Last week Zack Brym and I formally announced a semester long Data Carpentry course that we’ve have been building over the last year. One of the things I’m most excited about in this effort is our attempt to support collaborative lesson development for university/college coursework.
I’ve experience first hand the potential for this sort of collaborative lesson development though the development of workshop lessons in Software Carpentry and Data Carpentry. Many of the workshop lessons developed by these two organizations now have 100+ contributors. As far as I’m aware, Software Carpentry was the first demonstration that large-scale open collaboration on lessons could work (but I’d love to hear of earlier examples if folks are aware of them) and it has resulted in what is widely regarded as really high quality lesson material. Having seen this work so effectively for workshops, I’m interested in seeing how well it can work for full length courses.
Most college and university courses that I’m aware of start in one of three ways: 1) someone sites down and develops a course completely from scratch; 2) the course directly follows a text book; or 3) a new professor inherits a course from the person who taught it previously and adapts it.
Developing a course from scratch, even one following a text book fairly closely, is a huge time commitment. In contrast, with collaboratively developed courses new faculty, or faculty teaching new courses, wouldn’t need to start from scratch. They would be able to pick up an existing course to adapt and improve. I can’t even begin to describe how much easier this would have made my first few years as a faculty member. More generally, if we are teaching similar courses across dozens or hundreds of universities, it is much more efficient to share the effort of building and improving those courses than to have each person who teaches them do so independently.
In addition to the time and energy, there are often a lot of things that don’t work well the first time you teach a course and it typically takes a few rounds of teaching it to figure what works best. One of the challenges of developing lessons in isolation is that you only teach a class every 1 or 2 years. This makes it hard and slow to figure out what needs work. In contrast, a collaboratively developed course might be taught dozens or hundreds of times each year, allowing the course to be improved much more rapidly through large scale sampling and discussion of what works and what doesn’t. In addition to having more information, the fact that faculty are spending less time developing courses from scratch should leave them with more time for improving the materials. In combination this results in the potential for higher quality courses across institutions.
By involving large numbers of lesson developers, collaborative development also has the potential to help make courses more accurate, more up-to-date, and more approachable by novices. More lesson developers means a greater chance of having an expert on any particular topic involved, thus making the material more accurate and reducing the amount of bad practice/knowledge that gets taught. New faculty with more recent training on the development team can help keep both the material and the pedagogical practices up-to-date (this is hard when the same person teaches the same course for 20 years). More lesson developers also increases the likelihood that someone who isn’t an expert in any given piece of material is also involved, which should help make sure that the lesson avoids issues with expert blindness, thus making the material more accessible to students.
Collaborative college/university lesson development will not be without challenges. The skills required for collaborative lesson development in the style of Software and Data Carpentry require proficiency with computational approaches not familiar to many academics. The necessary skills include things like version control, developing materials in markdown, and working with static site generators like Jekyll. This means this approach is currently most accessible for those with some computational training and may initially work best for computing focused courses. In addition, organizing open collaborations takes time and energy, as does collectively deciding on how to design and update classes. Universities and colleges are not typically good at valuing time invested in non-traditional efforts and that would need to change to help support those managing development of courses with large numbers of faculty involved. More substantial may be the fact that faculty are not used to collaborating with other people on course development and are therefore not used to compromising and negotiating what should go into a course. This can be compensated for to some degree by making courses easy to modify and customize, as we’ve tried to do with the Data Carpentry Semester course, but ultimately there will still need to be a shift from prioritizing the personal desires of the faculty member to the best interests of the course more broadly. This approach will likely work best where there are a number of places that all want to teach the same general material.
Is it time? When I built my first version of Programming for Biologists back in 2010 I was really excited about the potential for collaborative open course development. I built the course using Drupal, emailed a bunch of my friends who were teaching similar courses and said “Hey, we should work together on this stuff”, and stuck some welcoming language on the homepage. Nothing happened. A few years later I was on sabbatical at the University of North Carolina and got the opportunity to talk a fair bit with Elliot Hauser who was part of a team trying to encourage this through a start-up called Coursefork. I was somewhat skeptical that this approach would work broadly at the time, but I thought it was really awesome that they were trying. They ended up pivoting to focus on helping computing education through a somewhat different route and became trinket. A couple of years I converted my course to Jekyll on GitHub and told a lot of people about it. There was much excited. Still nothing happened. So why might this work now? I think there are three things that increase the possibility of this becoming a bigger deal going forward. First, open source software development is becoming more frequent in academia. It still isn’t rewarded anywhere close to sufficiently, but the ethos of using and contributing to collaboratively developed tools is growing. Second, the technical tools that make this kind of collaboration easier are becoming more widely used and easier to learn through training efforts like Software Carpentry. Third, more and more people are actively developing university courses using these tools and making them available under open source licenses. Two of my favorites are Jenny Bryan’s Stat 545 and Karl Broman’s Tools for Reproducible Research. Our development of the Data Carpentry semester course has already benefited from using openly available materials like these and feedback from members of the computational teaching community. I guess we’ll see what happens next.
This post benefited from a number of comments and suggestions by Zack Brym, who has also played a central and absolutely essential role in the development of the Data Carpentry semester long course. The post also benefited from several conversations with Tracy Teal, the Executive Director of Data Carpentry about the potential value of these approaches for college courses
Over the last year and a half we have been actively developing a semester-long Data Carpentry course designed to be easily customized and integrated into existing graduate and undergraduate curricula.
Data Carpentry for Biologists contains course materials for teaching scientists how to work more effectively with data. The course provides introductions to data management and relational databases, data manipulation and analysis, and data visualization. It covers the same general types of material as a two-day Data Carpentry workshop, but expands the materials and opportunities for practice into a full-length university course. The teaching material uses R and SQLite, with some corresponding materials for Python as well. To help students understand the direct applications to their interests, the examples and exercises focus on biological questions and working with real data. The course emphasizes using best practices to produce reusable and reproducible data analysis.
Active-learning Teaching Materials
Learning computing requires active practice by working through programming problems. Just diving in to computing is challenging for most scientists, so the course instruction is designed to combine short live-coding introductions to concepts followed immediately by the students working on a related exercise. Additional exercises are assigned later for practice. This follows the “I do”, “We do”, “You do” approach to teaching, which leverages the benefits of active-learning and flipped classrooms without leaving students who are less comfortable with the material feeling lost. The bulk of class time is spent working on assigned exercises with the instructor moving around the room helping guide students through things they don’t understand and engaging with students who are thinking about advanced applications of what they’ve learned.
This approach is the result of lots of reading about effective teaching methods and Ethan’s experience teaching this and related courses over the last six years at Utah State University and the University of Florida. It seems to work well for both students that get the material easily and those that find it more challenging. We’ve also tried to make these materials as useful as possible for self-guided students.
Open course development
Software Carpentry and Data Carpentry have shown how powerful collaborative lesson development can be and we’re interested in bringing that to the university classroom. We have designed the course materials to be modular and easy to modify, and the course website easy to clone and set up. All of the teaching materials and associated website files are openly available at the Data Carpentry for Biologists repository on GitHub under CC-BY and MIT licenses. The course materials are all written in Markdown and everything runs on Jekyll through GitHub Pages. Making your own version of the course should take less than an hour. We’ve developed documentation for how to create your own version of the course and how to contribute to development. Exercises and assignments are modular and changing exercises and assignments simply involves reordering items in a list. Adding a new exercise involves creating a new Markdown file and then adding its title to the list of exercises for an assignment.
If you teach, or want to teach, a course like this, we’d love to get you involved. Here are some useful links for getting started.
We want to be sure getting involved is as easy as possible. We’ve worked hard to provide documentation and help resources for students and instructors. Students can find all they need to know at our student start guide. Instructors have access to course content and site design documentation.
Development of this course was generously support by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4563 to Ethan White and the National Science Foundation as part of a CAREER award to Ethan White.
Doing science in academia involves a lot of rejection and negative feedback. Between grant agencies single digit funding rates, pressure to publish in a few "top" journals all of which have rejection rates of 90% or higher , and the growing gulf between the number of academic jobs and the number of graduate students and postdocs , spending even a small amount of time in academia pretty much guarantees that you’ll see a lot of rejection. In addition, even when things are going well we tend to focus on providing as much negative feedback as possible. Paper reviews, grant reviews, and most university evaluation and committee meetings are focused on the negatives. Even students with awesome projects that are progressing well and junior faculty who are cruising towards tenure have at least one meeting a year where someone in a position of power will try their best to enumerate all of things you could be doing better . This isn’t always a bad thing  and I’m sure it isn’t restricted to academia or science (these are just the worlds I know), but it does make keeping a positive attitude and reasonable sense of self-worth a bit… challenging.
One of the things that I do to help me remember why I keep doing this is my Why File. It’s a file where I copy and paste reminders of the positive things that happen throughout the year . These typically aren’t the sort of things that end up on my CV. I have my CV for tracking that sort of thing and frankly the number of papers I’ve published and grants I’ve received isn’t really what gets me out of bed in the morning. My Why File contains things like:
- Email from students in my courses, or comments on evaluations, telling me how much of an impact the skills they learned have had on their ability to do science
- Notes from my graduate students, postdocs, and undergraduate researchers thanking me for supporting them, inspiring them, or giving them good advice
- Positive feedback from mentors and people I respect that help remind me that I’m not an impostor
- Tweets from folks reaffirming that an issue or approach I’m advocating for is changing what they do or how they do it
- Pictures of thank you cards or creative things that people in my lab have done
- And even things that in a lot of ways are kind of silly, but that still make me smile, like screen shots of being retweeted by Jimmy Wales or of Tim O’Reilly plugging one of my papers.
If you’ve said something nice to me in the past few years be it in person, by email, on twitter, or in a handwritten note, there’s a good chance that it’s in my Why File helping me keep going at the end of a long week or a long day. And that’s the other key message of this post. We often don’t realize how important it is to say thanks to the folks who are having a positive influence on us from time to time. Or, maybe we feel uncomfortable doing so because we think these folks are so talented and awesome that they don’t need it, or won’t care, or might see this positive feedback as silly or disingenuous. Well, as Julio Betancourt once said, "You can’t hug your reprints", so don’t be afraid to tell a mentor, a student, or a colleague when you think they’re doing a great job. You might just end up in their Why File.
What do you do to help you stay sane in academia, science, or any other job that regularly reminds you of how imperfect you really are?
 This idea that where you publish not what you publish is a problem, but not the subject of this post.
 There are lots of great ways to use a PhD, but unfortunately not everyone takes that to heart.
 Of course the people doing this are (at least sometimes) doing so with the best intentions, but I personally think it would be surprisingly productive to just say, "You’re doing an awesome job. Keep it up." every once in a while.
 There is often a goal to the negativity, e.g., helping a paper or person reach their maximum potential, but again I think we tend to undervalue the consequences of this negativity in terms of motivation [4b].
[4b] Hmm, apparently I should write a blog post on this since it now has two footnotes worth of material.
 I use a Markdown file, but a simple text file or a MS Word document would work just fine as well for most things.
Figuring out how to teach well as a professor at a research university is largely a self-study affair. For me the keys to productive self-study are good information and self-reflection. Without good information you’re not learning the right things and without self-reflection you don’t know if you are actually succeeding at implementing what you’ve learned. There have been some nice posts recently on information and self-reflection about how we teach over at Oikos (based on, indirectly, on a great piece on NPR) and Sociobiology (and a second piece) that are definitely worth a read. As part of a course I’m taking on how to teach programming I’m doing some reading about research on the best approaches to teaching and self-reflection on my own approaches in the classroom.
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.
- 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)
- 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)
- Combine graphics with verbal descriptions. Combine graphical presentations (e.g., graphs, figures) that illustrate key processes and procedures with verbal descriptions. (moderate)
- 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)
- Use quizzing to promote learning.
- Use pre-questions to introduce a new topic. (minimal)
- Use quizzes to re-expose students to key content (strong)
- Help students allocate study time efficiently.
- Teach students how to use delayed judgments of learning to identify content that needs further study. (minimal)
- Use tests and quizzes to identify content that needs to be learned (minimal)
- 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)
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.