1. Evidence Based School Counseling Chapter 2

2. Data Based Decision Making Relies on evidence to define problems Requires that goals are stated in ways that will allow for data to be gathered Used quantitative data analysis techniques to describe problems and to direct activities

3. Definitions Achievement Data – Standardized test scores, grade point averages, SAT and ACT scores, graduation rates, AP test scores Achievement Related Data – Course enrollment patterns, discipline referrals, suspension rates, attendance, parent involvement, homework completion rates Competency-related Data – Six or four year academic plans, job shadowing participation rate, peer mediation sessions, knowledge of graduation requirements.

4. Models of Data-Based Decision- Making Models Whole School Reform- Reynolds and Hines (2001) – SC leads an interdisciplinary team through the DBDM process to define problems and decide on interventions All DBDM focus on the use of data to – Define problems – Set goals – Target interventions

5. The DBDM Team Team is developed based on – Whole School reform initiative – Component of the School Counseling Program Questions – Does the team include all the needed perspectives to: correctly identify problems and potential solutions Correctly identify strategies and barriers to intervention implementation – Do team members have the necessary data literacy skills – Do team members show the capacity for effective collaboration

6. General Model of DBDM Describe the Problem Generate vision data Committing to benchmarks Identify where and how to intervene Selecting interventions Evaluating interventions Monitor problem data

7. Describe the Problem Important data to use include the following – Achievement data – Achievement related data – Guidance curriculum competency data – School Climate survey data – Relevant student health and well-being data – Needs Assessment data – Demographic Data (pg. 20 DCH for process model)

8. Generating Vision Data Reflect on the future goal toward which school efforts and resources need to be directed. – Goals need to be stated in concrete and measureable terms – What should the data look like in 3-5 years. – Should be ambitious and attainable Ambitious goals serves student interests and engages the passion of adults in the school An attainable goal is likely to motivate efforts to change

9. Committing to benchmarks Breaking down the goal expressed as vision data into yearly goals – Reflect equal increments of change needed each year to reach the vision – Sometimes DBDM team may create unequal increments

10. Identifying Where and How to Intervene Seven levels of programmatic interventions: – Individual – Group – Classroom – Grade level – Schoolwide – Home/family – Community/society May be at several levels.

11. Selecting Interventions (see chapter 4 for details ) Interventions selected must be supported by strong research evidence. Research programs carefully Not just the packaging of interventions

12. Evaluating Interventions (see chapter 5 for a detailed description) Evaluation should occur at three levels – Know whether the participants learned the knowledge or skills the intervention intended to teach or whether their attitudes shifted – Know whether the participants changed their behavior in the problem data in ways that predict success – Measure the actual change in problem data and to compare it with the benchmark targets

13. Monitoring Problem data Monitored each year. The DBDM team reviews the problem data in comparison to the stated benchmarks, examination data and decide whether the current strategies and approaches need to be continued, modified or abandoned.

14. Enabling Conditions Collaborative Culture Collaborative Structures Widespread Data Literacy Access to useful data (see pg, 25)

15. Practical Considerations in Using data Chapter 3 School counselors use data for a variety of purposes: – To ensure that every student receives the developmental instruction that is described in professional standards (ASCA, 2003 Campbell & Dahir, 1997). – To make decisions regarding which areas of need require additional support or intervention (Hatch, Holland & Meyers, 2003; Hayes, et al 2002). – To measure the effectiveness of their activities and interventions and to share their successes with the school community – To evaluate the effectiveness of their programs and for program inprovement

16. Types of Data Student Achievement Data – Passing rates for state achievement tests – Standardized achievement test data – SAT and ACT scores – Algebra passage rates – GPA – Drop-out rates and graduation rates – College acceptance rates – Completion of college prep requirements – College freshman remediation rates – Advanced Placement test scores Data should be mined to determine where resources are needed.

17. Achievement-Related Data Course enrollment patters Discipline referrals Suspension rates Alcohol, tobacco, and other drug use Attendance rates Parent involvement Extracurricular activities Homework completion rates

18. Standards and Competency-Related data Each school should have it’s own set of measurable competencies and student learning objectives Quantitative indicators of competency attainment – Percentage of students who know credit requirements for graduation – Demonstrate knowledge of study skills and how to use an academic planner – Use test-taking strategies – Can identify the steps in setting goals – Demonstrate conflict resolution skills, and – Believe that it is important to come to school every day.

19. Data Relationships (see pg 32 Table 3.1) – What achievement-related data must be moved to address the problem and what competencies must be developed to move the achievement related data – Next determine what standards and competences students lack.

20. Collecting data for Program Planning Getting access to data is a first step Existing data – Departments of education websites for data reporting New data – Successful college applications – Scholarship support

21. Analyzing Data Data should be entered in a comparable metric Raw data is converted to: – Percentages – Ratios – And/or probabilities Important to use appropriate comparison groups and comparisons across multiple measures

22. Disaggregation of Data Disaggregate by the following groupings: – Gender – Race/ethnicity – Socioeconomic status – Language – Special education placement – English language learner status – Grade level – Achievement quartile – Teacher/classroom

23. Developing Data-Based Action Plans Each plan should contain – Competencies addressed – Description of the activity – Data driving the decision to address the competency – A time line in which the activity is to be completed – Who is responsible for delivery – Means of evaluating student success – Expected results for students

24. Chapter 5 Evaluating School Counseling Interventions and Programs

25. What is Evaluation Evaluation-The use of scientific method (hypothesis testing) to improve local decision making by determining whether it was likely that implementing an intervention resulted in desired changes in behavior and performance. – Used to improve decision making in a single setting Research-The use of scientific method to determine whether an intervention brings about changes in affect, cognition, and /or behavior. – Used to identify practices that are theoretically effective across settings

26. Student-Focused versus System-Focused School Counseling Program Activities

27. Mentoring Students Phone Contact Study Skills Group Small Group Classroom Guidance Behavior Management Bully Proofing Program Tutoring Data Driven Interventions 70% Attendance Rate for Low SES Students Individual Counseling Student Focused

28. Student Focused Interventions Interventions designed to directly help students gain knowledge and skills in the areas of academic, career, and personal/social development in order to help them better navigate the educational system

29. System Focused Interventions Interventions designed to help the system (school) change in order to better meet the needs of the students. Examples: Change educator attitudes, expectations, and priorities Reduce with adult resistance to change Change policy Change practice

30. Activity Scenario: Free/reduced lunch students do not pass Math at the same rate as their non-free/reduced lunch peers. Brainstorm: Student Focused Interventions System Focused Interventions

31. Designing Interventions to Help All Students Meet High Academic Standards

32. Design Student Focused Interventions to Reach All Students

33. Who is Served by the School Counseling Program? ACTIVITY: Who Typically Receives School Counseling Program Services? Do school counselors filter information?

35. Funnel of Continuous Interventions DATA ACTION PLAN All Students Large group/ Classroom Small Group Individual Referral Some Students Few Students A Student

36. Continuum of Interventions to Reach All Students Small Group Interventions Counseling, study group, homework club, etc. For students needing extra help to master indicators School counselor or appropriate professional

37. Continuum of Interventions to Reach All Students Individual Support or Counseling Interventions Counseling, mentoring, etc. For students still needing extra help or support to master indicators School counselor or appropriate professional

38. Continuum of Interventions to Reach All Students Referral/Additional Interventions Mental health agency, medical organization, academic tutoring, community service, etc. For students and their families needing extra, outside help to enable students to master indicators Appropriate professionals

39. Creating Pre and Post Measures

40. Ways to Collect Data Paper/pencil measures – Likert scale – Multiple choice – True/False – Short answer – Fill in the blank Skill demonstration measures – Role play – Demonstration – Presentation – Verbal questions

41. Activity Design Pre and Post Measures

42. Use Data to Monitor Student Progress

43. Paradigm Shift From: To: Monitoring Only Process and Tallying Services Delivered Focusing on Results Tied to the Academic Goals of the School

45. Types of Data Process Perception Results

46. Types of Data Process Perception Results

47. Process Data - Examples Six counseling groups with 8 students each were held 1,350 6-8 th grade students received the “Time to Tell” guidance lesson All high school students seen individually to prepare 4 year plan.

48. Adding Process Data ActivityProcess Data Perception DataResults Data Question or Demonstration Pre Post How will student behavior change? PrePost DateDate # %# % DateDate # %# % DateDate # %# % DateDate # %# %

49. Perception Data “What others think, know or demonstrate” data. Metacognitive Awareness Measures competency achieved, knowledge gained or attitudes beliefs of students – Pre-post – Competency achievement – Surveys – Evaluations Measures what students are perceived to have gained in knowledge

50. Perception Data - Examples Competency Achievement – Every student in grades 9-12 completed a 4 year plan – Every 10 th grade student completed an interest inventory Knowledge Gained – 89% of students demonstrate knowledge of promotion/ retention criteria – 92% can identify Early Warning Signs of violence Attitudes or Beliefs – 74%of students believe fighting is wrong – 29% of parents say their child feels safe at school – 58% of teachers say students behavior appropriately in class

51. Adding Perception Data ActivityProcess Data Perception DataResults Data Question or Demonstration Pre Post How will student behavior change? PrePost DateDate # %# % DateDate # %# % DateDate # %# % DateDate # %# %

52. Results Data “So WHAT” data Response to Intervention or Behavior Responsiveness How has student behavior changed Proof activity has (or has not) positively impacted students ability to utilize the knowledge, attitudes and skills to effect behavior – Attendance – Behavior – Academic achievement

53. Results Data - Examples 42 students on the retention list avoided retentions Graduation rates improved 14% over three years Attendance improved among 9 th grade males by 49%

54. Adding Results Data ActivityProcess Data Perception DataResults Data Question or Demonstration Pre Post How will student behavior change? PrePost DateDate # %# % DateDate # %# % DateDate # %# % DateDate # %# %

55. Activity Fill in the following DATA columns on your Planning Tool Process Data1.What will you count to show what you did? Perception Data1.What question on a survey or test, or what demonstration will use to see if students (or adults) think the student has mastered the indicator? 2.What date will you collect the PRE data? 3.What date will you collect the POST data? Results Data1.What behavior will the students exhibit that will demonstrate they have mastered the indicator? 2.What date will you collect the PRE data? 3.What date will you collect the POST data?

56. Putting These Ideas To Work: Homework 1.With the input of your Advisory Council, faculty and students, complete the design of a continuum of student focused interventions for each of your prioritized indicators 2.Prepare for April training. Complete/revise/ review Essential Learnings Reflection and decide upon systemic change goals (changing policies, and/or practices to help the school better meet student need). Fill in systemic change section on the last page of the today’s Design Tool.