1. Data Driven Small Learning Communities Richard D. Jones Senior Consultant

2. Personality

3. Data Driven Small Learning Communities Richard D. Jones Senior Consultant

4. Bigger isn’t always better!

6. “Learning is about constructing relationships in which students connect with teachers or subjects. Small schools foster the personalization strategies to support those relationships. ” Tom VanderArk

7. Why Consider Small Learning Communities?  Academic Achievement  Social Benefits  Attendance and Graduation Rates  Safety and Discipline  Preparation for High Education  Extracurricular Participation  Financial Benefits

8. Bringing Best Practices to Scale 1. Small Learning Communities 2. High Expectations 3. 9 th Grade 4. 12 th Grade 6. Curriculum 5. Data 7. Relationships / Reflective Thought 8. Professional Development 9. Leadership

9. Do Small Learning Communities Make a Difference?  Increased Attendance  Increased Student Achievement  Increased Student Participation  Increased Student and Parent Satisfaction  Increased Positive Student Behavior  Greater Focus on Students’ Interests and Aptitudes  Relevancy Leads to High Achievement

10. Smaller Strategies Elementary  Reduced Class Size  Looping  Multi-age Groupings  Expanded Use of Adults  Learning Centers  Differentiated Instruction

11. Smaller Strategies Secondary  Schools-within-Schools  House Plans  Freshman Academy  Magnet Schools  Career Academies

12. Characteristics  Improved Knowledge of Student Needs  Longer Term Relationships  Personalization of Learning  Stronger Instructional Focus  Shared Sense of Accountability

13. Rigorous and Relevant Learning

14. Rigor

15. Relevance My only skill is taking tests.

16. All Students

17. Creating Small Learning Communities Driven by Data

18. Improving Student Achievement What are the biggest issues for teachers?

19. Work Harder ? Work Smarter?

20. Why?

21. Changing Nature of Work Why ?

22. Accelerating Technology

23. Employment 1970’s High Skill Low Skill

24. Employment 1990’s High Skill Semi Skill Low Skill

25. Employment 2010 High Skill Semi Skill Low Skill

26. 1970’s 1990’s 2010

29. Translating Standards Into Teaching and Learning Why?

30. Standards Destination vs. Route vs. Vehicle

31. Standards Standards vs. Curriculum vs. Student Work

32. An Overcrowded Curriculum Why?

33. Needed Time McREL 15,465 Hours Available Time 9,042Hours

34. Teachers struggling to teach an overloaded curriculum!

35. Focus on Student Learning Why?

36. Teaching VS. Learning

37. Expectations Standards

38. Tool For School Alignment Why ?

39. Language Arts Need S tandards H M L

40. Language Arts NeedTest Standards HH or L MM LL

41. Language Arts NeedTestInstruction Standards HH or LL MML - M LLM - H

42. Alignment for Improving Instruction Standards State Requirements Instructional Resources Teacher Materials Content Assessment Accountability Instruction Courses and Programs

43. Alignment for Improving Instruction Standards State Requirements Instructional Resources Teacher Materials Content Assessment Accountability Instruction Courses and Programs

44. Explaining What’s Important Why ?

45. Ask Me..... “How will I ever use what I’m learning today?”

46. In schools the status quo persists!

47. Why ? Prepare for THE test?

48. New SAT - 2005 More Application New Writing Section Expand Critical Reading for Information More Achievement Base on three years of Math Higher Level Math Skills Less Aptitude Eliminate analogies Eliminate simple math reasoning

50. NYS Math A Question June 2003

51. NYS Math A Question June 2002

52. Increase Student Motivation Why ?

54. Why SLCs and Data Driven? Changing Nature of Work Translating Standards into Teaching Reduce Overcrowded Curriculum Way to Focus on Student Learning Tool for School Alignment Explain What is Important Prepare for THE test Increase Student Motivation

55. What data do teachers use when deciding what to teach ?

56. Use of Data

57. Data Rich but Analysis Poor

58. Types of Data  Curriculum  Demographics  Student Learning  School Processes  Perceptions of Quality

59. Types of Data Example Type of DataLiteracy Curriculum Demographics Student LearningPerformance on State Test Processes Perceptions of Quality

60. Types of Data Example Type of DataLiteracy CurriculumLevel of Reading Comprehension on State Test Real World Postsecondary Learning DemographicsIncoming Student Reading Levels Student LearningPerformance on State Test Local Assessment ProcessesReading Levels of Textbooks Teaching Materials Success of Reading Practices Perceptions of QualityStudent Surveys

61. Relationships Clearly Important ? How to Quantify? How to Develop?

62. Relationships are Essential to Student Learning Family Peers Teachers Community Result of combination of support from:

63. Relationship Model 0.Isolated 1.Knowing 2.Assisting 3.Mentoring 4.Enduring 5.Mutually Beneficial

64. Everyone needs support when they take new risks

65. Relationship Model Key to Student Learning 1. Knowing Teachers get to know students and their families 2. Assisting Some positive support, but sporadic 3. Mentoring Moderate support from some individuals 4. Enduring Fully supported from all individuals 5. Mutually Beneficial Mutually supportive learning community

66. Support Behaviors Respect Being There Active Listening Frequent Contact Encouragement Avoiding “Put Downs” ??????

67. “Experience and research make it very clear that school size does matter-- but they also make it clear that ‘small’ is no silver bullet” Michelle Fine and Janice Somerville

68. Beginning to Use Data  Ask Questions and Analyze  Avoid Snap Judgments  Collect Data to Answer Questions  Set Goals Using Data

69. Staying on the Cutting Edge Cutting Edge

70. NYS Staff Development Council http://www.nyssdc.org

71. Further Information http://dickjones.us Or http://www.natpd.com