Power Of Data-Driven Teaching In Schools

Overcoming challenges and making learning visible

What you'll learn

Introduction to Data Driven Teaching in Schools

Understanding the Approach

Initiating the approach and implementation

Adopting the data driven practices

Enhancing personalised learning

Informed decision making

Driving continuous challenges

Requirements

Understanding and implementing the data driven approaches

Description

Module 1:Understanding Data-Driven Teaching in Schools:Learning Objectives:Define data-driven teaching and its significance.Understand the types of educational data available (academic, behavioural, attendance).Topics:The role of data in modern education.Overview of critical data sources in schools.Activities:Reflection: Assess your school’s current use of data.Case Study: How a school improved outcomes using data.Module 2: Collecting and Organizing DataLearning Objectives:Learn methods to collect meaningful and accurate data.Organize data effectively for analysis and application.Topics:Data collection tools and techniques (surveys, LMS, assessments).We are ensuring ethical data collection practices.Activities:Hands-on: Using data management tools (Google Sheets, Excel, or SIS).Discussion: Identifying data gaps in your institution.Module 3: Analyzing Data for InsightsLearning Objectives:Develop skills to interpret and analyze educational data.Use data visualization tools to identify trends and patterns.Topics:Key metrics for student performance and teacher effectiveness.Introduction to data visualization software.Activities:Workshop: Create a dashboard to track student performance.Group Exercise: Interpret data to suggest actionable insights.Module 4: Applying Data to InstructionLearning Objectives:Learn to use data for personalized instruction and intervention.Understand strategies to differentiate teaching based on data.Topics:Designing data-informed lesson plans.Using formative assessments to guide teaching.Activities:Role-Play: Customizing a lesson for diverse learners using data.Case Study: Successful intervention strategies.Module 5: Driving Equity Through DataLearning Objectives:Use data to identify and address inequities in education.Develop strategies for inclusive teaching.Topics:Recognizing patterns of inequity using data.Implementing targeted interventions for underserved groups.Activities:Discussion: Addressing unconscious bias with data.Action Plan: Develop an equity-focused strategy for your school.Module 6: Building a Data-Driven CultureLearning Objectives:Foster a school-wide commitment to data-informed decision-making.Train staff and stakeholders to use data effectively.Topics:Leadership’s role in promoting data use.Overcoming resistance to data-driven practices.Activities:Simulation: Leading a data-driven staff meeting.Workshop: Designing a professional development session on data use.Module 7: Tools and Technology for Data-Driven TeachingLearning Objectives:Explore technology tools that support data collection and analysis.Leverage AI and EdTech for predictive insights.Topics:Overview of Learning Management Systems (LMS).Introduction to AI in education analytics.Activities:Demo: Exploring EdTech tools like Power BI, Tableau, and AI platforms.Lab: Automating data reporting.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Collecting and Organizing Data

Lecture 3 Analyzing Data for Insights

Lecture 4 Applying Data to Instruction

Lecture 5 Tools and Technology for Data-Driven Teaching

Lecture 6 Conclusion

Educators/ heads of schools