Using Data for Problem Solving and Decision-Making

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Once students have been taught the classroom expectations and decisions have been made regarding what to document, it is important to use data within a problem solving approach for decision making. One evidence-based approach that aligns perfectly with the features of Positive behavior Supports is Team Initiated Problem Solving (TIPS) (reference). The TIPS problem solving approach was developed to assist school teams in using data to (a) define problems, (b) build solutions and transform solutions in practical action plans.

The process involves four steps. First, define the expected outcomes you want to achieve. The core values and behavioral expectations that were previously developed for the classroom and individual routines can be used for your outcomes. For example, if one of your core values is to be responsible and you have defined this as getting to class on time, coming prepared with materials and homework, and starting assigned tasks when asked, you have already developed three important outcomes.

Second, identify measures to monitor the core outcomes. The measures you chose to monitor outcomes can be positively stated as described in Step One or the opposite such as tardy to class, not completing homework or non-compliance. If the measures are positively stated, you need to develop a simple tracking form to gather that information. If the measures involve problem behaviors, then the forms developed previously can be used to collect data.

Third, establish and apply standards for the identified measures. Determining standards for how frequent, how long or what intensity you desire will allow you to set important goals for individual students or the classroom as a whole. For example, one middle school social studies teacher set getting to class on time four of five days a week for a student that was late 3 times a week on average as an initial goal. The same teacher set the standard of 90% of students being on time every day in a week as the class standard.

The last step, collect and use data throughout the process to build solutions and action plans is the most complicated. It begins by reviewing the current status and identifying the problems precisely including what behaviors and the context in which they occur. For example, let’s take starting assigned tasks when asked. It’s not only important to define the problem as student does not start assigned tasks when asked, but also to clarify the problem more precisely as to when (time of day), where (location or classroom routine), who, and why (motivation or function). In an elementary school class, the when might be identified by time of day, particular day of the week or subject/routine. In a middle school period, focusing on which instructional routines the non-compliance occurs would be a good place to start.

The who involves identifying which students exhibit the problem behaviors. The why involves thinking about what the student gains or avoids from the behavior, which is often called the function. Some students gain peer or adult attention. Some avoid work or embarrassment. The result of this information allows you to create a working hypothesis or the best explanation for what the data and your experience tell you. It also guides you to possible solutions. A possible hypothesis for the example above might be: There are 4 students who do not start assigned tasks when asked during social studies period. The noncompliance occurs on Tuesday and Thursdays during the routine of independent mapmaking while I am working with a small group. It is possible this occurs to gain my help with the work they find difficult to complete. Once a workable hypothesis has been determined, you can then select a potential solution. For example, one solution might be to pre-teach the map making skills to the students before giving them the independent assignment. Having a chart of steps to follow at their desk might also help prevent the problem. If we implement these two instructional strategies in an action plan for a week we can then evaluate the effectiveness and revise the pan as needed.


Team Initiated Problem Solving – PBIS

Data Driven Decision Making

Data Driven Decisions to Improve Results





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Using Data for Decision-making

Enhancing Schoolwide Climate and Discipline Using Data for Decision Making and Problem Solving (PP) by Howard Muscott

Muscott, H. S., Pomerleau, T., Park, K. L., Steed, E. A., Frey, A. J., & Korfhage, T. L. (2011). Setting sail for early learning success: Using a data-based decision making process to measure and monitor outcomes in early childhood programs. Kentucky Teacher Education Journal, 1(1), 23-41. 


By Howard Muscott, posted on Monday October 27, 2014


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