A Caltech Grad in a Caltech MOOC, Part 2

picture of Harry KellerBy Harry Keller
Editor, Science Education

[Note: Harry, who holds a BS in chemistry from the California Institute of Techology and a PhD in analytical chemistry from Columbia University, is sharing his first MOOC experience in this series. See part 1, 3, 4 and 5. -Editor]

October 19, 2013

The third week of this MOOC unveiled to me a problem with MOOCs. Not every student is a “student.” I am not really a student. I have conflicting priorities that few students have. Sure, some have jobs and family responsibilities. Generally speaking, they can leave their jobs behind when they leave their jobs.

As an entrepreneur, I am “on call” 24/7. I have to “steal” a few hours a week for this MOOC. I have a goal that may help my business — or may not. The MOOC has to be a low priority. I have no way of knowing when I can watch the lectures or do the homework beforehand.

If you cannot set aside a definite and adequate number of hours every week, you may not be able to achieve all you wish with your MOOC.

This has been a very busy week. I already have two full days committed next week. By full, I mean from arising to falling asleep, not a mere 8 or 9 or 10 hours. There will be not a minute for MOOCs.

Last week, I managed 8/10 on my homework due to not paying attention when I encoded my answers as (a) through (e). This week I paid attention to that but did not have time to analyze my answers as fully as I would have liked. Instead of the recommended ten hours spent on homework, I spent about one. My 7/10 on the homework reflected this less deep thinking.

One problem I missed had me determine the largest number of points that can be shattered by a planar triangle learning model. I really thought that I understood this problem but clearly did not. I don’t really have the time to figure out why.

The last problem in the set required finding the growth function for a planar annulus training model. I was able to find the answer quite nicely and got it right.

The good part about this homework is that I did not have to write any software. The two previous homework sets required considerable amounts of programming. You could make a mistake in understanding or in coding. Having two modes of error made the exercises more stressful than usual. 

Also, I was able to spend time reviewing the student discussions on the last homework but not on this one.

If you cannot set aside a definite and adequate number of hours every week, you may not be able to achieve all you wish with your MOOC. I may struggle through to the end or not. I’ve made it 33% of the way with a grade average on homework of 83, which gives me an A- so far.

I will do my best to make it through two more weeks and then reevaluate my commitment to completion at the mid-point.

October 28, 2013

Week #4 = lots more theory. This week I had meetings and deadlines associated with my business. I did my best to look at every homework problem and at least understand what it was asking. I didn’t have time to analyze every question and didn’t answer one at all.

If you are taking a MOOC with time constraints, make sure that you’ll always have the time. This week, I barely was able to finish watching the lectures.

Some of the questions required some deep thinking. That’s good — if you have time for it. It’s also typical of Caltech.

What’s missing in a course that’s this challenging is direct interaction with someone who understands the material or, in my case, about a dozen more hours to review and digest it.

The problem with all of the question sets is that you’ll never know how to solve the questions because you’re only told the correct answer. If you have lots of time, you can keep working on last week’s problem set until your answer matches the correct one. I wish I had that time.

The discussion group seemed oddly quiet this week. I’m guessing that last week’s theory-heavy material caused many students to drop out. This week was even more abstract and theoretical with even more terminology to master.

Depending on online, typed discussion groups to help out with understanding is not a winning strategy in this course. The professor clearly is an expert with much more than the usual expertise. He lectures carefully and paces his lectures well. His slides are nicely done and available throughout the course.

What’s missing in a course that’s this challenging is direct interaction with someone who understands the material or, in my case, about a dozen more hours to review and digest it.

This week was the worst so far for me. I get to drop two homework grades if I’m taking the course for credit, which I am not. This week would be one of those grades. I’m more concerned with my level of understanding. Maybe I’ll catch up in future weeks. Maybe not. Clearly, I must have more than four hours a week for this course. Next week looks more open, and hopefully I’ll not lose too much time due to loss of time this week.

I promised myself (and you) that I’d finish five weeks. One more week to go before a serious evaluation of whether to continue. The material is interesting in the abstract, but the applications are missing these two weeks. That’s supposed to change next week. We’ll see.

October 29, 2013

At lecture 9, things are getting more practical and less theoretical but still heavily mathematical. I have to wonder how the homework will be this time.

There may be many styles of MOOC. This particular one is two lectures, with audience questions after each, followed by a ten-question homework set. Each question has five possible answers. You choose one. As soon as you submit the answer to one question, you get scored. Answer keys are supplied after the due date. Then, the next two lectures and homework set are provided — on a weekly basis.

The Internet is being used just to stream lectures, provide background materials, and deliver multiple-choice, automatically scored tests. It can do so much more that this sort of thing borders on criminal.

Discussion groups may help students with problems if they can get answers.

The volume of discussion quickly diminished as the course progressed. I have to assume that, as the course was getting more difficult, the number of attendees declined rapidly.

To my mind, this is the minimum that an online course can be. It’s just the F2F course without the help from professor office hours or asking questions yourself but with the benefit of flexible time and low cost if you aren’t seeking a degree.

I had forgotten just how passive lectures are. You sit. You listen. You may take notes but don’t have to be here because you can always review the slides and play any part of the lectures.

Engagement comes only through the homework and discussion groups. The dwindling discussion groups provide less and less interaction. The homework interaction is mostly in my own mind.

In short, this is a great course in a poor medium.

The above does not condemn MOOCs. Rather it suggests that old-fashioned modes of presenting courses converted to online delivery are not the best way to go. IMO, they’re worse than the original.

The Internet is being used just to stream lectures, provide background materials, and deliver multiple-choice, automatically scored tests. It can do so much more that this sort of thing borders on criminal. Caltech, the home of some of the smartest minds (professors, graduate students, and undergraduate students) in the world, could do much more.

This could be a “learning to mastery” course, for example. That would mean a great deal more effort on the course developers but also a much higher completion and success rate. Instead, it reverts to the Caltech norm, which I had experienced first-hand in the ancient days (middle of last century) when I was a student there.

Students rarely mastered any course taken at Caltech while taking it. Sometime later, it finally sank in — if they were fortunate enough. The amount of material, the complexity and depth of the material, and the pace made mastery all but impossible. Some students managed it but most did not.

I remember my freshman physics course was difficult. I did get an A grade, but was fuzzy on much of the material. My mathematics aptitude saw me through. Next year, as I was taking second-year physics, some freshmen asked for help on an assignment. I could see that it was one that I had had trouble with the previous year. Yet, suddenly everything was clear. It was easy for me, the same stuff that was so hard before. This is the norm at schools such as Caltech and MIT (where my daughter went to school).

It’s past time to evolve away from the “Khan Academy” model of MOOCs and build something much greater. In some subjects, you may be able to rely on student interaction. In others, such as this machine learning course, that is just not the case.

This is a graduate-level course using rather high-level mathematics and all of the symbology you can imagine: set intersections, sums, expectation values, integrals, probability expressions, and so on.

Many have suggested, even at the recent conference I attended, that online courses are harder than their F2F counterparts. That’s generally true for a number of reasons. However, it does not have to be so, IMO. It’s just easier that way.

It’s the same thing with testing. This course has test questions that cannot be answered quickly and that require true understanding to answer correctly, even though they are multiple-choice. Most test questions lie at the opposite end of the scale. You only have to memorize and may have to perform some perfunctory analysis (in science, that’s “plug-and-chug”) to succeed.

The simple test questions take less time and cost less to create, which is why they’re so prevalent. Similarly, the lecture-and-quiz MOOC is the cheapest to create. Let’s hope that tools will be made that can break down the barrier to exciting, engaging MOOC-style courses.

When a course is over, you should know that you have learned to do something, and you learn by practicing, not by listening. Even Aristotle understood this: “For the things we have to learn before we can do them, we learn by doing them.”

2 Responses

  1. No, lectures and discussions are linked. Discussions are an integral part of the course. This is your opportunity to have those lectures and homework questions answered in more depth.

  2. The discussions should play a larger role, but I have found them to be unsatisfying. Either they’re about esoterica or trivia. Perhaps, I’m too pragmatic for this course. It’s not just about the course; it’s also about the students. It’s clear that not all students and courses go together.

    I think that I’ve already gained the most important insights into machine learning from this course, how to know whether a given situation lends itself to this valuable tool. Completing the course will expand the machine learning options and my depth of understanding of how to use them.

    Last week, I didn’t have the time to visit the discussion groups. This week, I don’t feel the necessity but may do so just to see what’s going on.

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