As introduced yesterday, I’m blogging my way through the SERC tutorial on course design, for a new graduate-only course on experimental design and data analysis. Yesterday, I explained the context and constraints on the course, and today I’m mulling on the course goals. I’m supposed to identify 1-3 over-arching goals for the course and 1-2 ancillary skills goals. Below the fold, I’ll share my overarching goals and how I got to them. But I’m struggling with the ancillary skills goals, dear readers, and I’d love your help.
Task 1.2c: Set one to three overarching goals for your course.
The SERC tutorial lays out some very specific guidelines for the over-arching goals. They should be student-centered (not teacher-centered), involve higher order thinking skills, concrete, and have measurable outcomes, and provide clear direction for course design. They should be phrased as “Students will be able to…” or “I want students to be able to…”. Before we get to the stage of actually writing the course goals, however, the tutorial asks us to read some example goals and assess whether they meet the guidelines above. The tutorial also asks us to think about what I do as a professional in my discipline, in the context of the course. Here’s what I came up with:
I identify questions that advance understanding of my field and which I have at least some of the skills and resources to answer. I form collaborations and write proposals to gain the necessary skills and resources to answer the questions. As part of the proposal writing process, I: (1) design field, lab, and modeling studies; (2) hypothesize the expected outcomes of those studies; and (3) plan the sorts of data analysis necessary to answer my questions based on my study design. I carry out the studies, usually as part of a team, adjusting the experimental design as necessary. I then analyze the data, attempt to answer the original question, and formulate next questions so that I can begin the process over again.
I also review proposals and manuscripts from other researchers working in my field, and I evaluate the scientific and technical merits of the project design, data analysis, and interpretation of the results.
Given what I do, here’s what I want my students to be able to do:
- I want students to be able to evaluate the connections between: (a) knowledge of existing literature and/or preliminary data; (b) research question and hypothesis generation; (c) experimental design; (d) quality of the collected data; (e) methods of data analysis; (f) ability to answer the posed research question.
- I want students to be able to work in teams to formulate a research question, design a study to answer the question, and analyze the resulting data using appropriate statistical techniques.
- I want students to be able to critique experimental design and data analysis techniques that appear in proposals or the published literature of their field.
The first goal may seem a bit redundant to the scientific method, but what I want my students to understand is a bit more nuanced. I want them to realize that you have to design the your data analysis at the same time as the experiment or you won’t be able to answer your question. I want them to understand the importance of preliminary data and intensive literature review for ensuring that the question they are asking is relevant and answerable and for ensuring that good data is collected once the project is underway. I want them to realize that if you design your experiment in an ad hoc fashion (as so many of us do), you risk collecting data that will be useless for answering the question you really wanted to answer.
The second point is asking them to apply and operationalize the concepts from the class. Maybe I’m getting ahead of myself, but I’m envisioning having the students work in teams to write an NSF-style proposal by the end of the semester. I’ve also toyed with the idea of asking them to collect some preliminary data to support that proposal, but I think I’m getting the cart ahead of the cart now.
The third goal is another application of the same concepts, but now applied not to their own research but to that of other researchers. I designed the goal to help teach the skill of peer-review, something on which I think many graduate students lack adequate training and mentoring.
Do my over-arching goals seem reasonable for a course on experimental design and data analysis, given the constraints and context of the course?
Assuming for the moment that I don’t experience a massive commenter uprising against the over-arching goals, what do you think the ancillary skills goals for the course should be. Here’s what SERC has to say about them:
What ancillary skills would like to have your students improve on during your course? Ancillary skills might include writing, quantitative skills, 3-D visualization, self-teaching, peer teaching, oral presentation, working in teams/groups, critically assessing information on the Internet, accessing and reading the professional literature, and so on.
Task 1.3: Set one or two ancillary skills goals for your course.
Which ancillary skills are important enough in the context of your course that you are willing to provide timely feedback and repeated practice so that students actually improve their skills? Be realistic. You can’t do everything and still address the content of the course. Choose one or two ancillary skills to work on – ones that you can commit to integrating from the beginning to the end of the semester.
I find myself oddly struggling with this one, maybe because the course seems, by its very nature, to be a skills-oriented course that will touch on many of those things. But how do I pick one or two to focus on? I’d really like to hear your comments, and I’ll try to participate in any comment thread discussions. Then in a day or two, I’ll have to move on, set the goals, and start choosing content to support those goals.