Fortnightly Mailing has a focus on online learning and the internet. It summarises and comments on resources and news that I find in the course of my work.
These two short videos from the Hoover Institution will give you a sense of the line of argument advanced by Terry Moe and John Chubb, who are right-of-centre, union-hostile US academics with trenchantly positive views about the value of on-line learning, and about the need to make the same kinds of technology/labour substitution in education as has already taken place in large swathes of the rest of the economy.
Gundega Dekena is a self taught Linux administrator and web programmer, based in Riga, Latvia. She has been studying all three of the October to December Stanford online computer science courses in parallel - Introduction to Artificial Intelligence (AI), Introduction to Machine Learning (ML), and Introduction to Databases (DB) - putting her in a good position to compare and contrast them. Gundega can contacted at gundega.dekena [AT] gmail.com, or through Google+.
Overview
Comparing these three courses feels a bit like comparing apples, screwdrivers and desks, yet I see a lot of students doing that, usually without much thought about the differences. So, this is my look (from the perspective of a student) atthe things that can be compared, and that can be learned from all three courses, for the benefit of the next batch of courses that is going to come from Stanford next year.
There is plenty of food for thought and a great deal to identify with - from a very diverse set of angles - in yesterday's 45 minute "Google+ Hangout" discussion between Sal Khan, Peter Norvig and Sebastian Thrun. Examples include:
the authenticity and hence superiority of unscripted, tentative explanations, with "low" production values;
how lectures empower lecturers not students;
data analysis as an "underpinning process" for real personalisation;
why their is such a mismatch between the value that a university thinks its courses provide what students think they provide;
the challenge of combining individual study with group interaction.
1. I wrote last week that the units on Games, Game Theory, and Advanced Planning had been hard work; and that the associated homework had felt demanding and been very time-consuming. Securing an adequate mark on that homework despite too many "stupid errors" means I probably learned more than I had realised. But I also know that I was answering some of the questions from the "stored fat" of knowledge I already had, rather than from what the course had been teaching me.
Last week's report focused mainly on the midterm exam, and on a conversation with Sebastian Thrun. This report conments on the midterm exam and reflects on the way the course has run this week.
First, some comments on the midterm exam
1. According to information just received from Sebastian Thrun, 23k students passed the midterm, with 85% currently falling into the B+ range.
2. There was a surprising amount of post-exam discussion between students about the substance of the exam. There was some whingeing, but not much given the very large numbers on the course. The interesting discussion fell into three categories:
"I think the shape of the next phase of online learning is being defined: reliance on open educational resources, supported by some sort of automated, analytics based and competency based testing mechanism."
Substantial changes made to A3 and small changes made to A5 and A7 below, 21 November 2011, and to introductory paragraph, 23 November 2011. [For visitors from the Aiqus discussion about student numbers, note that more light is shed on the "YouTube video counts question" - a distracting side-issue - in paragraph seven of report number two, 18 October 2011.]
Here is my seventh participant's report from the Stanford Introduction to Artificial Intelligence course. It is in three parts. A is the report proper. B picks up on a pre-arranged call from Sebastian Thrun. C lists further free CS courses that will be available from Stanford University in January 2012.
A - Report
1. A lighter week from the point of view of studying, because the last week saw less material presented, to allow students time to prepare for the midterm "examination", aka the midterm.
[Decided to remove image of scratch pad shortly after publishing this.]
In September 2007 I had a personally revelatory moment concerning how AI (as I now know it to be) might be used to provide formative feedback for learners. It stemmed from being involved in running the 2007 ALT conference at which Dylan Wiliam and Peter Norvig each gave keynote speeches. With Richard Noss (who now directs the ESRC/EPSRC funded TEL programme) we set up a Google Doc "scratch pad" to gather a shortlist of issues that could be worth examining. Not a lot (well, nothing!) came from it, as is often the way.
The paths of blindfolded walkers trying to walk in a straight line in overcast (blue) and sunlit (yellow) conditions. From Unit 8.2 of Introduction to Artificial Intelligence course.
1. I'd finished the work before I noticed that, unannounced, the order of the course had been changed from that shown in the originally published outline. Thus units on Representation with Logic, and Planning, taught by Peter Norvig, have come before the originally scheduled units on Hidden Markov Models and Bayes Filters.
2. This change of order probably explains why this week's study felt somewhat disconnected from last week's, a fact emphasised by a change of teacher from Sebastian Thrun to Peter Norvig. Thrun, it has to be said has a less austere and more down-to-earth presence than Norvig, whose delivery style is dry and very concentrated. Underlying this, I think Norvig's material on propositional logic and on mathematical representations of plans is by its nature relatively abstract: and for me this spells trouble, being someone who has always tended to struggle with the abstract.
3. The advice I'd give from a course design point of view is to further strengthen the illustrations as to why these kinds of abstraction matter. The video from which the picture above is taken a good example of this:
4. Secondly this kind of more abstract content needs more rather than less use of "dialogic" check questions, as has been the case in other units of the course. To illustrate this point here are the contents-lists of Units 4 and Units 8. Check-questions are indicated by ?. 12 sets of check questions out of 21 sections is a much more promising ratio than 3 out of 22.
5. During the last week Sebastian Thrun and Peter Norvig ran an online "office hours" session using a Google Plus "Hangout". I was not around to try to join this, but from Thrun's "We apologize for the large number of people who were denied participation in the online office hours via youtube. We had a lively discussion which was recorded on video - mostly on topics beyond this course (e.g., what are great research topics). We will soon post the video on this site. Apologies again. Technical problems with the Hangout-Youtube link." it looks as if this was a partial success. I think this is a "forgivable" issue, given the very large number of people who will have attempted to take part in it.
6. Next week I hope to find out whether and if yes by how much the participation rate has changed, as measured by the submission of homework for weeks 3 and 4.
This is a shorter report than #1-3, mainly because the course has got into a rhythm and because there've been no substantial changes in delivery methods.
1. Despite the work that has been done to the web systems that sit behind the site, it looks as if there were again overload issues at and around the week 2 homework submission deadline, and this despite the probability the number submitting homework may have dropped quite a bit dropping by nearly 20% to ~37,000 from the ~46,000 reported after the week 1 homework deadline.
2. The course continues to fascinate. For example it is nice to gain a practical understanding of how things like spam filters actually work.
Moe and Chubb - Liberating Learning
These two short videos from the Hoover Institution will give you a sense of the line of argument advanced by Terry Moe and John Chubb, who are right-of-centre, union-hostile US academics with trenchantly positive views about the value of on-line learning, and about the need to make the same kinds of technology/labour substitution in education as has already taken place in large swathes of the rest of the economy.
Terry Moe
John Chubb
For more on/from both, go to Liberating Learning - Technology, Politics, and American Education.
Posted on 18/12/2011 in News and comment | Permalink | Comments (0)