Student engagement is the bridge between academic achievement and appropriate student behavior. It is often viewed as a student trait; however, it is multi-dimensional and significantly impacted by instructional, curricular, and environmental variables.
School attendance includes whole school days, tardies, and early dismissals. In order to build an efficient and effective intervention system for attendance, universal attendance strategies and practices based on research should be utilized. The handout on the right provides recommendations.
The following should be taught to both staff and students-
Social and emotional learning (SEL) is the process through which children and adults understand and manage emotions, set and achieve positive goals, feel and show empathy for others, establish and maintain positive relationships, and make responsible decisions. (CASEL.org)
It is important for teams to establish a common language and understanding of social-emotional learning practices within an MTSS, and this tool can be utilized for team discussions and planning.
When teams begin the work of defining Core behavior practices, the primary focus should be around the adult routines that contribute to strong classroom management and promote positive student behavior. This following resource can be utilized by teams as an outline to begin this work. Please refer to the Positive Behavior Intervention and Support (PBIS) page for additional information.
Before planning the system of interventions, the team should look for the presence of all of the following in order to form a solid foundation of supports for all:
· Staff commitment to managing behavior
· Universal attendance policies that positively impact school attendance
· Fidelity of Tier One/Core/Universal support implementation
o Clearly defined and communicated expectations and rules
o Clearly defined consequences for unwanted behavior
o System of instruction for teaching students desired behaviors
o Procedures for acknowledging appropriate behavior and good attendance/improved attendance
HCS MTSS
Attendance Roadmap
Although many behavior and attendance interventions may not require a specified time during the school day to meet for instruction of students (with the exception of Social Skill Groups), teams will wish to ensure that the adults specified to support students with behavior and attendance difficulties are available at the times specified. For example, if designing a supplemental Check In-Check Out program for a group of students is an option the school team undertakes, ensure that the morning and afternoon check in/check out staff member is available at those times. For social-skill groups, teams may wish to use some intervention times already built into the school schedule or other times during the school day.
What are data decision rules and what questions are they meant to answer? Data decision rules are formal procedures that inform our actions around data. We set data decision rules to provide a framework for our teams and practitioners to interpret data. Schools and districts will need to design their own rules based on best practice and their unique circumstances. When deciding on your data decision rules for behavior, social-emotional learning, and attendance you will need to consider the following questions:
1. How will we determine if our Core support is effective for our population?
2. How will we decide if students are at risk?
3. How will we address student risk- Core, Supplemental or Intensive?
4. How will we know Core support changes are working for our population?
5. How will we determine that our interventions and intervention systems are effective?
6. How will we determine if students receiving intervention are progressing?
For students receiving interventions for behavioral and/or social-emotional skills, progress on acquiring the skills taught should be monitored in a systematic way. The measures utilized depend on the focus of the intervention. As with progress-monitoring academic skills, monitoring of behavioral and social-emotional skills should utilize repeated measures of performance over time represented graphically.