Archive for March 12, 2010

Race Matters, but not Gender

Some new results point out that having black teachers has an important impact on getting black students to continue in science.  What’s particularly interesting for me is that they did not find that gender of teachers had a particular impact on female students.  This echoes a finding by Joanne Cohoon about computer science from several years ago, but I do keep hearing from teachers that having female teachers is critical for getting female students to succeed in computing. Joanne found that the gender of the teacher didn’t matter — it was encouragement that mattered, whether from a female or male teacher.

A new study points to another factor: the role of black college instructors in encouraging black science students to persist as science majors. The study finds a statistically significant relationship between black students who plan to be a science major having at least one black science instructor as freshmen and then sticking to their plans. The finding could be significant because many students (in particular members of under-represented minority groups) who start off as science majors fail to continue on that path — so a change in retention of science majors could have a major impact. At the same time, the study did not find a similar impact based on gender.

via News: Race Matters – Inside Higher Ed.

March 12, 2010 at 7:29 pm 3 comments

Carl Wieman plenary at SIGCSE 2010: The brain’s a muscle?

Carl Wieman‘s talk at SIGCSE 2010 was intriguing.  I really liked the teaching practices that he recommended.  I didn’t buy his explanations for why they were good.  But as I’ve started poking at the references he provided, I’m finding that there is evidence to support at least some of his claims.  I’m downloading more in order to dig deeper.

(Yeah, I was too far away for this photo.)

Carl said that the goal of his institute is both to have students learn more effectively and to make teaching more efficient and rewarding for the teacher.  He recommends a model of carefully identifying the components of expertise, measuring the development of expertise in students, and iteratively experimenting and assessing to get it right.  He identified expert competence as having lots of facts, having a good knowledge organization framework, and monitoring one’s own understanding and learning. The goal of science education is to get students to be more like that.

Carl first presented evidence that we’re not doing well now.  He cited a paper by Richard Hake describing a 6,000 student survey (Yes! Three zeroes there!) showing that “On average, students learn less than 30% of concepts that they did not already know in lecture classes. Lecturer quality, class size, and institution doesn’t matter.”  With improved methods, that can rise to 40-60% or better.

He gave four principles of effective learning and teaching. (1) Motivation which he said is “essential, but often neglected.” (2) Connecting with and building on prior thinking. (3) Applying what is known about memory (where he recommended Robert Bjork’s work). And (4) explicit authentic practice of expert thinking.  This last part is where he went into an argument that I didn’t quite buy.  He said that “Brain development is much like muscle development.” It takes lots of practice, and that’s why motivation is so important.

Now, when I took cognitive science in the late 1980’s and early 1990’s, I was told explicitly that the brain was not a muscle and shouldn’t be thought about that way.  It wasn’t about practice.  So, I started digging into it.  Looks like Wieman is right!  There are these really intriguing studies showing that simply telling kids that the brain is like a muscle leads to better learning.  Of course, it’s still controversial, and it’s not about the brain being biologically similar to muscle. It’s about thinking about brain development as being like muscle development. Practice matters.

Carl pointed out errors that we make as teachers by not taking all of this into account.  For example, weighting exams most heavily in determining course grades is counter-productive.  Making exams important leads to cramming, which does result in better exam performance — and minimizes long term retention of that information.  You learn it only for the exam.

Then Carl claimed that lectures tend to cover too much material. We should try to teach less per lecture because there are limits on short term memory.  We shouldn’t try to teach more than seven concepts in a lecture, because we can only hold 7+/-2 subjects in short term memory.  Now, I don’t buy this one.  The duration of short term memory is at most 10 minutes, or as short as 30 seconds.  That’s not lecture-length times.  Cognitive load is certainly a critical issue, but I don’t know of evidence (and can’t find any yet) supporting the argument for no more than seven concepts per 60-90 minute lecture.

Several of the methods that Carl promoted really resonated with me.  His argument that we should start top-down, with an interesting problem and then explain what’s needed to solve it (as opposed to bottom-up, providing background knowledge, and then problems that integrate that knowledge) meshes with our notions of contextualized-computing education.  He’s a big fan of peer learning and the use of “clickers” in classrooms.  He provided lots of pointers to what he called “more scientific forms of teaching.”

Carl’s talk has me digging into areas of educational psychology than I’ve not looked at in a long time.  He’s also got me thinking about how to implement some of his methods in computing classrooms.  How do we give “quick, effective” feedback on homework?  No way is entering a whole program into an IDE then interpreting Java error messsages counts as “quick and effective”!  (Alex Repenning had a great quote from a student in his talk: “Computer science class?  That’s where the teacher gives you a program on the board, then you type it in, and it doesn’t work.”)  How do we provide homework or in-class activity that gets at expert computing thinking skills, like debugging and testing, without overloading that with also having to design programs, write programs, enter programs, and fight the compiler’s error messages?

Another great note keynote well worth the price of admission, er, the time and expense to travel to Milwaukee for SIGCSE 2010.

March 12, 2010 at 6:32 pm 16 comments

Website for the new AP “Computer Science: Principles”

A website for the new Advanced Placement exam in CS, “Computer Science: Principles” is being announced here at SIGCSE 2010–

Computer Science: Principles is a new course under development that seeks to broaden participation in computing and computer science. Development is being led by a team of computer science educators organized by the College Board and the National Science Foundation.

via CS Principles – Home.

March 12, 2010 at 3:43 pm Leave a comment

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