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Introductory programming classes are renowned for their high dropout rates. The authors propose that this is because students learn to adopt a fixed mindset towards programming. This paper reports on a study carried out with an introductory programming class, based on Dweck’s mindset research. Combinations of three interventions were carried out: tutors taught mindset to students; growth mindset feedback messages were given to students on their work; and, when stuck, students were encouraged to use a crib sheet with pathways to solve problems. The study found that the mixture of teaching mindset and giving mindset messages on returned work resulted in a significant change in mindset and a corresponding significant change in test scores – improvements in test scores were found in a class test given immediately after the six-week intervention and at the end-of-year exam. The authors discuss the results and the strengths and weaknesses of the study. learner's mindset towards ability levels has a crucial effect on their learning 5. She identifies two categories of learners, one consisting of those with a fixed mindset (the students described above) and the other, those with a growth mindset, who act as if persistent effort and attention to data gleaned from failures will lead to the desired learning. Dweck’s work on mindsets highlights a number of ramifications for learning. Each mindset is supported by a motivational framework guiding future thinking and behaviour 3. Those with a fixed mindset tend to be interested only in performance goals – they feel a need to be seen to be achieving well at all times, since this broadcasts their ability to the world. Those with a growth mindset adopt learning goals. They are classical deep learners who sacrifice looking good in the eyes of others in order to learn
Cutts et al. (Wed,) studied this question.