Seeking Collaborators for a Study of Achievement Goal Theory in CS1: Guest blog post by Daniel Zingaro
I have talked about Dan’s work here before, such as his 2014 award-winning ICER paper and his Peer Instruction in CS website. I met with Dan at the last SIGCSE where he told me about the study that he and Leo Porter were planning. Their results are fascinating since they are counter to what Achievement Goal Theory predicts. I invited him to write a guest blog post to seek collaborators for his study, and am grateful that he sent me this.
Why might we apply educational theory to our study of novice programmers? One core reason lies in theory-building: if someone has developed a general learning theory, then we might do well to co-opt and extend it for the computing context. What we get for free is clear: a theoretical basis, perhaps with associated experimental procedures, scales, hypotheses, and predictions. Unfortunately, however, there is often a cost in appropriating this theory: it may not replicate for us in the expected ways.
Briana Morrison’s recent work nicely highlights this point. In two studies, Briana reports her efforts to replicate what is known about subgoals and worked examples. Briefly, a worked example is a sample problem whose step-by-step solution is given to students. And subgoals are used to break that solution into logical chunks to hopefully help students map out the ways that the steps fit together to solve the problem.
Do subgoals help? Well, it’s supposed to go like this, from the educational psychology literature: having students generate their own labeled goals is best, giving students the subgoal labels is worse, and not using subgoals at all is worse still. But that isn’t what Briana found. For example, Briana reports  that, on Parsons puzzles, students who are given subgoal labels do better than both those who generate their own subgoal labels and those not given subgoals at all. Why the differences? One possibility is that programming exerts considerable cognitive load on the learner, and that the additional load incurred by generating subgoal labels overloads the student and harms learning.
The point here is that taking seriously the idea of leveraging existing theory requires concomitant attention to how and why the theory may operate differently in computing.
My particular interest here is in another theory from educational psychology: achievement goal theory (AGT). AGT studies the goals that students adopt in achievement situations, and the positive and negative consequences of those goals in terms of educationally-relevant outcomes. AGT zones in on two main goal types: mastery goals (where performance is defined intrapersonally) and performance goals (where performance is defined normatively in comparison to others).
Do these goals matter? Well, it’s supposed to go roughly like this: mastery goals are positively associated with many outcomes of value, such as interest, enjoyment, self-efficacy, and deep study strategies (but not academic performance); performance goals, surprisingly and confusingly, are positively associated with academic performance. But, paralleling the Briana studies from above, this isn’t what we’ve found in CS. With Leo Porter and my students, we’ve been studying goal-outcome links in novice CS students. We’ve found, contrary to theoretical expectations, that performance goals appear to be null or negative predictors of performance, and that mastery goals appear to be positive predictors of performance [2,3].
We are now conducting a larger study of achievement goals and outcomes of CS1 students — larger than that achievable with the couple of institutions to which we have access on our own. We are asking for your help.
The study involves administering two surveys to students in a CS1 course. The first survey, at the beginning of the semester, measures student achievement goals. The second survey, close to the end of the semester, measures potential mediating variables. We plan to collect exam grade, interest in CS, and other outcome variables.
The hope is that we can conduct a multi-institutional study of a variety of CS1 courses to strengthen what we know about achievement goals in CS.
Please contact me at daniel dot zingaro at utoronto dot ca if you are interested in participating in this work. Thanks!
 Briana Morrison. Subgoals Help Students Solve Parsons Problems. SIGCSE, 2016. ACM DL link.
 Daniel Zingaro. Examining Interest and Performance in Computer Science 1: A Study of Pedagogy and Achievement Goals. TOCE, 2015. ACM DL link.
 Daniel Zingaro and Leo Porter. Impact of Student Achievement Goals on CS1 Outcomes. SIGCSE, 2016. ACM DL link.