Ray Schroeder, Inside Higher Ed
To customize learning for each of 30 or 40 students in a class, monitor their individual progress and provide meaningful feedback has been just too time-consuming. Now, machine learning can synthesize the huge volume of data needed to more fully deliver student-centered learning. It can assemble the background, take input from the individual learner regarding their self-determined needs and expectations, identify learning deficits and needs, and produce and present the learning path to best accomplish those goals. In this case, the role of the faculty member shifts from directly delivering materials and grading based on a single syllabus to advising, assisting and assessing personalized learning that meets the needs of both the individual and the prescribed outcomes of the program.
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