I ran the final meeting of Spring's CS222: Advanced Programming class similarly to Fall's, which I wrote about back in December. First, the students brainstormed a list of the 80 things they thought they learned through the course. Then, each student was given three votes, and we voted on the most significant items. It should be noted that the students had freedom to choose how they determined what it meant for learning to be related to the class, and so not all of the items fall directly from in-class interactions; similarly, the students could choose any heuristic for voting. The top earners with their total vote tally are shown below.
These are all self-explanatory except perhaps "professional tips." We spent a good deal of time in the course practicing and reflecting on how we learn the best practices for a language and environment. I used Java as a language of instruction, and everyone had a copy of Joshua Bloch's Effective Java, second edition. Early in the semester, I gave carefully-constructed assignments in which the students would read portions of Effective Java and apply them. As the semester went on, I loosed the reins, and by the end of the semester, I had the students searching through the book to find tips to apply in their six-week projects. I hope and believe that this kind of inversion of control was useful in helping the students learn how to be better learners.
"Time management" may be worth another mention, since my approach to this was pragmatic. There were few directed exercises that were related to time management, the most prominent being requiring projects to be completed in multiple deliverables. Students, as a subset of humans, procrastinate. Making someone take an incremental and iterative approach can have a huge impact in how they think about their own work, especially when it's the first time having to do this in a software development environment.
After this convergent thinking activity, we returned to divergent thinking as I asked the students to brainstorm on how they learned these things. As in Fall's class, the students seemed to have a harder time answering this question, and I don't think it's just because they were tired. I suspect that, despite all my efforts to foster reflective practice, the students spend most of their cognitive energy on content rather than learning processes. I need to try to find more ways to help students make the leap to metacognition, although I acknowledge that I am limited in what I can do in this sophomore-level course. Regardless, here are the only three of the 22 that earned any votes when each student was given one vote:
|Practice / Doing||7|
|Working in a group||5|
|Failing usefully / learning from mistakes||2|
Clearly, all three of these are closely related, and they all deal with learning-by-doing, as I expected. Of course, the course was framed around constructionist and studio-based learning principles, and so it's no surprise that students would say they learned from these activities—it's practically all they did!
Here's the part that surprised me: in the list of 22 items, reflective writing did not come up. The students did a lot of reflective writing. The most prominent example was the learning objectives document, which I wrote about in April. In a nutshell, this collaboratively-written document was the medium by which the students' learning was to be communicated with me, and it was explicitly designed for reflective practice. Yet, even at the end of the semester when thinking about their own learning processes, and even when I was trying to steer their thinking towards this while moderating the brainstorming session, no one even mentioned this reflective writing. Far from meaning that this writing was unnecessary, I think this echoes my earlier point, that (generally) the students were not savvy at moving between multiple levels of cognition, from the content, to the thinking about the content, to metacognition.
The good news is that this was an experimental class, part of a research study on how collaborative writing impacts learning in STEM. My co-PI and I have a large amount of data to analyze, and hopefully we can gain insight into what the students experienced and how they made sense out of it.