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Data based decision making is a common term heard in the education of students and students with disabilities, but the process is still difficult to understand and implement daily for many educators. With an abundance of methods to choose from educators often do not know which is the best to use. When it comes to working with students with disabilities data collection is the foundation needed for students to succeed to their greatest potential. This poses the questions: What is Data, why do educators need to look at data, what types of data are there, and how will it benefit students learning outcome?
What is Data-Based
Data is facts and statistics collected together for reference or analysis. There are many forms of data used in the world today; growth in population, politics, and environmental issues are just a few, but when the tern is used in the context of schools; data is information that is collected and organized to represent some aspect of schools (Schildkamp, Mei Kuin, and Lorna, 2012). The aspects of school could be how a student does on a test, behavior of a student, or classroom observations by a teacher or administrator. By using “snap shots” of information this allows the educator to give unbiased data of the student.
Data-Based Decision Making
Data collection forms the basis for evaluating and informing student learning outcomes and the effectiveness of teaching practices (Brawley & Stormont, 2013). The data-based decision making movement began with educational reform efforts such as the Every Student Succeeds Act (U.S. Department of Education, 2009) and federal legislation such as the Individuals With Disabilities Education Improvement Act (2004), commonly referred to as IDEA, according to Ruble, McGrew, Wong, & Missell, (2018.) With these laws in place educators were pushed to provide unbiased evidence on the educational growth on their students.
Different Types of Data
When working with students with disabilities educators must decide what type of data will be most suitable to the student’s needs. (Schildkamp, et al.) provided several different types of data that can be used by educators.
- Data on student characteristic, such as data on truancy, intake, transfer and school leaves, home language, ethnicity, and socioeconomic status.
- Data on teacher characteristics, such data on teacher qualifications and length of teaching.
- Data on student achievement, such as assessment results, written and oral exams, portfolios, and report cards.
- Data on student well-being, such as well-being surveys.
- Data on instruction and types of assessment, such as observations and documents on instruction and learning strategies, instruction time, organization of instruction, classroom management and organization of assessment.
Outcome data is the most frequently used form by special education teachers to see the progression of their students educationally growth. Input data is also used but more for the aspect of behavior or time in school, which can have an effect on a student’s education.
Another common form of data collection is through Response to Intervention (RTI). “RTI is the practice of providing high-quality instruction and intervention matched to student need, monitoring progress frequently to make decisions about change in instruction or goals and applying child response data to important educational decisions.” (Broxterman and Whalen, 2013) When using RTI educators are working on a team collaboratively to provide the student with the best resources needed.
While teachers have the benefit of standardized assessment and curriculum-based assessment to collect data on students, special education teachers do not. According to (Ruble, et al.) “special education teachers must document progress on individualized education program (IEP) goals. The IEP goals are unique for each student so an idiographic approach is needed. What this means for the teacher is that each student will need individualized data taken to meet their IEP goals.” According to (Ruble, et al.) a qualitative analysis identified that the majority of the teachers (48%) reported that the reason to collect data is for progress monitoring of IEP goals. For instance, teachers reported that data are used to “determine what a child knows and what to work on next” and to “track progress.”
Evaluation Design (AB Design)
“A design commonly used in applied settings is the AB design. In this design, child performance data are collected before the start of an intervention and are shown in the baseline phase, or Phase A, the first phase of an AB design (Kazdin, 1982). Data collected under intervention conditions are depicted in Phase B, the second phase, of the AB design. The effect of an intervention can be determined by evaluating changes in behavior after an intervention is introduced relative to the behavior before the intervention, or by comparing data in Phase A to data in Phase B. For educators, the AB design has particular appeal because it is easy to implement and interpret, and because it mirrors common informal evaluation practice in educational settings. That is, data are collected on a target behavior, an intervention occurs, and data are collected again.” Gischlar, K., Hojnoski, R., &Missal, K. (2009) The AB design is a visual graph which makes it easier to see the results of the intervention and if the student has made progress. For example: Dillon is an 8-year-old boy who is on the Autism Spectrum and struggles with verbalizing his wants and needs. His teacher begins using picture cards to help him in the classroom. After a few weeks she can go back and look at the data collected to see if Dillon is able to communicate using the picture cards.
Benefits to Special Education Students
Special education students are required to have an IEP, Individualized Educational Program, this allows the teacher to already have a goal in mind for this student. The teacher can then create a plan to help the student meet that goal. Using data along the way allows the teacher to see the growth of the student or if there is not growth to come up with an alternate method of instruction. Teachers must consider student needs and values. As teachers adapt, select, and/or develop individualized interventions, student needs and values should be a primary consideration (Cook et al., 2012; Detrich et al., 2009). Teachers should consider where the student is presently, where the student is going, and how they are going to get the student there (Mazzotti,V., Rowe, D., & Test, D., 2012).
Although Data based decision making may seen overwhelming at first, it is not. When looking at the education of students with disabilities it is a necessity to use in their growth and development. Frequently analyzing the data will help teachers maintain the best educational goal needed for students with disabilities to succeed.
- Brawley, S., Stormont, M. A. (2013). Investigating reported data practices in early childhood: An exploratory study. Journal of Positive Behavior Interventions, 14, 1-10. doi:10.1177/1098300713480838
- Broxterman, Kelly., and Angela J. Whalen. RTI Team Building: Effective Collaboration an Data-based Decision Making. New York: Guilford Publications, 2013. EBooks on Demand. Web.
- Gischlar, Karen L, Robin L Hojnoski, and Kristen N Missall. “Improving Child Outcomes with Data-Based Decision Making: Interpreting and Using Data.” Young Exceptional Children 13.1 (2009): 2-18. Web.
- Ledford, Jennifer R, Erin E Barton, Jessica K Hardy, Katie Elam, Jordan Seabolt, Meredith Shanks, M. L Hemmeter, and Ann Kaiser. “What Equivocal Data from Single Case Comparison Studies Reveal About Evidence-Based Practices in Early Childhood Special Education.” Journal of Early Intervention 38.2 (2016): 79-91. Web.
- Mazzotti, Valerie L, Dawn R Rowe, and David W Test. “Navigating the Evidence-Based Practice Maze: Resources for Teachers of Secondary Students with Disabilities.” Intervention in School and Clinic 48.3 (2013): 159-66. Web.
- Ruble, Lisa A, John H McGrew, Wing Hang Wong, and Kristen N Missall. “Special Education Teachers’ Perceptions and Intentions Toward Data Collection.” Journal of Early Intervention 40.2 (2018): 177-91. Web.
- Schildkamp, Kim., Mei Kuin. Lai, and Lorna, Earl. Data-based Decision Making in Education: Challenges and Opportunities. Dordrecht: Springer, 2012. EBooks on Demand. Web
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