How is machine learning changing the world of education? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Answer by Jack Buckley, President and Chief Scientist, Imbellus, on Quora:
This is a big question. I believe that the application of new technologies from the fields of Machine Learning (ML) and Artificial Intelligence (AI) have the capability of transforming education but that there will be more hype than results in the short run—much like the case of other transformative educational technologies in the past (I am old enough to remember when the filmstrip and, then, the VHS cassette were supposedly going to revolutionize the delivery of instruction in our classrooms).
Perhaps the area of promise that has garnered the most attention is “personalized learning.” This can be a slippery concept to define (for example, some observers consider Individualized Education Plans as a form of personalized learning—one that requires no particular technology) but I am using it here to mean the use of educational technology to permit students in the same classroom to learn different curricular content, tailored to their own pace and level of mastery (e.g. the Summit Learning platform). The promise of ML/AI in personalized learning is mostly in optimization or, at least, improved efficiency of instruction. Coupled with a robust system of formative assessment, ML/AI-enhanced personalized learning may be able to tell teachers what the optimal next lesson is on a student-by-student level, saving time and increasing student learning.
Another area of promise for ML/AI in education is in predictive analytics. For example, one of my research teams at the American Institutes for Research developed an “early warning system” that could detect students at-risk of not graduating on the basis of behavioral and other data. The approach used here was later adapted and applied as part of a larger suite of ML analytics tools in a product for school systems.
Finally, of course, ML and AI are helping us revolutionize educational assessment. At Imbellus, for example, we use AI to understand the difficulty level of various forms of our assessment tasks to help improve our ability to “equate” or ensure that forms are fair for every test-taker. We also rely on ML to help understand and better predict how our scenarios will perform psychometrically. And we are working toward a future approach to educational testing that uses ML to build an integrated system of formative and interim assessment that enables us to accurately measure student ability across the school year without the need for time consuming end-of-year summative assessments.
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