Giving Compass' Take:
- Jon Reifschneider shares his experience developing and testing an AI program to better understand individual student learning patterns and expand its implications for broader use.
- In what ways does the collection and analysis of individualized student data help educators teach more effectively? How can funders support research that improves our ability to educate current and future generations?
- Learn about other ways in which technology is shaping our educational system.
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It is historically one of the most vexing problems in education that there is a tradeoff between scale and achievable quality of instruction: As class sizes grow larger, the ability of a teacher to provide the type of personalized guidance shown by learning science research to be most effective is diminished.
Yet as instructors in the new world of online education, we have access to ever-increasing amounts of data—from recorded lecture videos, electronically submitted homework, discussion forums, and online quizzes and assessments—that may give us insights into individual student learning. In summer 2020, we began a research project at Duke to explore how we could use this data to help us as instructors do our job better. The specific question we set out to answer was: “As an instructor, how can I use the data available to me to support my ability to provide effective personalized guidance to my students?”
This is where machine learning comes in. Fundamentally, machine learning is used to recognize patterns in data, and in this case the technology can be used to identify students’ knowledge states from their performance patterns across quizzes and homework.
The project culminated in the creation of a prototype tool called the Intelligent Classroom Assistant. The tool reads instructor-provided class quiz or homework results and the set of learning topics covered so far in the course. It then analyzes the data using a machine learning algorithm and provides the instructor with three automated analyses about: quiz and homework topics with which the class has struggled; learning topics the class has and has not mastered; and the performance of each student.
Read the full article about using AI to teach better by Jon Reifschneider at EdSurge.