Education m7 Assignment
katherine0619
DataManagementPlanPPT-2.pptxData Management Plan for an Ideal School
Group 1:
Katherine Rosales
Deanna Allick
Rashonda Mckinney
Ingrid Amago
Key Meetings & Events Requiring Data
Faculty Meetings (Monthly)
Grade-Level Team Meetings (Weekly)
Professional Learning Communities (PLCs) (Bi-weekly)
Parent-Teacher Conferences (Quarterly)
School Improvement Plan (SIP) Meetings (Quarterly)
Mid-Year and End-of-Year Data Reflection Meetings
Somerset Academy South Miami uses structured meetings to guide decision-making. Faculty meetings focus on school-wide performance trends. Grade-level and PLC meetings concentrate on instructional strategies using student data. SIP meetings and reflection sessions evaluate long-term progress, while parent-teacher conferences incorporate individual student data to support learning at home.
‹#›
Faculty Meetings (Monthly)
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| First Wednesday of each month | Administration, all teachers, support staff | School-wide assessment data, attendance rates, behavior progress toward SIP goals, events and staff of the month. | One week prior to meeting How: Extracted from district systems (e.g., gradebook, assessment platforms) | Student Information System (SIS), assessment dashboards, Google Slides/Sheets |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
Grade-Level Team Meeting Data Plan
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| Weekly (Every Monday) | Grade-level teachers, instructional coach, assistant principal | Student assessment scores (Math & ELA), attendance records, intervention data | Collected from classroom assessments, updated weekly in SIS by teachers | Student Information System (SIS), Data dashboard, Excel tracking sheets |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
Professional Learning Communities (PLCs) (Bi-weekly)
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| Every other week | Teachers, instructional coach, sometimes admin | Benchmark data, common assessment results, intervention data | When: After each assessment cycle How: Uploaded into shared data platforms | Data dashboards, Google Drive, assessment systems |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
Parent-Teacher Conferences (Quarterly)
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| End of each quarter | Teachers, parents/guardians, sometimes students | Report cards, assessment scores, behavior records, work samples | When: 1–2 weeks before conferences How: Pulled from gradebook and student portfolios | Gradebook system, printed reports, student folders |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
School Improvement Plan (SIP) Meetings (Quarterly)
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| Quarterly | Administration, leadership team, department/ grade representatives | State assessment data, benchmark data, attendance, discipline, subgroup performance | When: After major testing windows How: District reports, state databases | District data systems, spreadsheets, presentation tools |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
School Improvement Plan (SIP) Meetings for Leadership Members (Beginning of the year)
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| Beginning of the year | Administration, leadership team, department/ grade representatives | State assessment data, benchmark data, attendance, discipline, subgroup performance | When: Previous year major testing windows PM3 FAST Assessment, and Attendance Data How: District reports, state databases, Attendance quarterly results. | District data systems, spreadsheets, presentation tools |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
Mid-Year & End-of-Year Data Reflection Meetings
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| January (Mid-Year), May/June (End-of-Year) | Administration, all teachers, support staff | Mid-year benchmarks, final assessments, growth data, intervention effectiveness | When: Immediately after testing windows How: multiple data systems such as FAST, portfolio, and Iready Diagnostic | Data dashboards, spreadsheets, reporting tools |
Grade-level meetings occur weekly and focus on reviewing student performance in Math and ELA, where proficiency has increased from 87% to 96% in Math and 83% to 90% in reading. Teachers input assessment and attendance data into the SIS weekly. This data is then analyzed collaboratively to identify struggling students and adjust instruction. The use of dashboards allows quick visualization of trends and supports timely interventions.
‹#›
EESAC Meeting (Quarterly)
| Date/Time | Participants | Data Needed | Data Source & Collection | Tools/Software |
| Quarterly: 4 times a year | Principal, A.P., EESAC members (teachers, parents, students | Discuss budget and student needs, review SIP, involve stakeholders | Attendance Manager, MDCPS School Profile | Budget, attendance report, SIP, Title I information, academic needs |
‹#›
Data Required Across School Events
Academic Data: Math and Reading proficiency trends (2022–2025)
Demographic Data: Enrollment, ELL (9%), SWD (13%), economically disadvantaged (43%)
Attendance Data: 95–96% attendance rates, 4% chronic absenteeism
Behavior Data: Suspension rates reduced from 2% to 1%
Perception Data: School climate survey results (90%+ satisfaction)
Data used across meetings reflects the school’s performance and environment. Academic data shows strong growth trends. Demographic data highlights the need for culturally responsive teaching and targeted support. Attendance and behavior data indicate improved engagement, while climate survey results confirm a positive learning environment.
‹#›
Data Collection & Availability Timeline
State assessment data collected annually (testing windows)
Classroom assessment data collected weekly
Attendance and behavior data recorded daily
Climate survey data collected annually
Data uploaded and updated continuously in SIS
Data collection follows a structured timeline. State assessments provide yearly benchmarks, while classroom data is collected weekly to monitor progress. Attendance and behavior data are recorded daily, ensuring real-time tracking. Climate surveys are conducted annually to assess stakeholder perceptions. All data is stored in centralized systems for easy access.
‹#›
Data Gathering Process & Responsibilities
Teachers: Collect and input academic and classroom data
Administrators: Monitor school-wide trends and ensure accountability
Data Teams/PLCs: Analyze and interpret data
IT Staff: Maintain data systems and dashboards
Data management is a shared responsibility. Teachers play a key role in collecting and entering data. Administrators oversee trends and ensure alignment with school goals. Data teams analyze patterns during PLC meetings, and IT staff ensure that systems are functional and accessible for all users.
‹#›
Training Needs for Effective Data Use
Training on Student Information Systems (SIS)
Data analysis and interpretation skills
Data-driven instructional strategies
Ongoing professional development through PLCs
Effective use of data requires continuous training. Teachers must understand how to interpret student performance data and apply it to instruction. Training on SIS and dashboards ensures accurate data use. Ongoing professional development strengthens collaboration and improves instructional practices.
‹#›
Data Management Plan for an Ideal School
Centralized data system integrating all data sources
Regular data review cycles (weekly, monthly, quarterly)
Focus on actionable and relevant data only
Elimination of redundant tools and processes
Strong collaboration through PLCs and leadership teams
The ideal school uses a centralized system where all data is accessible in one place. Regular review cycles ensure that data is consistently used to guide decisions. Only meaningful data is prioritized, eliminating unnecessary tools. Collaboration among staff ensures that data leads to improved teaching and learning outcomes.
‹#›
Timeline, Monitoring & Implementation
Weekly: Grade-level and PLC data review
Monthly: Faculty meetings to review trends
Quarterly: SIP reviews and parent conferences
Mid-Year & End-Year: Comprehensive data evaluation
Continuous monitoring through dashboards and leadership oversight
Implementation of the data management plan follows a structured timeline. Weekly reviews support immediate instructional adjustments, while monthly and quarterly meetings focus on broader trends. Mid-year and end-of-year evaluations assess overall progress. Continuous monitoring ensures accountability and sustained improvement.
‹#›
Proficiency Rates in Math and Reading (2022–2025)
| Academic Year | Math Proficiency | Reading/ELA Proficiency |
| 2022–23 | 87% | 83% |
| 2023–24 | 90% | 86% |
| 2024–25 | 96% | 90% |
‹#›
Attendance, Absenteeism, and Suspension Trends (2022–2025)
‹#›
References
Bingham, A. J. (2023). From data management to actionable findings: A five-phase process of qualitative data analysis. International journal of qualitative methods, 22, 16094069231183620. https://doi.org/10.1177/16094069231183620
Poom-Valickis, K., Eisenschmidt, E., & Leppiman, A. (2022). Creating and developing a collaborative and learning-centred school culture: Views of Estonian school leaders. Center for Educational Policy Studies Journal, 12(2), 217-237. https://doi.org/10.26529/cepsj.1029
Smith, L. B. (2024). The Impact of a Data-Driven Continuous Cycle of School Improvement Process on Student Growth (Doctoral dissertation, University of St. Francis).
Zong, Z., & Guan, Y. (2025). AI-driven intelligent data analytics and predictive analysis in Industry 4.0: Transforming knowledge, innovation, and efficiency. Journal of the knowledge economy, 16(1), 864-903. https://doi.org/10.1007/s13132-024-02001-z
