What can machine learning research tell us about self-directed learning in people?

In a lot of ways, research on machine learning and research in psychology seem worlds apart. One is the science of the artificial (i.e., intelligent, adaptable computer systems). The other is the science of the mind, a messy biological system. However, the field of Cognitive Science has been exploring the intersection of these two worlds for the last 35 years. Advances in Cognitive Science have frequently come when open questions in the science of the human mind map on to new innovations in the machine learning or artificial intelligence communities (e.g., connectionism coincided during a growth period in research for artificial neural networks).

We recently published an article in Perspectives in Psychological Science arguing that one such area of confluence may be the study of active or self-directed learning.

One of the most influential and long-standing ideas in education is that students learn more effectively when they are “active” or have some control over the learning process (e.g., a hands-on lab class). This is often placed in opposition to more “passive” forms of learning (e.g., sitting in a lecture hall).

One reason being self-directed may improve learning is that it allows individual learners to focus their study effort on parts of the world they have not yet mastered. Rather than wasting time on material they already know, self-directed learners can keep pushing the boundaries of their knowledge. The end result is more total learning, since less time is spent on redundant material.

Over the last decade or so a similar idea has been making waves in the machine learning community under the name “active learning“. Here the main goal is to speed up the training of a machine learning system, but the deeper issue is ultimately the same one faced by human learners. Just as a student might choose to ignore material they have already mastered, it makes sense for machine learning systems to know what kinds of data are likely to be informative and focus effort on those expected to be particularly revealing.

Better Learning from Less Data

How can you know how informative something will be before you’ve learned about it? Algorithms developed in the machine learning literature solve this problem by estimating how much learning will occur given different possible answers to a question the learner asks. For example, a recommendation engine (like Netflix or Amazon) might aim to predict whether you like lots of different products, but it can only ask you to rate a very small proportion of items before it becomes annoying. As a result, the websites should only ask about an item if it is expected to lead to a better understanding of your general preferences (regardless of whether you say you like that particular product or not). Given the choice, perhaps knowing if you like or dislike Kubrick’s art-house classic 2001: A Space Odyssey is better for predicting your other movie preferences than knowing if you like a summer block-buster like Transformers.

The exciting thing about this research (at least to cognitive scientists like us) is that it offers a precise, quantitative framework for understanding self-directed learning in humans. In effect, we can “borrow” ideas from the machine learning literature to help us develop theories about how people choose to collect information while they learn. Somewhat serendipitously, some of the algorithms we “borrow” from machine learning actually can predict how humans will gather information when learning (e.g., [1, 2, 3])! It’s also possible for the flow of ideas to run in reverse: ideas and findings in psychology may someday help inform better machine learning systems.

Our paper reviews the psychological and cognitive factors that determine whether self-directed learning will be an effective learning strategy in humans (which it may not be for certain kinds of learning problems). We then try to find some common ground between the formal methods developed in machine learning research and our understanding of self-directed learning from psychology.

Check out the paper here. For those interested in learning more about this topic, there are a lot of great references to both the machine learning and cognitive science literatures contained within. Also check out our paper archive for some of our recent work on this.

From the abstract:

A widely advocated idea in education is that people learn better when the flow of experience is under their control (i.e., learning is self-directed). However, the reasons why volitional control might result in superior acquisition and the limits to such advantages remain poorly understood. In this article, we review the issue from both a cognitive and computational perspective. On the cognitive side, self-directed learning allows individuals to focus effort on useful information they do not yet possess, can expose information that is inaccessible via passive observation, and may enhance the encoding and retention of materials. On the computational side, the development of efficient “active learning” algorithms that can select their own training data is an emerging research topic in machine learning. This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.

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