Machine Learning (Theory)

11/15/2009

The Other Online Learning

Tags: Machine Learning,Online,Teaching jl@ 3:07 pm

If you search for “online learning” with any major search engine, it’s interesting to note that zero of the results are for online machine learning. This may not be a mistake if you are committed to a global ordering. In other words, the number of people specifically interested in the least interesting top-10 online human learning result might exceed the number of people interested in online machine learning, even given the presence of the other 9 results. The essential observation here is that the process of human learning is a big business (around 5% of GDP) effecting virtually everyone.

The internet is changing this dramatically, by altering the economics of teaching. Consider two possibilities:

  1. The classroom-style teaching environment continues as is, with many teachers for the same subject.
  2. All the teachers for one subject get together, along with perhaps a factor of 2 more people who are experts in online delivery. They spend a factor of 4 more time designing the perfect lecture & learning environment as verified by extensive study.

These two approaches have a similar economic cost, with the additional effort in the second approach being offset by the fact that it is a one-time effort rather than an annual effort.

I’m sure many people prefer the classroom approach, because it’s traditional, because a teacher can adjust dynamically and intelligently to the student, and because a teacher provides ancillary benefits such as day care and child abuse detection. Nevertheless, the second approach represents a compelling alternative addressing education. For classes commonly taught through high school, it’s difficult to imagine how good a learning experience could be after millions of hours spent refining to create the perfect approach. Imagine repeating a lecture over-and-over, testing the resulting student understanding a {day, week, month, year, decade} later to such an extent that every slide, every sentence, and every exercise is optimized for excellent learning. We could even imagine adapting the lecture to the learning style of each student.

The process of converting to the second approach has been slow, but it seems to be picking up. This suggests we can expect several things:

  1. Shakeout Like all new approaches, there is room for early adopters to win while the established old order suffers. We can expect the most severe impact on pure teaching institutions which do not adopt the newer approaches. Research universities will be insulated in two ways: much of their revenue comes from research grants anyways while the new approach creates a flight to excellence, which the research universities can lay some claim to. At one extreme, I understand that only 4-5% of the operating budget for Caltech comes from student tuition.
  2. Centralized Testing. Although class lessons can be taught at a distance, and exercises worked out by students, there is great room for cheating. The remedy for this is a strong centralized testing service. This already exists in the form of SAT, GRE, and AP tests, because grade inflation and nonuniform standards are common across schools. If a student can ace these tests after taking online learning classes, then there is a real sense in which colleges accepting students are satisfied by their qualifications. We can expect this to become more true, and perhaps to see more employer-oriented tests. We can also expect that testable subjects have an inherent advantage in online learning. As centralized testing is a difficult market to break into, the existing systems have a substantial advantage here.
  3. Digitization. Doing online learning brings all the advantages and disadvantages of any other digital media. These include perfect replicability, essentially free distribution, and difficult economics—on one hand the approach could be vastly valuable while on the other it’s difficult to charge someone for something they can get free. The economics imply that there is room for a major charity or state government to accomplish a great deal which might be difficult to accomplish in a business model.
  4. Gaps. There are areas of teaching which are not amenable to online instruction. For example, teaching people to do research remains in the apprentice model. Similarly, letters of recommendation remain an aspect of the apprentice model. Subjects of relatively small interest such as individual research directions may not merit the effort of a highly polished online instruction system. Similarly, many elements of our current education system are not related to formal education, but rather are about students meeting students, teachers acting as daycare for students, or simply structuring the day for learning. Mechanisms achieving the same ends with online human learning systems are necessary, and the conflation of goals represented by the traditional education approach will retard (but not stop) the adoption of online learning approaches. This process has already taken a decade, and we can expect more decades to come.

For those of us interested in online machine learning, it’s natural to question the relationship with online human learning. The practices differ entirely, but the theory still applies, as there are no clauses in the theorem statements of the form “if the learning agent is not a human then…” When you examine the theorem statements for applicability to online human learning, there are a few ideas which may transfer well. One of these is the necessity and techniques for handling exploration problems. If there are two ways to teach a subject, then you could simply try both and take the best. But if your resources are limited then a UCB approach provides a more efficient mechanism for doing this testing. Similarly, if a student has a set of known attributes, contextual-bandit approaches suggest a sound mechanism for personalization of lessons.

Much of our other theory about the process of online learning may be helpful in a heuristic-motivating manner, but it appears typically too pessimistic to accurately capture what is possible. For example, a common technique to explain an idea when teaching is to simply cover a few extreme cases from which all others are some interpolation. The closest common machine learning analogue to this is some active learning algorithms, where a learning algorithm chooses which examples to label. But, of course, this is not an accurate model, because it’s not the student, but rather the teacher which is choosing the examples. A setting more suitable for student and teacher has been studied in learning theory (see the bibliography here for a link into the citation tree). However, these results are typically rather brittle, so it’s not clear yet that we have understood the right way to formalize this process.

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