Introduction to Learning Engineering

Learning engineering is the process and practice of applying learning sciences via human-centered engineering design methodologies and data-informed decision making to support learners and their development.

This learning engineering approach is critical because the current process to test and establish the efficacy of new ideas is too long and too expensive, leaving teachers and administrators with neither proven tools nor the research needed to make informed pedagogical decisions.

In this video series, you will learn about how you can apply a learning engineering perspective to your work.

Overview of Learning Engineering Kumar Garg

In this video, Kumar Garg, managing director at Schmidt Futures, will explain what learning engineering is and why it’s so important to safely collect data, analyze data, and iterate based on what you learn. As Garg notes, learning engineering requires us to humbly admit that we don’t know everything there is to know about how students learn. But, at the same time, he argues that educators can do a lot to learn about learning! When individuals think from a learning engineering perspective, they’re constantly planning systematic ways to test their own assumptions about how their tool works and then make changes based on what they learn.

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The Practice of Learning Engineering Ryan Baker

In this video, Ryan Baker, professor of education and computer science at the University of Pennsylvania and director of the Penn Center for Learning Analytics, will explain some of the common methods learning engineers use in practice. This includes a deeper look into the types of questions that can be asked and answered using educational data. He’ll also give an overview of some of the ways this data is collected and analysed and some of the terms commonly used in learning engineering. He also explains the differences between learning engineering and other related fields, such as learning science and educational technology.

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The Challenges of Learning Engineering Diane Litman

In this video, Diane Litman highlights some of the challenges that are commonly encountered in learning engineering. She talks about concerns for student privacy, the ethics of good research design, and considerations of bias in machine learning algorithms. Diane is a computer science professor, a senior scientist with the Learning Research and Development Center, and faculty in the Intelligent Systems Program, all at the University of Pittsburgh.

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Case Study: Talking Points Heejae Lim

In this video, Heejae Lim, Talking Points’ founder and CEO, explains how the company thinks about both enabling outside researchers to conduct research more easily using the Talking Points platform and using their own data internally to better understand how parent-teacher-administrator communications affect and reflect student learning. Talking Points is a platform that allows student families, teachers, and administrators to communicate with each other in their own native languages through human and AI-enabled two-way translation.

Case Study: The Feedback Prize Aigner Picou

In this video, Aigner Picou gives an overview of an exciting new competition in learning engineering called The Feedback Prize. The Feedback Prize, a joint project of The Learning Agency Lab and Georgia State University, was created to spur the development of open-source algorithms to better provide automated feedback on student writing. Aigner, a program director at The Learning Agency Lab, explains the datasets and the competition’s goals for improving feedback on argumentative writing and bettering writing feedback for English language learners.

Case Study: UpGrade, a Carnegie Learning A/B Testing Tool April Murphy

In this video, April Murphy, a learning engineer at Carnegie Learning, gives an overview of Carnegie Learning’s tool, UpGrade. UpGrade is an open-source web-based platform for A/B testing in education. UpGrade allows researchers to compare the efficacy of different learning resources such as videos, tests, texts, algorithms, and more, all with the goal of better understanding – and thus improving – student learning.

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