Academic publications Education English

Fixed versus Growth Mindset Does not Seem to Matter Much In Late Bachelor Level

On Monday, I had the honor of presenting a paper that I coauthored with my colleague Ville Tirronen. We had wondered if our two problematic courses might benefit from mindset interventions – after all, we regularly run into student behaviors that are consistent with the mindset theory.

The mindset theory, as you may recall, sorts people into two rough categories at a particular point in time: people with a fixed mindset view their own intelligence as something they cannot change; they adopt behaviors that try to emphasize their brilliance and hide their stupidity, including choosing safe (not challenging) problem-solving tasks; they view effort as a proof of their own stupidity; and thus they tend to not reach their own full potential as problem solvers. People with a growth mindset view their own intelligence as growable by learning; they tend to choose challenging tasks as those give the best opportunities to learn, and they see effort as a sign of learning; they thus are able to reach their full potential in problem solving.

We ran an observational study in two of our courses last fall, where we used a questionnaire to measure student mindset and then we statistically estimated its effect on course outcomes (did the student pass, and if so, what grade they got). It turned out that observed mindset had nothing to do with student achievement on our two courses. This was not what we expected!

Another surprising finding was that there were relatively few students with a fixed mindset on these courses. This raises the question, whether students who are affected by their fixed mindset drop out of our bachelor program before they reach our courses; unfortunately, our data cannot answer it.

While I still believe in the compelling story that the mindset theory tells, and believe a causal connection exists between mindsets and achievement, this study makes me very skeptical about its practical relevance. At least in the context where our study was run, the effect was so small we could not measure it despite a decent sample size (n = 133).

The paper citation is

Antti-Juhani Kaijanaho and Ville Tirronen. 2018. Fixed versus Growth Mindset Does not Seem to Matter Much: A Prospective Observational Study in Two Late Bachelor level Computer Science Courses. In Proceedings of the 2018 ACM Conference on International Computing Education Research (ICER ’18). ACM, New York, NY, USA, 11-20. DOI:

While the publisher copy is behind a paywall, there are open access copies available from my work home page and from our institutional digital repository.

The reception at the conference was pretty good. I got some tough questions related to methodological weaknesses, but also some very encouraging comments. The presentation generated Twitter reactions, and Andy Ko has briefly reviewed it in his conference summary.

Now some background to the paper that I did not share in my presentation and that is not explicit in the paper. Neither of us have done much quantitative research with human participants, so the idea was originally to do a preliminary study that allows us to practice running these sorts of studies; we expected to find a clear association between mindset and outcomes, and with that confirmation that we are on the right track we would have then moved on to experiments with mindset interventions. Well, the data changed that plan.

I had hoped to present an even more rigorous statistical analysis of our data, based on Deborah Mayo’s notion of severe testing – it gives us conceptual tools to evaluate results like ours that are difficult to interpret using the traditional tools of significance testing. Unfortunately, while the conceptual basis of Mayo’s theory is well established, there is very little literature on how it is actually applied in practical research. I hope her forthcoming book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars will contain some technical development of the practical kind beyond what has previously been published. But until that technical development, I really cannot use Mayo’s theory to argue for a particular statistical model in a particular paper. Thus, while our drafts contained discussions of Mayo’s conceptual ideas, they were too far removed from the rest of the paper without the technical developments, and thus were deleted before submission.

We sent this paper to ICER mostly because we wanted to offer something to a conference that is held in Finland, and this one was ready. While we were confident of our method and results, we did not think it very likely that it would be accepted, as it is notoriously difficult to publish null result papers. We were quite surprised – though very happy – to get positive reviews and an acceptance.

We should publish negative results – in cases where there is a plausible theoretical basis to expect a positive result, or a practical need for an answer either way – much more than we do. A bias for positive results increases risks for bad science significantly, from the file drawer effect to outright data manipulation and deliberate misanalysis of data. I am extremely happy that our negative result was published, and I hope it will help change the culture toward healthy reporting practices.

Academic publications English Programming

Discussing the future of the field: Do it on the record!

I came to the field of programming language research as an outsider. Though we had an active researcher in our faculty (he has since retired), for various reasons he was never my mentor, so I never got personal introductions nor did I receive much oral wisdom from an elder in the field. Instead, I immersed myself in the literature. Eventually, I got good enough to write a reasonably good licentiate thesis, which in turn led me to spend three months visiting one of the external examiners, Stefan Hanenberg. From him, I got some of the inside story, and the world looked much different. Of course, he is a minority voice in the field, but every participant has a unique point of view anyway. The thought I am writing about here crystallised for me immediately: too much of the field’s development happens off the record!

On Wednesday, I presented my essay “Concept analysis in programming language research: Done well it is all right” [ACM DL] [Author’s PDF] [presentation slides (PDF)] at SPLASH Onward here in Vancouver. I told some of my story there; the session chair Robert Biddle expanded on it and made a forceful point, which I am repeating and expanding on here now.

The discussions that lead to significant developments in the field must happen on the record! It is fine to talk with friends and colleagues in pubs and at lunch (or wherever), but if the discussion leads to a concrete proposal that would affect the field either substantially (in terms of, for example, conceptual developments), the issue should be written up and published in a publication of record, and sufficient time should be allowed (if possible) for contrary and refinement views to be similarly published on the record.

The reason for this is, on the one hand, the empowerment of the community fringe, who does not have the opportunity to participate in off the record discussions, and, on the other hand, the creation of a full record for the future generations of researchers so that they can read up and learn about why things are the way they are.

Concept analysis, as I proposed it in my essay, is one way of proceeding with this on-the-record development of the field in terms of conceptual issues. Too often it appears to an outsider that things just appear out of thin air. Instead, any conceptual developments should be argued for in the literature!

I think the field would benefit enormously if we stopped thinking of research publication as the accumulation of facts (or the completion of a grand theory), and instead took a page from the humanities and the social scientists: for them, scholarly publications form a grand – multicentennial – discussion where individual researchers listen for a while, then start participating for a couple of decades, and then go away, while others take their place. This viewpoint has a side-effect of creating a fuller historical record, but it also places more responsibility on the reader: you have to listen for a while to catch the import of what you are reading, instead of grabbing a paper here or a paper there and taking them to be the gospel.

It would also allow putting more of the development of the field on the record.

Academic publications English Philosophy

Concept Analysis in Programming Language Research: Done Well It Is All Right (To appear in Onward’17)

I just submitted my camera ready version of Concept Analysis in
Programming Language Research: Done Well It Is All Right
, a methodology essay which has been accepted at Onward’2017. I will probably write about it more extensively later.

Here is the accepted version, for personal use (not for redistribution): PDF (copyright 2017 Antti-Juhani Kaijanaho, exclusively licensed to ACM).

Antti-Juhani Kaijanaho. 2017. Concept Analysis in Programming Language Research. In Proceedings of 2017 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!’17). ACM, New York, NY, USA, 14 pages. (the DOI link will not work until October).