Intro to Learning Engineering Video Series
The Learning Engineering Tools Competition asks competitors to engage in learning engineering. But what if you’re not sure what learning engineering is?
The videos below, each created by a leader in the field, are meant to give you a general overview of learning engineering and some examples of how to think from a learning engineering perspective about your tool.
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, you’re constantly planning systematic ways to test your own assumptions about how your tool works and then making changes based on what you 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.
Mentioned in the video:
- High-Leverage Opportunities for Learning Engineering
- Learning Engineering Recommendations
- Big Data and Education Course on edX
- Pittsburg Science of Learning Center Data Shop at CMU
- ASSISTments
- BlackBox Data Collection Project
- MORF Framework for massive open online course data
- Riid EdNet
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The Challenges of Learning Engineering Diane Litman
Diane Litman is a computer science professor, a senior scientist with the Learning Research and Development Center, and faculty of the Intelligent Systems Program all at the University of Pittsburgh. In this video, she highlights some of the challenges that are common to encounter in learning engineering. She talks about concerns for student privacy, the ethics of good research design, and considerations of bias in machine learning algorithms.
Mentioned in the video:
- FERPA (Family Educational Rights and Privacy Act)
- NSF (National Science Foundation)
- IES (Institute of Education Sciences)
- NIH (National Institutes of Health)
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Case Study: Talking Points Heejae Lim
Talking Points is a platform that allows for student families, teachers, and administrators to communicate with each other in their own native languages through human and AI-enabled two-way translation. In this video, Heejae Lim, Talking Points’ founder and CEO, explains how the company thinks about both enabling outside researchers to conduct their research more easily using their platform and using their own data internally to better understand how parent-teacher-administrator communications affect and reflect student learning.
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Case Study: The Feedback Prize Aigner Picou
Aigner Picou is a program director at The Learning Agency Lab. In this video, she gives an overview of an exciting new competition in learning engineering called The Feedback Prize. The Feedback Prize is a joint project of The Learning Agency Lab and Georgia State University created to spur the development of open-source algorithms to better provide automated feedback on student writing. Aigner explains the datasets and the goals of the competition to improve feedback on argumentative writing and to improve feedback for English language learners on their writing.
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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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With the help of our sponsors, the Learning Engineering Tools Competition aims to support new tools that will improve learning and advance the field of learning engineering.