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Worthington presentation: data use, MTSS and attendance measures guide interventions for chronically absent students
Summary
Academic coordinators outlined how Worthington uses attendance, universal screeners, NCE/cohort analysis and MTSS meetings (elementary MTSS three times a year) to identify students who need targeted interventions; the district's 2023-24 report card shows 13.8 percent chronically absent (about 1,400 students).
At a presentation the district's academic coordinators described how multiple data sources are combined to inform instructional decisions and early interventions.
Tom Kaczmarek and Brian Genius introduced a framework of "triangulation," using classroom common assessments, MAP scores and state Ohio State Test data (OST) to build cohort views and NCE scores that reveal long-term growth patterns. The presenters said these views let teachers and MTSS teams detect downward trends that indicate students are getting less-than-expected growth and to design targeted interventions.
The presenters highlighted chronic absenteeism: "Chronically absent is defined by the Department of Education as missing 10% of possible attendance hours, for any reason," a presenter said, and then noted the district reported 13.8 percent chronically absent in the 2023-24 report card, roughly 1,400 students. They said the district uses Infinite Campus and summary charts to help teachers and building leadership teams see attendance patterns and intervene earlier.
Kaczmarek and Genius explained MTSS processes at the elementary level, including that buildings hold MTSS meetings three times a year and that teachers participate in the meetings to identify needs and apply interventions such as foundational numeracy supports or reading interventions linked to specific subskills. Board members asked how teachers escalate concerns between scheduled MTSS meetings; the presenters said interventions and coach support can begin whenever a teacher identifies a need.
Board members praised the presentation and asked follow-up questions about bias-checking and how cohort and item-level data help place students on the right interventions or accelerate learning for cusp students. The presentation closed with an offer to answer further implementation questions in subsequent meetings.
