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Enhancing Data-Driven Decision-Making Across Educational Levels: A Comprehensive Analysis

This action research investigates data-driven decision-making across four educational levels: Central Office, High School, Middle School, and Elementary School. It examines the effectiveness of demographic, perception, student learning, and school process data in informing decisions. Findings reveal that while demographic data is utilized, there is inconsistency in the use of perception and student learning data across levels. Recommendations include improving access to data for teachers, enhancing training for stakeholders, and ensuring a more integrated approach to data usage for effective programming and funding.

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Enhancing Data-Driven Decision-Making Across Educational Levels: A Comprehensive Analysis

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  1. Data Driven Decision-Making:Cross-level investigation Amy Ford Fritz Geissler Phyllis Harris Christian Nolde

  2. Process for action-research: • Information collected across four levels: • Central Office • High School • Middle School • Elementary School • Information collected cross three areas: • School counseling • Department leader • Administration

  3. Four types of data in question: • Demographics • Perceptions • Student learning • School process • *is this present in practice?*

  4. Central Office Present in practice: Room for growth: • Demographics data consistently used across areas but not at teacher level. • Perceptions data used at school and division level but not at lead level. • Student learning data for the purposed of funding should be more thoroughly explained to stakeholders; more training needed. • Some data pulled in isolation & not used across areas for full programming benefit • Demographics data pulled across all areas • Perceptions data not consistently used across levels • Student learning data used across areas for a variety of purposes; all areas reported use of information for funding of programs • School process data use was inconsistent • At director level role was as a data source, not interpreter or for implementation

  5. High School Present in practice: Room for growth: • Demographic data not mentioned often across all levels as a frequently cited source. • CO disaggregates data taking a lot of this work away from school staff. • Combining other data sources with perception data to produce a deeper level of analysis. • Demographic Data: • Used to identify subgroups in closing Achievement Gap and in perception surveys. • Attendance, health, and contact information are collected multiple times throughout the year. • Perception Data: • Major driver of CSIP as consistent trends have emerged for improvement. • Taken yearly from school staff, parents, and students.

  6. High School Present in practice Room for growth • Teachers do not receive access to all learning data, though this is starting changing with school’s CARR giving more in depth explanations to core subject areas. • Ability to get as much information on student learning as needed from CO. • Specific measures of classroom data could be more exactly and frequently measured across all classrooms. • Student learning data: • Used across all levels consistently in multiple ways- line analysis of test responses, program development and retention of program, curriculum driver, CSIP. • SOL, AP, SAT, Exams, & Discipline (main) • School Processes: • Consistently used across all levels. • Teaching strategies, counseling programs, and CSIP results are measured. • Combined often with other data sources, usually student learning.

  7. Middle School Present in practice: Room for growth: • Demographic data is consistently used. • Used largely by counseling and administration. Not used or evaluated by leads. • Demographic data: • Compiled and used in relation to student learning data: • Attendance • Subgroups • Funding • Perception data: • Within School: • Surveys (counseling) • Individual meetings with teachers • Outside of school: • Online surveys (infrequently used)

  8. Middle School Present in practice: Room for growth: • Used by administration, counseling, and leads. Could improve in used of data to evaluate decisions to monitor progress and inform future practice (in connection with School Processes) • Could be used more in overall functioning of the school. Many processes are set and maintained over the course of the year. Could revisit more frequently. • Student Learning: • Used to determine resource distribution, remediation/tutoring, instruction, and class groupings • NWEA, SOL, Common Assessments, D/F report, Pre and post tests • School Processes • Effectiveness of counseling curriculum • Dress code and tardy information • Lunch seating (joint decision with students) • Administrators used data across all 4 types as did school counseling. Leads typically focused on student learning data to inform instruction and remediation.

  9. Elementary School Room for growth: Present in practice:

  10. Reasons for analyzing school data Funding, state reporting, program development and teacher/student growth consistent across all levels and all areas.

  11. Leadership styles noted:

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