One of the things we’ve been reading is a great report by the US Department of Education’s Institute of Education Sciences on Organizing Instruction and Study to Improve Student Learning. The report synthesizes existing research on what to do in the classroom to facilitate meaningful long-term learning, and distills this information into seven recommendations and information on how strongly each recommendation is supported by available research.

Recommendations

1. Space learning over time. Arrange to review key elements of course content after a delay of several weeks to several months after initial presentation. (moderate)
2. Interleave worked example solutions with problem-solving exercises. Have students alternate between reading already worked solutions and trying to solve problems on their own. (moderate)
3. Combine graphics with verbal descriptions. Combine graphical presentations (e.g., graphs, figures) that illustrate key processes and procedures with verbal descriptions. (moderate)
4. Connect and integrate abstract and concrete representations of concepts. Connect and integrate abstract representations of a concept with concrete representations of the same concept. (moderate)
5. Use quizzing to promote learning.
   1. Use pre-questions to introduce a new topic. (minimal)
   2. Use quizzes to re-expose students to key content (strong)
6. Help students allocate study time efficiently.
   1. Teach students how to use delayed judgments of learning to identify content that needs further study. (minimal)
   2. Use tests and quizzes to identify content that needs to be learned (minimal)
7. Ask deep explanatory questions. Use instructional prompts that encourage students to pose and answer “deep-level” questions on course material. These questions enable students to respond with explanations and supports deep understanding of taught material. (strong)

(Quoted directly from the original report via a Software Carpentry blog post)

This is a nice summary, but it’s definitely worth reading the whole report to explore the depth of the thought process and learn more about specific ideas for how to implement these recommendations.

How am I doing?

Recently I’ve been teaching two courses on programming and database management for biologists. Because I’m not a big believer in classroom lecture, for this type of material, a typical day in one of these courses involves: 1) either reading up on the material in a text book or viewing a Software Carpentry lecture before coming to class; 2) a brief 5-10 minute period of either re-presenting complex material or answering questions about the reading/viewing; and 3) 45 minutes of working on exercises (during which time I’m typically bouncing from student to student helping them figure out things that they don’t understand). So, how am I doing with respect the the above recommendations?

1. Space learning over time. I’m doing OK here, but not as well as I’d like. The nice thing about teaching introductory programming concepts is that they naturally build on one another. If we learned about if-then statements two weeks ago then I’m going to use them in the exercises about loops that we’re learning about this week. I also have my advanced class use version control throughout the semester for retrieving data and turning in exercises to force them to become very comfortable with the work-flow. However, I haven’t done a very good job of bringing concepts back, on their own, later in the semester. The exercise based approach to the course is perfect for this, I just need to write more problems and insert them into the problem-sets a few weeks after we cover the original material.

2. Interleave worked example solutions with problem-solving exercises. I think I’m doing a pretty good job here. Student’s see worked examples for each concept in either a text book or video lecture (viewed outside of class) and if I think they need more for a particular concept we’ll walk through a problem at the beginning of class. I often use the Online Python Tutor for this purpose which provides a really nice presentation of what is going on in the program. We then spend most of the class period working on problem-solving exercises. Since my classes meets three days a week I think this leads to a pretty decent interleaving.

3. Combine graphics with verbal descriptions. I do some graphical presentation and the Online Python Tutor gives some nice graphical representations of running programs, but I need to learn more about how to communicate programming concepts graphically. I suspect that some of the students that struggle the most in my Intro class would benefit from a clearly graphical presentation of what is going happening in the program.

4. Connect and integrate abstract and concrete representations of concepts. I think I do this fairly well. The overall motivation for the course is to ground the programming material in the specific discipline that the students are interested in. So, we learn about the general concept and then apply it to concrete biological problems in the exercises.

5. Use quizzing to promote learning. I’m not convinced that pre-questions make a lot of sense for material like this. In more fact based classes they are helping to focus students’ attention on what is important, but I think the immediate engagement in problem-sets that focus on the important aspects works at least as well in my classroom. I do have one test in the course that occurs about half way through the Intro course after we’ve covered the core material.  It is intended to provide the “delayed re-exposure” that has been shown to improve learning, but after reading this recommendation I’m starting to think that this would be better accomplished with a series of smaller quizzes.

6. Help students allocate study time efficiently. I spend a fair bit of time doing this when I help students who ask questions during the assignments. By looking at their code and talking to them it typically becomes clear where the “illusion of knowing” is creeping in and causing them problems and I think I do a fairly good job of breaking that cycle and helping them focus on what they still need to learn. I haven’t used quizzes for this yet, but I think they could be a valuable addition.

7. Ask deep explanatory questions. One of the main focuses in both of my courses is an individual project where the students work on a larger program to do something that is of interest to them. I do this with the hope that it can provide the kind of deep exposure that this recommendation envisions.

So, I guess I’m doing OK, but I need to work more on representation of material both through bringing back old material in the exercises and potentially through the use of short quizzes throughout the semester. I also need to work on alternative ways to present material to help reach folks whose brains work differently.

If you are a current or future teacher I really recommend reading the full report. It’s a quick read and provides lots of good information and food for thought when figuring out how to help your students learn.

Thanks for listening in on my self-reflection. If you have thoughts about this stuff I’d love to hear about it in the comments.

So, why haven’t universities been smart enough to centralize an even more valuable computational resource, their computer labs?

As any student of Software Carpentry will tell you, it is far more important to be able to program well than it is to have access to a really large high performance computing center. This means that the most important computational resource a university has is the classes that teach their students how to program, and the computer labs on which they rely.

At my university [2] all of the computer labs on campus are controlled by either individual departments or individual colleges. This means that if you want to teach a class in one of them you can’t request it as a room through the normal scheduling process, you have to ask the cognizant university fiefdom for permission. This wouldn’t be a huge issue, except that in my experience the answer is typically a resounding no. And it’s not a “no, where really sorry but the classroom is booked solid with our own classes,” it’s “no, that computer lab is ours, good luck” [3].

And this means that we end up wasting a lot of expensive university resources. For example, last year I taught in a computer lab “owned” by another college [4]. I taught in the second class slot of a four slot afternoon. In the slot before my class there was a class that used the room about four times during the semester (out of 48 class periods). There were no classes in the other two afternoon slots [5]. That means that classes were being taught in the lab only 27% of the time or 2% of the time if I hadn’t been granted an exception to use the lab [6].

Since computing skills are increasingly critical to many areas of science (and everything else for that matter) this territoriality with respect to computer labs means that they proliferate across campus. The departments/colleges of Computer Science, Engineering, Social Sciences, Natural Resources and Biology [7] all end up creating and maintaining their own computer labs, and those labs end up sitting empty (or being used by students to send email) most of the time. This is horrifyingly inefficient in an era where funds for higher education are increasingly hard to come by and where technology turns over at an ever increasing rate. Which [8] brings me to the title of this post. The solution to this problem is for universities to stop allowing computer labs to be controlled by individual colleges/departments in exactly the same way that most classrooms are not controlled by colleges/departments. Most universities have a central unit that schedules classrooms and classes are fit into the available spaces. There is of course a highly justified bias to putting classes in the buildings of the cognizant department, but large classes in particular may very well not be in the department’s building. It works this way because if it didn’t then the university would be wasting huge amounts of space having one or more lecture halls in every department, even if they were only needed a few hours a week. The same issue applies to computer labs, only they are also packed full of expensive electronics. So please universities, for the love of all that is good and right and simply fiscally sound in the world, start treating computer labs like what they are: really valuable and expensive classrooms.

—————————————————-

[1] Think of a single scientist who keeps 10 expensive computers, only uses them a total of 1-2 months per year, but when he does the 10 computers aren’t really enough so he has to wait a long time to finish the analysis.

[2] And I think the point I’m about to make is generally true; at least it has been at several other universities I’ve worked over the years.

[3] Or in some cases something more like “Frak you. You fraking biologists have no fraking right to teach anyone a fraking thing about fraking computers.” Needless to say, the individual in question wasn’t actually saying frak, but this is a family blog.

[4] As a result of a personal favor done for one administrator by another administrator.

[5] I know because I took advantage of this to hold my office hours in the computer lab following class.

[6] To be fair it should be noted that this and other computer labs are often used by students for doing homework (along with other less educationally oriented activities) when classes are not using the rooms, but in this case the classroom was a small part of a much larger lab and since I never witnessed the non-classroom portion of the lab being filled to capacity, the argument stands.

[7] etc., etc., etc.

[8] finally…

Software Carpentry to the Rescue

Fortunately you don’t need to go back college and get another degree to substantially improve your knowledge and abilities when it comes to scientific programming, because with a few weeks of hard work Software Carpentry will whip you into shape. Software Carpentry was started back in 1997 to teach scientists “the concepts, skills, and tools they need to use and build software more productively” and it does a great job. The newest version of the course is composed of a combination of video lectures and exercises, and provides quick and to the point information on such critical things as:

• Version Control
• Automated Testing
• Using a Debugger
• Databases
• Using the Shell

along with lots of treatment of best practices for writing code that is clear and easy to read both for other people and for yourself a year from now when you sit down and try to figure out exactly what you did³.

The great thing about Software Carpentry is that it skips over all of the theory and detail that you’d get when taking the relevant courses in computer science and gets straight to crux - how to use the available tools most effectively to conduct scientific research. This means that in about 40 hours of lecture and 100-200 hours of practice you can be a much, much, better programmer who rights code more quickly, with fewer bugs, that be easily reused. I think of it as boot camp for scientific software development. You won’t be an expert marksman or a black belt in Jiu-Jitsu when you’re finished, but you will know how to fire a gun and throw a punch.

I can say without hesitation that taking this course is one of the most important things I’ve done in terms of tool development in my entire scientific career. If you are going to write more than 100 lines of code per year for your research then you need to either take this course or find someone to offer something equivalent at your university. Watch the lectures, do the exercises, and it will save you time and energy on programming; giving you more of both to dedicate to asking and answering important scientific questions.

______________________________________________________

¹I took 3 computer science courses in college and I get the impression that that is about 2-3 more courses than most ecologists have taken.

²I don’t know of any data on this, but my impression is that over 90% of code written by ecologists is written by a single individual and never read or used by anyone else. This is in part because we have no culture of writing code in such a way that other people can understand what we’ve done and therefore modify it for their own use.

³I know that I’ve decided that it was easier to “just start from scratch” rather than reusing my own code on more than one occasion. That won’t be happening to me again thanks to Software Carpentry

*Thanks to Joanna Hsu and Peter Adler for passing this along.

Working to get your PhD is like training to become a Jedi Knight,” I started. “You follow a Master; you live a life of sacrifice; you must develop rational thought and patience…

If you’re looking for five minutes of academically oriented fun go check out the whole article.

Over the past 10-20 years, biology has become increasingly quantitative, and mathematical sciences have in turn been increasingly influenced by biology. It has been said that “mathematics is biology’s next microscope, only better” (Cohen, J.E., PloS Biology, 2004) because mathematical, statistical, and computational sciences will continue to reveal unsuspected and entirely new worlds within biology, just as the microscope revealed previously unseen worlds following its invention. It has also been said that “biology is mathematics’ next physics, only better” (Cohen, J.E., PloS Biology, 2004) because biology will in turn continue to spur major new developments in computation, mathematics and statistics, just as physics has done in the past several hundred years.

Recognizing this integration, the MSc in Quantitative Biology provides students of life sciences with the quantitative skills they will need to thrive in the modern discipline of biology, and provides students from a more quantitative background with the biological insight they need to apply their technical skills. The course is unique in integrating important current research questions in biology with data from ecosystems down to cells and state-of-the-art quantitative methods. Graduates will be highly trained scientists prepared for employment in any of several settings, including as PhD students in universities and institutes worldwide; in the research departments of multinational industries concerned with the environment (e.g., pharmaceuticals, biotechnology); in conservation, management and agricultural agencies; and in local and national governments.

Say NO!

You could easily spend your entire life as an assistant professor doing some combination of serving on departmental committees and student committees, going to seminars and workshops, checking email, sending email, meeting with students outside your group to “talk about their research”, reviewing for journals, etc. While much of this is technically within your job description no one really cares if you do any of these things. Seriously, they don’t. They will say that they do, they may ask you why you weren’t at the meeting on such and such, but when push comes to shove at the vast majority of research universities none of this matters. So learn to say No. This is hard for a lot of people, but it is critical to your success (and for me at least, happiness).

My approach to this challenge is that my default response to any request for me to spend time on anything other than research and teaching is No. This means that the activity has to be important enough to me to overcome the default response. A lot of professors that I know have the opposite default – they say yes unless there is a particular reason that they can’t participate. This typically ends up meaning that the only reason they don’t do something is because they already have something scheduled when it would occur. This is a great way to end up with a calendar that is chock full of things that having nothing to do with succeeding in the tenure process. A very successful senior colleague of mine once told me that every time someone asks him to be involved in something new he thinks about what he isn’t going do if he chooses to participate in this new activity. So, when someone asks me to do something my default response is no and if I then bypass the default because this new thing sounds worthwhile I then ask myself if I’d rather do it than the things that are already on my list.

Schedule time for your actual work

Most people will agree to schedule something as long as they don’t have a direct conflict on their calendar. The problem with this approach is two fold. First, it means that the only time you have to work on what really matters – conducting research, writing papers, and, to a lesser degree, preparing high quality classroom experiences (lectures, exercises, etc.) – is whatever’s left over after everything that doesn’t matter is scheduled. This is obviously a bit… backwards.

The second problem is that even if you have enough total time left over to dedicate to your primary interests/responsibilities that time will be chopped up into little blocks. The vast majority of scientists and other knowledge workers that I know do not work well with short chunks of time. That is because the real work of science and other intellectual pursuits requires serious thought and that often cannot be accomplished in thirty minute chunks.

My answer to this is to schedule large blocks of time on my calendar for research and teaching. I do all of my teaching during the spring semester so this means that in the fall I typically block out Tue-Thur mornings from 8-12 and all day Friday for research. In the spring I block out those same three mornings for class preparation and keep Friday reserved for research. When someone wants to schedule a meeting with me I give them a list of available times that does not include these reserved periods. It can be hard to stick to this system when someone is having a hard time scheduling a meeting, but I rarely make an exception and then only when the person who is trying to schedule the meeting has completely run out of options and isn’t trying to schedule things at the last minute (and frankly this combination is quite rare).

Hide

Even if you follow all of these suggestions, life will find a way to interrupt your efforts. Other faculty will stop by your office for a few minutes to chat, your graduate students or the staff or your department head will have a quick question or need a signature on something, a valued colleague will send you an email that you’d like to respond to quickly, a book vendor will stop by to tell you about their latest and greatest text book, and the next thing you know you’ve spent an entire day dealing with little things that have almost nothing to do with getting tenure. The only method that I have found for dealing with this is to hide. That can mean closing (and locking; seriously) your office door and not answering it under any circumstances, it can mean working in a remote corner of the library or some other hidden nook on campus, or it can mean working at home if the distractions there aren’t worse than those at the office. I aim for eight hidden hours a week, typically by working at home one day.

Prioritize your efforts (by focusing on your CV)

Of course in order any of this to work you need to know what is important when it comes to getting tenure. This can be difficult to determine because most institutions will basically tell you that everything is important. Research is important, teaching is important (and not just actually teaching well, but going to workshops on teaching, having a teaching philosophy grounded in cutting edge pedagogy, documenting your teaching, and your efforts to improve it, and your efforts to improve your documentation of it), service is important (and not just on committees, but educating the general public, visiting K-12 classrooms, helping the department recruit students at job fairs, and helping put together departmental packets for university awards). So, basically everything you can imagine is important… Except, of course, it really isn’t. If you are in position with a research role that is greater than or equal to your other roles (faculty positions have role statements associated with them that are typically broken out as percentages of research, teaching and service; e.g., 50% research, 40% teaching, 10% service) then what matters most is your research (and by a greater margin than your role statement indicates). I have a simple rule for prioritizing things when it comes to promotion and tenure considerations:

Things only matter if you put them on your CV and their importance is in proportion to the amount of space you dedicated to them on your CV.

Your CV is the document that you use to characterize your accomplishments in your job. As such, things that you include on it are the things that really matter – publications, grants, some (generally brief) indication that you have taught classes and perhaps that your students didn’t totally hate you. You probably have a small section on service, but that probably has more to do with service outside the university than within it. From what I’ve seen promotion and tenure hinges almost exclusively on your CV so this is the right strategy. If you don’t believe me try asking your department head when the last time someone didn’t get tenure primarly due to their teaching or service. This strategy also has the nice ancillary benefit that if your university really is more interested in how many committee meetings you’ve gone to than how many papers you’ve published, then you’ll be able to use your spiffy new vitae to get a job someplace that’s a little more serious about research, or teaching, or whatever it is that you actually got into academia to do.

UPDATE: I forgot to add the credit for the title of this post that is of course due to Mike Kaspari’s excellent blog on the topic and that there is some other excellent related advice on this subject over at The EEB & Flow.