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Evidence Based School Counseling

Evidence Based School Counseling. Chapter 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. Definitions.

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Evidence Based School Counseling

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  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 dataChapter 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-FocusedSchool Counseling Program Activities

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

  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?

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

  35. 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

  36. 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

  37. 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

  38. Creating Pre and Post Measures

  39. 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

  40. Activity Design Pre and Post Measures

  41. Use Data to MonitorStudent Progress

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

  43. Types of Data • Process • Perception • Results

  44. Types of Data • Process • Perception • Results

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

  46. Adding Process Data

  47. 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

  48. Perception Data - Examples • Competency Achievement • Every student in grades 9-12 completed a 4 year plan • Every 10th 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

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