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Using Data in School Counseling Programs. By Katie Ackerman, Karla DeCoster, Lisa Lyke. Thinking About Data. 1.) As a teacher, how do/did you assess the success of your      lesson? 2.) As counselors, why is data collection important? 

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Using data in school counseling programs

Using Data in School Counseling Programs

By Katie Ackerman, Karla DeCoster, Lisa Lyke


Thinking about data
Thinking About Data

1.) As a teacher, how do/did you assess the success of your

     lesson?

2.) As counselors, why is data collection important? 

    A.) So we can be utilized to best fulfill students' needs

    B.) To show others how we can make a difference in

         students' lives 

                                                                                           Resource: ASCA (2008)


The engine of accountability
The "Engine" of Accountability

Using data means we must 

    1.) collect data, which is crucial to decision-making

    2.) analyze data

    3.) make decisions that are guided by data

    4.) show results for accountability and evaluation

Source: Isaacs, M.L. (April 2003)


Resistence to data
Resistence to Data

1.) Using and understanding data is time-consuming.

2.) Some counselors do not possess the self-     efficacy needed to carry out the process. Some

     dispositions of counselors included the following:

     A.) general self-efficacy

     B.) school counselor self-efficacy

     C.) commitment to counseling improvement

     D.) openness to change

     E.) years of experience 

Sources: Holcomb-McCoy, C., Gonzalez, I., & Johnston, G. (June 2009) 


How to help
How to Help

1.) increase counselor self-efficacy and help them to see

    the time spent will benefit their program

2.) provide training and opportunities for them to practice

     learning

3.) provide situations for observing and assisting others with

     utilization of data 

4.) provide time for them to discuss data

5.) provide time for them to work on the process (collect,

     analyze and evaluate data)

6.) provide them resources and any assistance they may

     need

                      Sources: Holcomb-McCoy, C., Gonzalez, I., & Johnston, G. (June 2009)


Types of data
Types of Data

1) Process Data

        a) Demographics

        b) # of interventions

        c) discipline referrals

2) Perception Data

        a)School climate survey

        b)  Survey of students, parents, and administrators

3)Results Data

        a) Homework Completion

        b) GPA

Source: ASCA 2008


How to collect data
How to Collect Data

1) Interviews

2) Parent/Faculty/ Student Surveys

3) Student Records

4) Outside Community Resources 

        Ex: Department of Health sees an increase in STDs in teenagers

Source: Gysbers, N. 2006


Resources that can help organize your data
Resources that can help organize your data

  • Microsoft Excel spreadsheet or a similar program

  • Online source like ezanalyze or google docs

  • If necessary contact a local college and enlist the help of a graduate student in statistics and school counseling

    Source: Isaacs 2003


What data can tell us
What Data Can Tell Us

1) Effectiveness

    a) Entire Program

    b) Use of time and resources

    c) Intervention

2) Areas of need

    a)Academic progress

    b)Barriers

    c)Achievement gaps

    d) Interventions

Resource:Dimmitt, C 2003, Hayes, R L et. al. 2002


How to use data
How to Use Data

1) To make purposeful, consistent, and defendable decisions

2) To Plan

3) To implement changes

4) After the implementation re-evaluate

4) Communicate with stakeholders

-Goals & Priorities

    -Benefits of program

    -Value of each dollar spent on program

    -High Expectations

    -Give others reason to support your efforts

5) To allocate resources

6) Make referrals 

 Sources: Dimmitt 2003; Isaacs, 2003; Holcomb-McCoy et. al., 2009;

Kaffenberger & Davis, 2009; Young et. al., 2009


Ideas model of dbdm
IDEAS Model of DBDM

Stage 1: Identify a Question

    Task 1: Form a DBDM Team

    Task 2: Identify a goal to address

    Task 3: Collect and analyze data

Stage 2: Develop a Plan

    Task 1: Identify barriers to the goal

    Task 2: Create/choose an evidenced-based intervention

    Task 3: Develop an action plan

    Task 4: Develop an evaluation plan

 Sources: Poynton & Carey, 2006


Ideas model of dbdm continued
IDEAS Model of DBDM (continued...)

Stage 3: Execute the Plan

    Task 1: Begin implementation

    Task 2: Monitor Implementation

    Task 3: Assess as you go and make adjustments

Stage 4: Answer the Question

    Task 1: Analyze new data

    Task 2: Interpret results to see if you met goal

Stage 5: Share Results

Sources: Poynton & Carey, 2006


Activity
Activity

  • For our activity we are going to carry out the first two stages of the IDEAS model

    Stage 1: Identify a Question

        1) Divide into DBDM teams

        2) We are going to focus on the following goal:

    to maximize the academic development

    of every student

        3) Analyze the results of the state assessment

    (to keep the activity simple we are only looking at one set of data.

    In real life you will want to use multiple sources of data)

        4) Now, write your question


Activity continued
Activity (continued...)

Stage 2: Develop a Plan

    1) Looking at the results, what areas are a barrier to our

    goal?

    2) Create an intervention that could address this barrier

    to help us meet our goal

    3) Outline a plan to implement the intervention

    4) Outline a plan you would use to evaluate the

        intervention


Sources
Sources

American School Counseling Association. (2008). ASCA national model:Use of data. Retrieved

    from: http://ascanationalmodel.org/content.asp?pl=21&sl=24&contentid=24Camizzi, E., Clark, M.A., Yacco, S., & Goodman, W. (August 2009). Becoming 'difference makers'

    school-university collaboration to create, implement, and evaluate data-driven counseling

    interventions. Professional School Counseling, 12. p. 471-479.

Dimmitt, C. (June 2003). Transforming school counseling practice through collaboration and the

    use of data: a study of academic faliure high school. Professional School Counseling. 6. p. 340-

    349.

Gysbers,N. (2006). Developing & managing: Your school guidance and counseling program (4th ed). American Counseling Association.

Hayes, R.L., Nelson, J-L., Tabin, M., Pearson, G., & Worthy, C. (Dec 2002). Using school-wide

    data to advocate for student success. Professional School Counseling.6. p. 86-94.

Holcomb-McCoy, C., Gonzalez, I., & Johnston, G. (June 2009). School counselor dispositions as

    predictors of data usage. Professional School Counseling. 12. p. 343-351.

Isaacs, M.L. (April 2003). Data-driven decision making: The engine of accountability.

    Professional School Counseling. 12. p. 288-295.

Kaffenberger, C., & Davis, T. (August 2009). Introduction to special issue: a call for

    practitioner research. Professional School Counseling.12. p. 392-394.

Poynton, T.A., & Carey, J.C. (Dec 2006). An integrative model of data-based decision making

    for school counseling. Professional School Counseling. 10. p. 121-130.

Young, A., Hardy, V., Hamilton, C., Biernesser, K. Sun, L.L., & Neibergall, S. (August 2009).

    Empowering sents: using data to transform a bullying prevention and intervention program.

    Professional School Counseling. 12. p. 413-420.


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