Training Course on Formative and Summative Assessment Data Analysis

Training Course on Formative and Summative Assessment Data Analysis

Introduction

In today’s data-driven educational landscape, the ability to effectively analyze formative and summative assessment data is critical for driving student achievement and improving instructional strategies. Training Course on Formative and Summative Assessment Data Analysis equips educators, instructional leaders, and school administrators with advanced skills in educational data analysis, data-driven decision-making, and performance evaluation using evidence-based practices. Participants will explore how to collect, interpret, and apply assessment data to personalize learning, monitor student progress, and align instructional planning with learning outcomes.

With a strong emphasis on real-time analytics, data visualization, and educational technology integration, this course bridges the gap between raw data and actionable insights. By the end of the training, attendees will be able to confidently leverage both formative assessments (ongoing assessments to inform instruction) and summative assessments (end-of-instruction evaluations) to enhance academic performance, support teacher development, and meet institutional goals for continuous improvement and accountability.

Course Objectives

1. Understand the differences between formative and summative assessments.
2. Apply data-driven decision-making in instructional planning.
3. Analyze student performance data using assessment tools.
4. Use data dashboards for real-time academic insights.
5. Create visual reports from assessment data for stakeholders.
6. Integrate learning analytics with curriculum adjustments.
7. Develop strategies to close achievement gaps.
8. Align assessments with learning outcomes and standards.
9. Utilize AI-powered assessment tools effectively.
10. Monitor student growth using longitudinal data.
11. Promote teacher collaboration using shared data insights.
12. Evaluate instructional effectiveness through data trends.
13. Establish assessment literacy among educators and leaders.

Target Audience

1. Classroom Teachers (K-12)
2. School Principals and Vice Principals
3. Instructional Coaches
4. Curriculum Coordinators
5. District Administrators
6. Special Education Professionals
7. Educational Data Analysts
8. Teacher Education Faculty

Course Duration: 10 days

Course Modules

Module 1: Introduction to Assessment Types

• Define formative and summative assessments
• Explore purposes of each assessment type
• Discuss advantages and limitations
• Understand assessment timing and frequency
• Identify stakeholders in the assessment process
• Case Study: Assessment Misalignment in Middle School Math

Module 2: Principles of Data-Driven Instruction

• Define data-driven instruction
• Align instruction with assessment results
• Set SMART goals using data
• Identify key performance indicators (KPIs)
• Create feedback loops for continuous improvement
• Case Study: Using Data to Transform Instruction in Urban Schools

Module 3: Designing Effective Formative Assessments

• Characteristics of effective formative assessments
• Types of formative tools (exit tickets, quizzes, journals)
• Student self-assessment and peer assessment
• Use of digital formative assessment platforms
• Linking formative assessment to instructional next steps
• Case Study: Real-Time Feedback in a Science Classroom

Module 4: Designing Summative Assessments

• Align assessments to learning standards
• Balance objective and subjective assessment formats
• Create rubrics for performance-based assessments
• Address test reliability and validity
• Prevent assessment bias and inequity
• Case Study: Standardized Test Revisions in High Schools

Module 5: Collecting and Organizing Data

• Set up digital assessment platforms
• Use spreadsheets and LMS exports
• Organize data by standards and benchmarks
• Track student growth over time
• Conduct error and trend analysis
• Case Study: Data Disaggregation in English Language Learning

Module 6: Analyzing Quantitative Data

• Understand descriptive statistics (mean, median, mode)
• Use frequency distribution and histograms
• Apply item analysis to test questions
• Analyze subgroup performance
• Leverage statistical software (Excel, SPSS)
• Case Study: Equity Gap Analysis in Reading Performance

Module 7: Analyzing Qualitative Data

• Collect open-ended student feedback
• Conduct coding and theme development
• Integrate teacher reflections and observational data
• Use student work samples as evidence
• Combine qualitative with quantitative data
• Case Study: Mixed-Methods Analysis in Project-Based Learning

Module 8: Visualizing Data for Impact

• Select appropriate graphs and charts
• Use dashboards to monitor performance
• Share data with students and families
• Develop data presentations for stakeholders
• Customize reports by subject/grade
• Case Study: Interactive Dashboard Use in Elementary School

Module 9: Using Technology in Data Analysis

• Use Google Forms and Sheets for quick analysis
• Integrate LMS with data platforms
• Analyze data with AI tools
• Automate feedback and progress reports
• Ensure data security and privacy
• Case Study: Leveraging EdTech in a District-Wide Rollout

Module 10: Data-Driven Instructional Planning

• Identify student learning needs
• Group students based on performance
• Adjust pacing and differentiation strategies
• Plan targeted interventions
• Monitor plan implementation effectiveness
• Case Study: Response to Intervention (RTI) Based on Data

Module 11: Building Assessment Literacy

• Define assessment literacy
• Build shared vocabulary among staff
• Develop data interpretation skills
• Provide professional development on data use
• Foster a culture of collaboration
• Case Study: PLCs and Data Use in a High-Performing School

Module 12: Promoting Equity Through Data

• Identify inequities in assessment outcomes
• Support culturally responsive assessment
• Adjust practices to ensure fairness
• Use data to inform equitable policies
• Train educators on equity-centered analysis
• Case Study: Closing the Opportunity Gap Using Data

Module 13: Monitoring and Reporting Student Growth

• Track growth targets by semester/year
• Use growth percentile and SGPs
• Report data transparently to families
• Compare growth across cohorts
• Design student-led data conferences
• Case Study: Longitudinal Tracking of Math Growth

Module 14: Enhancing Teacher Effectiveness with Data

• Use evaluations linked to student data
• Facilitate teacher self-assessment using metrics
• Conduct classroom walkthroughs with data focus
• Use coaching tied to assessment results
• Create teacher data portfolios
• Case Study: Improving Instructional Practices via Coaching

Module 15: Creating a Data-Informed School Culture

• Involve all staff in data conversations
• Hold regular data meetings
• Recognize and celebrate data-driven success
• Align school improvement plans with data findings
• Foster trust in transparent data use
• Case Study: Building a Data Culture in a Title I School

Training Methodology

• Interactive workshops with hands-on data exercises
• Real-world case studies and scenario simulations
• Group discussions and collaborative data analysis
• Use of digital tools for live data interpretation
• Ongoing assessments and reflection activities
• Access to post-training resources and templates

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LD account, as indicated in the invoice so as to enable us prepare better for you.