Is adaptivity a qualitative or quantitative problem?

One common criticism of adaptive learning is that by tailoring instruction so closely to students’ needs, it doesn’t challenge them enough. As embodied by James Paul Gee’s critique:

People who never confront challenge and frustration, who never acquire new styles of learning, and who never face failure squarely may in the end become impoverished humans. They may become forever stuck with who they are now, never growing and transforming, because they never face new experiences that have not been customized to their current needs and desires.

While I agree with the dangers of what he describes, I question the causal attribution.

First, adaptive learning systems that indulge in too much customization may instead be guilty of relying on a too-narrow prescription for the student’s “zone of proximal development (ZPD)”. Individualized learning does not require giving only incremental steps; it can (and should) include more ambitious steps to occasionally challenge students, perhaps just beyond their conventional ZPD (or at the limits of their ZPD when defined by “lots of help”). Students need to struggle—manageably—as part of their learning. Adapting to students’ needs can include optimizing the nature and amount of that struggle based on past experiences and future expectations.

Second, this can also be overcome by building a certain amount of variability into the system, for the sake of both the students and the system. Occasionally presenting students with problems that may or may not lie within their ZPD can help them learn “what to do when you don’t know what to do” (in the words of a dear colleague of mine, Joe Wise). Whether framed as desirable difficulties, germane cognitive load, preparation for future learning, or the development of adaptive expertise rather than just routine expertise, unexpected challenges can offer invaluable learning opportunities. Further, adaptive learning systems need to reach beyond what is already known in order to improve themselves. A truly intelligent system should be discovering new knowledge about its particular learners and even about learning in general. The possible paths a student might take are infinite, and the system’s designers don’t know what’s best—only what tends to be better compared to other paths that have already been examined. That is, adaptive learning must itself be an adaptive learner.

Both of these issues point to a quantitative problem due to adapting too narrowly or too often. The deeper question is whether adaptivity is a fundamental, qualitative problem: Does having any adaptivity at all invite complacency among students accustomed to having their learning experiences at least partly tailored to their needs? Given the well-established importance of scaffolding instruction according to students’ needs, I would argue that adaptive learning is a valuable tool not simply for accelerating but also for enriching instruction.

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