Evidence to Action: Using Data and Research to Drive Learning in EdTech Products series explores how data can be used to understand learning, improve experiences, and make better decisions. Whether you’re an educator, product builder, or program designer, this three-course series will guide you through the foundations of using data, the role of research, and how analytics can support smarter, more responsible EdTech decisions.

By the end of this series, you’ll be able to use data confidently across your work, moving from simple observation to structured research and advanced analytics to design better learning experiences and drive meaningful impact.

 

Duration	Designed for	Cost
3-5 hours per course (9-12 hours for the series)	Educators Product managers Edtech developers	Free to Fellows in the program

From Data to Impact: Using Data to Support Learning

Many educators and EdTech innovators design excellent learning experiences with the learner in mind. Yet, too often, data is viewed mainly as a reporting tool for funders or external requirements. This introductory course reframes that perspective. It helps you see data as a learner-centered tool for reflection, iteration, and continuous improvement.

This course introduces the essential ideas, language, and practices that will prepare you to work confidently with evidence in your own context. You’ll learn how to interpret the signals already available in your programs, organize them through clear frameworks, and use simple, sustainable tools to improve the learning experience. Step by step, the course demystifies data and shows how small, evidence-informed adjustments can lead to meaningful gains in engagement, confidence, and outcomes.

By the end of this course, you will be able to:

• Recognize how data and evidence can inform better learning design and decisions.
• Identify, organize, and evaluate learner data to ensure it is useful, valid, and ethical.
• Interpret learner signals and use simple methods to generate actionable insights.
• Apply data to drive continuous improvement and demonstrate meaningful impact.

Whether you’re a teacher, program manager, or EdTech designer, this course offers a practical and accessible introduction to using data for learning improvement giving you the foundation to move from collecting data to creating real impact.

Introduction to Research for EdTech Innovators

Many educators, designers, and EdTech innovators already ask questions, observe learners, and make small adjustments based on what they see. Yet research is often treated as something formal, academic, or reserved for external evaluators. This introductory course reframes that perspective. It helps you see research as a practical, learner-centered tool for reflection, iteration, and stronger decision-making.

This course introduces what research is, why it matters, and how to apply it directly to your product, program, or classroom challenges, which will help you work confidently with evidence in your own context. You’ll learn how to turn curiosity into focused questions, gather and analyze information systematically, and organize what you discover into meaningful insights. Along the way, you’ll learn how simple, evidence-informed inquiry can strengthen your design choices, deepen your understanding of users, and support better learning outcomes.

By the end of this course, you will be able to:

• Define research in plain terms and explain its practical value in EdTech beyond academic settings.
• Frame strong, focused research questions that are clear, focused, and meaningful for your EdTech work
• Select and apply basic data collection and analysis methods suitable for different research questions and contexts.
• Communicate and apply research findings through clear, actionable stories that inform design, programs, and decision-making.

Learning Analytics for Smarter Product Design

Many EdTech teams collect large amounts of learner data such as quiz attempts, time on task, and course completion rates. Yet this data is often used only at a surface level or reduced to simple dashboards. Learning analytics is not just about tracking activity. It is about using data thoughtfully to understand learning, make better product decisions, and support meaningful outcomes.

This course reframes learning analytics as a practical, decision-making tool for founders, product teams, and educators. Instead of focusing on complex algorithms or coding, it helps you understand what your data actually represents, what it can and cannot tell you, and how to use it responsibly. You will learn how to move from raw data to meaningful insights, how to interpret patterns over time, and how to think critically about predictions before acting on them.

Throughout the course, you will explore how learning can be modeled over time, how features are constructed from messy real-world data, and how different analytical approaches support different types of decisions. You will also examine the risks of misinterpretation, bias, and over-reliance on metrics, especially in diverse and resource-constrained contexts.

By the end of this course, you will be able to:

• Evaluate educational datasets, define meaningful measures of success, and interpret model performance to support responsible product decisions.
• Select and justify appropriate regression and classification models based on data characteristics, decision stakes, and interpretability requirements.
• Explain learning as a latent process and interpret models such as Item Response Theory, Knowledge Components, and knowledge tracing to reason about learner understanding over time.
• Assess feature engineering choices, detect risks of overfitting, and balance robustness, generalizability, and interpretability in analytics systems.
• Evaluate ethical risks, bias, governance constraints, and the appropriate use of emerging AI methods when applying learning analytics in product contexts.