using data to improve

decision making. Thousand Oaks, CA: Corwin Press. Cromey, A., “Using Student Assessment Data: What Can We Learn from Schools?” North Central

Regional Educational Laboratory, Policy Issues Brief No. 6, November 2000. Dembosky, J. W., J. F. Pane, H. Barney, and R. Christina, Data-Driven Decision Making in

Southwestern Pennsylvania School Districts, WR-326- HE/GF, Santa Monica, Calif.: RAND Corporation, 2005. Online at http://www.rand.org/pubs/working_papers/2006/RAND_WR326.sum.pdf.

Deming, W. E., Out of Crisis, Cambridge, Mass.: MIT Center for Advanced Engineering Study, 1986. Detert, J. R., M. E. B. Kopel, J. J. Mauriel, and R. W. Jenni, “Quality Management in U.S. High

Schools: Evidence from the Field,” Journal of SchoolLeadership, Vol. 10, 2000, pp. 158–187.

Dowd, A. (2005). Data don’t drive: Building a practitioner-driven culture of inquiry to assess community college performance. Lumina Foundation for Education: Indianapolis, ID.

Retrieved August 22, 2008 from http://www.achievingthedream.org/_images/_index03/datadontdrive2005.pdf

Edmonds, Ronald, “Effective Schools for the Urban Poor,” Educational Leadership, Vol. 37, No. 1, 1979, pp. 15–24. Feldman, Jay, and Rosann Tung, “Whole School Reform: How Schools Use the Data-Based Inquiry

and Decision Making Process,” paper presented at the 82nd Annual Meeting of the American Educational Research Association, Seattle, Wash., April 2001.

Fuller, B., Loeb, S., Arshan, N., Chen, A. & Yi, S. (2007). California principals’ resources:

Acquisition, deployment and barriers. Paper prepared for the Getting Down to Facts Project. Stanford, CA: Stanford University Institute for Education Policy and Practice. Read p. 1-54. Retrieved August 22, 2008 from http://irepp.stanford.edu/documents/GDF/STUDIES/13-Fuller/13-Fuller(3-07).pdf

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Perspiration, and Time: Operations and Achievement in Edison Schools, MG-351-EDU, Santa Monica, Calif.: RAND Corporation, 2005. Online at http://www.rand.org/pubs/monographs/MG351/.

Halverson, R., Grigg, J., Prichett, R., & Thomas. C. (2007, March). The new instructional

leadership: Creating data-driven instructional systems in school. Journal of School Leadership, 17, 159-194. Retrieved August 22, 2008 from http://vnweb.hwwilsonweb.com/hww/results/external_link_maincontentframe.jhtml?_D ARGS=/hww/results/results_common.jhtml.29

Hamilton, L. S., “Assessment as a Policy Tool,” Review of Research in Education, Vol. 27, 2003, pp. 25–68. Hamilton, L. S., and M. Berends, InstructionalPractices Related to Standards and Assessments,

WR-374-EDU, Santa Monica, Calif.: RAND Corporation, 2006. Online at http://www.rand.org/ pubs/working_papers/WR374/.

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Research on Evaluation, Standards, and Student Testing, 2002. Herman, J., and B. Gribbons, Lessons Learned in Using Data to Support School Inquiry and Continuous

Improvement: Final Report to the Stuart Foundation, Los Angeles, Calif.: National Center for Research on Evaluation, Standards, and Student Testing, 2001.

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central offices: Toward a policy and research agenda. Educational Policy, 22(4), 578- 608. Retrieved August 22, 2008 from http://epx.sagepub.com.proxy.lib.csus.edu/cgi/reprint/22/4/578

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James, E. A., Milenkiewicz, M.T., & Bucknam, A. (2008). Participatory action research for educational leadership: Using data-driven decision making to improve schools. Thousand Oaks, CA: Sage Press.

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Schmoker, M., “Tipping Point: From Feckless Reform to Substantive Instructional Improvement,”

Phi Delta Kappan, Vol. 85, 2004, pp. 424–432. Schmoker, M., and R. B. Wilson, “Results: The Key to Renewal,” Educational Leadership, Vol. 51, No. 1, 1995, pp. 64–65. Senge, P., The Fifth Discipline: The Art and Practice of the Learning Organization, New York: Doubleday,1990. Snipes, J., F. Doolittle, and C. Herlihy, Foundations for Success: Case Studies of How Urban School Systems Improve Student Achievement, Washington, D.C.: MDRC and the Council of Great City Schools, 2002. Spillane, J. P., and C. L. Thompson, “Reconstructing Conceptions of Local Capacity: The Local Educational Agency’s Capacity for Ambitious Instructional Reform,” Education Evaluation and Policy Analysis, Vol. 19, No. 2, 1997, pp. 185–203. Stecher, B. M., and L. S. Hamilton, Using Test-Score Data in the Classroom, WR-375-EDU, Santa

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Symonds, K. W., After the Test: How Schools Are Using Data to Close the Achievement Gap,

San Francisco, Calif.: Bay Area School Reform Collaborative, 2003. Thernstrom, A., & Thernstrom, S. (2003). No excuses: Closing the racial gap in learning. New York:

Simon & Schuster. Vierra, A., & Pollock, J. (1998). Reading educational research. New York: Merrill. Wahlstrom, D. (1999). Using DATA to Improve Student Achievement. Suffolk, VA: Successline. http://www.successlineinc.com/books-using-data.html Wayman, Jeffrey C., “Involving Teachers in Data-Driven Decision Making: Using Computer Data

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Practices in Using Data Warehouse and Reporting Systems,” paper presented at the 2005 Annual

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Meeting of the American Educational Research Association, Montreal, Canada, April 2005. Wayman, Jeffrey C., Sam Stringfield, and Mary Yakimowski, Software Enabling School Improvement

Through Analysis of Student Data, Baltimore, Md.: Center for Research on the Education of Students Placed at Risk, Johns Hopkins University, 2004.

Wellman, B., & Lipton, L. (2004). Data-driven dialogue: A facilitator’s guide to collaborative inquiry.

Sherman, CT: MiraVia.

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APPENDIX A

PROFESSIONAL STANDARDS FOR STUDENT PERFORMANCE

Graduate students in the Department of Educational Leadership at The University of Montana are expected to:

• Demonstrate professional vision in the practice of educational administration • Accept responsibility and accountability for class assignments in their role as members of the

class • Demonstrate growth during the period of their graduate career • Demonstrate good decision making and an awareness of organizational issues from a variety of

perspectives • Demonstrate imagination and originality in the discussion of educational leadership issues • Understand the relationship between theory and practice and the value of reflective leadership • Demonstrate a moral, humanistic, ethical and caring attitude toward others • Demonstrate an ability to build trust and positive relationships with others • Demonstrate a tolerance for diversity and a warm acceptance of others regardless of their

backgrounds or opinions • Demonstrate emotional stability and an ability to work well with other members of the class,

including the instructor • Demonstrate an ability to express himself/herself well in speech and writing, and • Demonstrate mastery of fundamental knowledge of course content and an understanding of its

application

FAILURE TO DEMONSTRATE THE AFOREMENTIONED QUALITIES ON A CONSITSTENT BASIS MAY RESULT IN REMOVAL FROM CLASSES AND/OR THE EDUCATIONAL LEADERSHIP PROGRAM.

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APPENDIX B

Mission Alignment The Department of Educational Leadership has aligned itself with the mission of The University of Montana-Missoula and the College of Education and Human Sciences Mission. The following mission statements demonstrates this alignment. Learning activities in this course have been designed to address appropriate areas of these mission statements. The University of Montana-Missoula Mission The mission of The University of Montana-Missoula is the pursuit of academic excellence as indicated by the quality of curriculum and instruction, student performance, and faculty professional accomplishments. The University accomplishes this mission, in part, by providing unique educational experiences through the integration of the liberal arts, graduate study, and professional training with international and interdisciplinary emphases. Through its graduates, the University also seeks to educate competent and humane professionals and informed, ethical, and engaged citizens of local and global communities. Through its programs and the activities of faculty, staff, and students, The University of Montana-Missoula provides basic and applied research, technology transfer, cultural outreach, and service benefiting the local community, region, state, nation and the world. Phyllis J. Washington College of Education and Human Sciences Mission Statement The College of Education and Human Sciences shapes professional practices that contribute to the development of human potential. We are individuals in a community of lifelong learners, guided by respect for knowledge, human dignity, and ethical behavior. We work together producing and disseminating knowledge to advance the physical, emotional, and intellectual health of a diverse society. Educational Leadership Mission Statement The mission of Educational Leadership at The University of Montana-Missoula is to develop leaders for learning organizations who are guided by respect for knowledge, human dignity, and ethical behavior. This is accomplished by providing high quality academic and professional opportunities. We subscribe to a definition of leadership wherein individuals assume evolving roles within influence relationships requiring their contributions in order to achieve mutual purposes.

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APPENDIX C

ISLLC Standards for School Leaders Standard 1: A school administrator is an educational leader who promotes the success of all students by facilitating the development, articulation, implementation, and stewardship of a vision of learning that is shared and supported by the school community. Standard 2: A school administrator is an educational leader who promotes the success of all students by advocating, nurturing, and sustaining a school culture and instructional program conducive to student learning and staff professional growth. Standard 3: A school administrator is an educational leader who promotes the success of all students by ensuring management of the organization, operations, and resources for a safe, efficient, and effective learning environment. Standard 4: A school administrator is an educational leader who promotes the success of all students by collaborating with families and community members, responding to diverse community interests and needs, and mobilizing community resources. Standard 5: A school administrator is an educational leader who promotes the success of all students by acting with integrity, fairness, and in an ethical manner. Standard 6: A school administrator is an educational leader who promotes the success of all students by understanding, responding to, and influencing the larger political, social, economic, legal, and cultural context.

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APPENDIX D Montana PEPP Standards and Procedures

Montana Professional Educator Preparation Program Standards and Procedures For a more detailed explanation of the PEPP Standards, visit the web site for OPI website: http://opi.mt.gov/pdf/Accred/09PEPPSManual.pdf 10.58.705 SCHOOL PRINCIPALS, SUPERINTENDENTS, SUPERVISORS AND CURRICULUM DIRECTORS (1) The program requires that successful candidates:

(a) facilitate the development, articulation, implementation, and stewardship of a school or district vision of learning supported by the school community in order to promote the success of all students; (b) promote a positive school culture, provide an effective instructional program, apply best practice to student learning, and design comprehensive professional growth plans for staff in order to promote the success of all students; (c) manage the organization, operations, and resources in a way that promotes a safe, efficient, and effective learning environment in order to promote the success of all students; (d) collaborate with families and other community members, respond to diverse community interests and needs, including Montana American Indian communities, and mobilize community resources in order to promote the success of all students; (e) act with integrity, fairness, and in an ethical manner in order to promote the success of all students; (f) understand, respond to, and influence the larger political, social, economic, legal, and cultural context in order to promote the success of all students; and (g) complete an internship/field experience that provides at least 216 hours of significant opportunities to synthesize and apply the knowledge and practice and develop the skills identified in this rule through substantial, sustained, standards based work in real settings, planned and guided cooperatively by the institution and properly administratively endorsed school district personnel for graduate credit.

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APPENDIX E

Review of Research Article

Find and critically review a published quantitative research article/paper that should have never been published and prepare a written report that specifically identifies statistical, methodological, logical, and/or other research errors. Examples of exemplary assignments will be presented in class. Assignments are to reflect very high quality of thought and content. Written assignments must be presented in APA format. This assignment must be turned in to the professor on the date that has been assigned. How to find a quantitative research article/paper/dissertation?

1. Search ERIC online to find the article/paper/dissertation. Students should be searching ERIC every week, thus becoming familiar with the search process and finding research that is of interest.

2. Search the library in person to find the article/paper/dissertation. Look for journals of interest such as the American Educational Research Journal.

What part of the research?

1. Examine the whole research article/paper, not just part of it such as the summary. The student must go to the research as published by the researcher rather than a secondary source summarizing the research.

2. Attach the title page and abstract of the article/paper/dissertation to your review. What to look for in the quantitative article/paper/dissertation?

1. Research Problem: Is a research problem stated? 2. Population and Sample: What is the population? What is the sample?

(The sample must have at least 30.) Is it the correct size to represent the population from which it is sampled? Is the sample random? (90% of research articles/papers cannot meet these criteria alone!) This is very simple for students to read and determine. Were the findings generalized to anyone who did not have an equal chance to be part of the sample? (This error is present in 99% of the research out there.)

3. Variables: What are the variables? Are they defined? That is, if education is one of the variables, has education been defined? If not, then the research lacks validity because if you do not define what you are going to measure, then you have no idea what you have found after measurement. You cannot measure what you cannot define. No definition, then no findings.

4. Level of measurement: What is the level of data? Has non interval/ratio data been used in tests requiring interval/ratio level data? For example, have means etc. been calculated on ordinal data? If so, then the findings are not mathematically meaningful.

5. Statistic: What statistical procedure was used? What is appropriate? Here, students are limited in research they critique to a correlation/regression study or t-test study so they will be familiar with the stats being used. Students can easily do this by doing a search on research and requiring that the word “regression” or whatever be used in the search criteria.

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6. a priori: Has the magnitude of importance for the relationship (correlation) or mean difference (t-test) been established a priori? That is, did the researcher wait until the findings were in before deciding what would be an important finding?

7. Words used in the research: Are the words biased? For example, an author might discuss “manipulating” the numbers, or may state that the researcher completed five studies and because of not finding the desired results conducted a sixth and final study and thus finally found what was wanted!

Requirements for the review 1. APA format (title page, reference page, numbered pages, double-spaced, etc.) 2. Pages requirements: One title page, two-three pages for the body of the assignment, and one

page for references. 3. This assignment must be turned in to the professor on the date that has been assigned

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APPENDIX F

General Topics for the Five-Chapter Research Paper IS THERE A RELATIONSHIP BETWEEN:

1. Amount of coffee undergraduate students drink on the day of the interpersonal communication midterm exam and their achievement on the exam?

2. How many hours of moderate intensity exercise a student engages in per week and the student’s rating on the Body Mass Index (B.M.I.)?

3. The number of public speaking classes attended per semester and the academic performance of students enrolled in Public Speaking 101 at the UM?

4. The number of verified practice hours of driving time and the score of the student on the state-mandated driving exam?

5. Amount of time spent with families per week and a student’s GPA at the end of the semester? 6. The amount of vegetable intake for a family per week and the amount of household annual

income? 7. Amount of time students study for the final examination in statistics and the score they earn on

it? 8. The amount of time a female student spends doing physical activity and her weight in pounds? 9. The amount of beers that a student drinks in a week and the percentage on the final exam? 10. The number of ounces of candy that a student eats and the number of cavities that he/she has in a

year? 11. The number of parking tickets that a student receives and the GPA at the end of the semester? 12. The amount of time in hours spent in the library and the percentage scores on the mid-term

exam? 13. The number of days hunting and the number of big game killed? 14. The amount of store sales in a month and the size (in square feet) of the store? 15. The amount of labor hours that it takes a moving company employee to move household goods

and volume of goods (in cubic feet)? 16. The level of intervention (hours of training) and the number of injuries in college athletes? 17. The height and weight of 35 sixth graders? 18. Income (annual income in dollars) and level of education (years of education)? 19. The number of wins last season in tennis and the average number of weekly injuries?

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APPENDIX G

Prepare a five chapter research paper in which you discuss a research problem, data collection, null hypothesis, and statistical procedures as covered in class. You will then generate dummy data, run a statistical analysis of the data, report the findings, make a decision regarding your hypothesis, and formulate your conclusion. Examples of exemplary assignments will be presented in class. Assignments are to reflect very high quality of thought and content. Written assignments must be presented in APA format. This assignment must be turned in to the professor on the date that has been assigned. Checklist of the elements in the assignment:

1. APA format a. Chapters: each chapter begins on a new page b. Headings: headings are centered on the page c. Title page: title page is attached d. Spacing: double-spacing occurs throughout the paper e. Pages: pages are numbered f. Other: follows APA format

2. Attachments

a. Excel: copy of Excel sheet with the raw data b. Excel: copy of Excel sheet with the statistical analysis of the data c. Sample size: copy of Raosoft Sample Size calculator d. Tables copy of appropriate tables, charts, graphs

3. Statistics

Stats for a Correlation Stats for a t-test Two variables that are at an interval/ratio level of data Two variables that are appropriate for a correlation

Two variables that are at an interval/ratio level of data Two variables that are appropriate for a correlation

Sample size that is the correct size to represent the population

Sample size that is the correct size to represent the population

Correct statistics used for Correlation Correct statistics used for Correlation Pearson r is reported in a decimal; r² is reported in a percent

Both the observed and critical value of t are reported and explained

Table 10 must be attached and the critical value circled

Table 4 or 5 must be attached and the critical value circled

Null hypothesis: reject or fail to reject Null hypothesis: reject or fail to reject

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APPENDIX H

Administrative Biography

Patty Kero

Patty M. Kero, Ed.D., is an Assistant Professor of Education in the Department of Educational Leadership at the P.J.W. College of Education and Human Sciences of The University of Montana. She holds an Ed.D. from The University of Montana in Educational Leadership; a M.Ed. from Harvard University in Administration, Planning, and Social Policy; and a B.A. from The University of Montana in Elementary Education.

Professor Kero has taught seventeen consecutive years in the Pre K-12 setting: five years in Montana and twelve years in Washington. Her fifteen years of elementary and secondary administrative experience in public school settings in Nevada, Idaho, and Montana included assistant principal, principal, Title 1 director, and superintendent. Educators, locally and nationally, have studied her school in Nevada after they won Redbook’s nationally recognized America’s Best Schools award and her school in Idaho was the national winner of “What Parents Want in Their Schools.” While in Idaho, she also worked with Barbara Morgan, NASA’s Teacher in Space astronaut. For six years, she taught as an adjunct for Sierra Nevada College and Great Basin College in Elko, Nevada. While earning her Master’s, she worked at the Harvard Principals’ Center with Roland Barth and Richard Ackerman. As a professor at UM, she has taught undergraduate and graduate courses in the areas of statistics and school administration including analysis of educational data, supervision and evaluation, public relations, and advanced educational statistics. Working as a Due Process Hearing Officer in Nevada, she served the students who were identified as needing special education services. Her published article in Kappan addressed special education issues in schools. Her research interests are brain-based learning and teaching, leadership in education, and legal issues surrounding schools. Her leadership commitment has been to advocate for successful continuous learning and teaching for all members of the educational community, by serving the educational needs of all, regardless of age, gender, cultural group identity, or abilities.

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APPENDIX I

Assignment

Paper Title

by Your Name

Submitted to Dr. Patty Kero

In Partial Fulfillment of the Requirements of EDLD 519: Measurement and Analysis of Educational Data

Summer 2012

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EDLD519 Student ID Term519A

Component Exemplary Acceptable Unacceptable18-20 points 14-17 points 0-13 points

Analyze and Apply the Knowledge and Understanding

The student clearly exhibits outstanding ability to

articulate a school-wide plan analyzing and applying the

appropriate data-driven practices to improve

educational outcomes.

The student exhibits ability to articulate a school-wide plan

analyzing and applying the appropriate data-driven practices

to improve educational outcomes.

The student does not adequately exhibit ability to articulate a school-

wide plan analyzing and applying the appropriate data-driven

practices to improve educational outcomes.

18-20 points 14-17 points 0-13 points

Creative Thinking

The student is highly adept at creatively articulating the

process of decision-making with data on a particularly

complex educational issue. The student enthusiastically

attempts original and appropriate ways, methods or strategies of approaching the complex ideas, monitors the progress, and evaluates the success based on their deep

understanding of his/her discipline. The student clearly

goes beyond what is known and stretches in new

directions.

The student is adept at creatively articulating the process of

decision-making with data on a particularly complex educational issue. The student attempts new ways, methods or strategies of approaching the complex ideas,

monitors the progress, and evaluates the success. The

student goes beyond what is known and stretches in new

directions.

The student is not adept at creatively articulating the process of decision-making with data on a particularly complex educational

issue. The student does not attempt new ways, methods or strategies of

approaching the complex ideas, monitors the progress, and

evaluates the success. The student does not go beyond what is known

and stretches in new directions.

18-20 points 14-17 points 0-13 points

Montana PEPPS: facilitate the development, articulation, implementation, and stewardship of a school or district vision of learning supported by the school community in order to promote the success of all students

The student clearly articulates a well developed

improvement plan (using the format of Data Wise: A Step

by Step Guide to Using Assessment Results to Improve Teaching and

Learning) for the facilitation and implementation of a

shared vision for using data to make decisions.

The student clearly articulates an improvement plan (using the

format of Data Wise: A Step by Step Guide to Using Assessment Results to Improve Teaching and Learning) for the facilitation and

implementation of a shared vision for using data to make

decisions.

The student fails to articulate an improvement plan (using the

format of Data Wise: A Step by Step Guide to Using Assessment Results to Improve Teaching and Learning)

for the facilitation and implementation of a shared vision for using data to make decisions.

18-20 points 14-17 points 0-13 points

Montana PEPPS: Promote a positive school culture, provide an effective instructional program, apply best practice to student learning, and design comprehensive professional growth plans for staff in order to promote the success of all students

The student demonstrates an impressive depth of

knowledge and level of synthesis in articulating how data-driven decision making promotes a positive school

culture, an effective instructional program, and

applies it to best practices in teaching and learning.

The student demonstrates a depth of knowledge and level of

synthesis in articulating how data-driven decision making promotes

a positive school culture, an effective instructional program, and applies it to best practices in

teaching and learning.

The student fails to demonstrate an impressive depth of knowledge and

level of synthesis in articulating how data-driven decision making

promotes a positive school culture, an effective instructional program, and applies it to best practices in

teaching and learning.

18-20 points 14-17 points 0-13 points

Format

The student includes and properly identifies all nine

chapters (aligned to the corresponding chapters in the

textbook) and appendices. The student includes and

properly explains visuals such as charts, graphs, and tables.

Further, the student demonstrates understanding

on statistics.

The student includes all nine chapters and appendices. The student includes and explains

visuals such as charts, graphs, and tables. Further, the student

demonstrates understanding on statistics.

The student includes the nine chapters and appendices. The

student fails to include and explains visuals such as charts, graphs, and

tables. Further, the student fails to demonstrate understanding on

statistics.

18-20 points 14-17 points 0-13 points

MechanicsThe student follows APA

format.

The student follows APA format and mechanical errors do not

detract from the paper.

The student partially follows APA format and mechanical errors are

minimal.

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APPENDIX J EDLD519 Student ID Term 519A Component Exemplary Acceptable Unacceptable 18-20 points 14-17 points 0-13 points Analyze and Apply the Knowledge and Understanding

The student clearly exhibits outstanding ability to articulate a school-wide plan analyzing and applying the appropriate data-driven practices to improve educational outcomes.

The student exhibits ability to articulate a school-wide plan analyzing and applying the appropriate data-driven practices to improve educational outcomes.

The student does not adequately exhibit ability to articulate a school-wide plan analyzing and applying the appropriate data-driven practices to improve educational outcomes.

18-20 points 14-17 points 0-13 points Creative Thinking The student is highly

adept at creatively articulating the process of decision-making with data on a particularly complex educational issue. The student enthusiastically attempts original and appropriate ways, methods or strategies of approaching the complex ideas, monitors the progress, and evaluates the success based on their deep understanding of his/her discipline. The student clearly goes beyond what is known and stretches in new directions.

The student is adept at creatively articulating the process of decision-making with data on a particularly complex educational issue. The student attempts new ways, methods or strategies of approaching the complex ideas, monitors the progress, and evaluates the success. The student goes beyond what is known and stretches in new directions.

The student is not adept at creatively articulating the process of decision-making with data on a particularly complex educational issue. The student does not attempt new ways, methods or strategies of approaching the complex ideas, monitors the progress, and evaluates the success. The student does not go beyond what is known and stretches in new directions.

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18-20 points 14-17 points 0-13 points Montana PEPPS: facilitate the development, articulation, implementation, and stewardship of a school or district vision of learning supported by the school community in order to promote the success of all students

The student clearly articulates a well-developed improvement plan for the facilitation and implementation of a shared vision for using data to make decisions.

The student clearly articulates an improvement plan for the facilitation and implementation of a shared vision for using data to make decisions.

The student fails to articulate an improvement plan for the facilitation and implementation of a shared vision for using data to make decisions.

18-20 points 14-17 points 0-13 points Montana PEPPS: Promote a positive school culture, provide an effective instructional program, apply best practice to student learning, and design comprehensive professional growth plans for staff in order to promote the success of all students

The student demonstrates an impressive depth of knowledge and level of synthesis in articulating how data-driven decision making promotes a positive school culture, an effective instructional program, and applies it to best practices in teaching and learning.

The student demonstrates a depth of knowledge and level of synthesis in articulating how data-driven decision making promotes a positive school culture, an effective instructional program, and applies it to best practices in teaching and learning.

The student fails to demonstrate an impressive depth of knowledge and level of synthesis in articulating how data-driven decision making promotes a positive school culture, an effective instructional program, and applies it to best practices in teaching and learning.

18-20 points 14-17 points 0-13 points Format The student includes

and properly identifies all five chapters. The student includes and properly explains visuals such as charts, graphs, and tables. Further, the student demonstrates understanding on statistics.

The student includes all five chapters. The student includes and explains visuals such as charts, graphs, and tables. Further, the student demonstrates understanding on statistics.

The student includes the five chapters. The student fails to include and explains visuals such as charts, graphs, and tables. Further, the student fails to demonstrate understanding on statistics.

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18-20 points 14-17 points 0-13 points Mechanics The student follows

APA format. The student follows APA format and mechanical errors do not detract from the paper.

The student partially follows APA format and mechanical errors are minimal.

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APPENDIX K Sources of Accountability

Sources of Accountability

Examples

Appendix A: National Which sources has the greatest impact on your school? Why?

No Child Left Behind (NCLB) Elementary and Secondary Education Act (ESEA), Race to the Top Common Core State Standards, Adequate Yearly Progress (AYP), National Assessment of Educational Progress (NAEP) Free and Reduced Lunch Response to Intervention (RtI) Individuals with Disabilities Education Act (IDEA) Highly Qualified Teachers (HQT) Even Start Literacy Program Gun-Free Schools Act English Language-Learners Title 1 Title II-Improving Teacher Quality Grants Title III Part A-English Acquisition and Enhancement Title IV-Safe Schools Title V-Innovative Programs, Character Education Title VI-Rural Education Title X Part C-Education of Homeless Children

Appendix B: State Which sources has the greatest impact on your school? Why?

Montana OPI: Reports and Data: Measurement and Accountability Measured Progress MontCAS/CRT’s, Common Core State Standards Initiative Indian Education for All Fall Report Accreditation Standards Coordinated School Health Program School Wellness Policy Tobacco Use Prevention Program E-Rate Graduation Matters-Montana Montana Behavioral Initiative (MBI) Neglected, Delinquent Youth Program School Nutrition Program

Appendix C: Local Which sources has the

Graduation Matters-Missoula, School Board Policy Creative Classroom Grants

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greatest impact on your school? Why?

MUD Project-Missoula Urban Demonstration Project Missoula Outdoor Learning Adventures (MOLA) Big Brother/Big Sisters-Missoula Missoula Flagship Program Olweus Bully Prevention Program- Hellgate Elementary District

Appendix D: Continuous School Improvement Plan

Yearly Action Plan, Yearly Effectiveness Report The Board of Public Education established the goal that all school districts develop, implement, evaluate, and revise a single five-year comprehensive education plan to ensure continuous education improvement for all students and all schools. Write about one aspect of your school’s improvement plan that has the greatest impact on the improvement of teaching and learning.

Appendix E: A Creative Way to Address Accountability

Write about one additional tool that your school could use as a source of accountability that is NOT being used presently. Please be creative! For example: Authentic accountability, measurements, assessments, senior projects, culminating portfolio presentations

Appendix F: Montana State Student Information System

Montana has a Student Information System titled Achievement in Montana (AIM). This system reports student-related data from school districts to OPI, including enrollment, demographic data, eligibility for state and federal education programs, registration for the statewide assessments, and special education planning and reporting. Write about your practical experience with inputting data into the program or reading the AIM reports.

Appendix G: National Assessment of Educational Progress

NAEP operates on a two-year cycle, with major assessments taking place in odd-numbered years, and much smaller, limited assessments and special studies taking place in even-numbered years. In even-numbered years, Montana usually has fewer than fifteen schools in the national sample, and in 2012, the number is zero. Altogether, fourteen states have no schools participating this year. Montana will participate in the Reading and Mathematics assessments in 2013, the next "big NAEP" year. Write about something that you learned about the NAEP in Montana from the Montana OPI website: http://opi.mt.gov/Reports&Data/index.html?gpm=1_5#gpm1_9

Appendix H: Indian Education Data, Research and Reports

Write about something that you learned about the Indian Education Data in Montana from the Montana OPI website:

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http://opi.mt.gov/Reports&Data/index.html?gpm=1_5#gpm1_7 Appendix I: Reports and Data, Measurement and Accountability

Write about something that you learned about the Graduation Rates in Montana from the Montana OPI website: http://opi.mt.gov/Reports&Data/Measurement/Index.html

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The “Data Wise” Improvement Process: Eight steps for using test data to improve teaching and learning by Kathryn Parker Boudett, Elizabeth A. City, and Richard J. Murnane Prepare 1. Organize for Collaborative Work: Schools develop a data team to gather data from many sources and then set up schedules to allow school personnel to collaborate in their examination of the data.

2. Build Assessment Literacy: Educators learn the basics of assessment terminology and learn how to both analyze and talk about the data.

Inquire 3. Create a Data Overview: Educators create a concise summary of student achievement results that inspires rather than overwhelms. The summary is intended to show what students are learning and reveal gaps in that understanding.

4. Dig into Data: Now that the data is available, educators look at a broader range of student work, such as projects, classwork, and homework, to provide more clarity on what the data has shown. Through this step, educators discover gaps in certain areas that are common to large numbers of students.

5. Examine Instruction: Educators look at how classroom instruction has affected poor student performance in areas revealed by digging into the data. They identify an area that they want to tackle through collaboration.

Act 6. Develop an Action Plan: The team decides to address a certain area and designs a professional development program to support educators who will put that plan into action.

7. Plan to Assess Progress: Educators set goals — both shortterm and long-term — and develop a plan that will determine if those goals have been met.

8. Act and Assess: Educators monitor how the plan is working, and then make adjustments to make improvements. The process then returns to Step 3, in which data is gathered for analysis, and a new cycle of review begins.

Read more: http://www.gse.harvard.edu/news-impact/2012/01/the-data-wise-process/#ixzz1zO9gK9Vp

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