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