Author: Jenny Terry, Project Officer, University of Brighton
As well as our Project Officer roles on the Changing Mindsets project, many of us also teach. This means that as well as promoting the growth mindset approach with other staff, we’ve also been ‘walking the talk’ and putting some of the strategies into practice with our own students. I have recently begun teaching on the Introduction to Research Methods module for first year students in Brighton’s School of Applied Social Science (SASS). A large proportion of the content is statistics – something that students often cite as finding especially tricky. The pedagogical literature suggests that there are a multitude of reasons for this, such as motivation, performance extremes, making learning last, and statistics anxiety (Conners, Mccown, & Roskos-Ewoldson, 1998), with statistics anxiety often being hailed as the most important factor (Blalock, 1987; Bessant, 1992; Field, 2010).
Statistics anxiety is the “apprehension that occurs when an individual is exposed to statistics content or problems and instructional situations, or evaluative contexts that deal with statistics” (Macher, Papousek, Ruggeri & Paechter, 2015, p.1). Zeidner (1990) describes its manifestation as including “extensive worry, intrusive thoughts, mental disorganization, tension, and physiological arousal” (p. 319). It should be unsurprising then that it has been shown to negatively impact performance outcomes via reduced memory efficiency, an impaired understanding of research papers, and the inability to analyse and interpret data (Onwuegbuzie & Wilson, 2003). It can also reduce motivation, self-efficacy, achievement expectancies, and persistence (Paxton, 2006). On the flip-side, those low in statistics anxiety show higher levels of achievement (Schutz, Drogosz, White, & Distefano (1998).
So, what causes statistics anxiety? Well, lots of things; Onwuegbuzie and Wilson (2003) describe it as a complex, multi-dimensional construct with antecedents that can be situational (e.g. receiving low maths grades in the past), dispositional (e.g. negative beliefs about one’s own ability), and environmental (e.g. gender stereotypes about maths ability). A full review of this literature is beyond the scope of this article, but the main point to draw out here is that all of these factors can contribute to the feeling that “I am just not a ‘statistics person’”.
Dweck’s (2008) mindset theory tells us this is a ‘fixed mindset’ belief. That is, the ability to understand statistics is seen as a stable trait, often innate, and, therefore, something that we can’t do anything about – we’ve either got it or we haven’t. Crucially, students with fixed mindset beliefs about maths have a “significantly disadvantage” (Dweck, 2008, p.1). The good news is that mindsets can be changed. For example, an early study showed that a mindset intervention improved the maths GPAs of pupils in an American school (Blackwell, Trzesniewski, & Dweck, 2007) so there is good reason to expect an intervention with undergraduates taking a maths-based statistics module may have a similar impact.
There is, however, only one such study that has, to the best of my knowledge, explored growth mindsets in this context. Zonnefeld (2016) ran a study with 64 undergraduates taking an introductory statistics course. They were given 4 15-minute sessions within their existing teaching schedule that used a common and well-evidenced mindset intervention – teaching the basics of neuroplasticity (how the brain grows as we learn). The results showed improvements in self-reported effort and perceived course value as well as a greater rate of increased mastery of statistical concepts in females compared to males. An examination of previous years showed no such difference. At the University of Brighton, we are working on plans to run a similar project with our own research methods students and look forward to contributing to this fascinating but so far limited area of research.
In the meantime, we are ploughing on with our current intervention and offering staff some practical techniques that theory suggests will encourage positive attitudes to learning in students. We break these down into: teaching neuroplasticity, praising the process, role-modelling, creating a safe space for mistakes, providing opportunities for reflection, and developing new learning strategies. Having noticed how relevant mindset theory appears to be to the barriers of learning statistics, yesterday, I delivered a ‘Research Methods Edition’ of our staff workshop to some of the SASS team. After outlining the basic principles behind Dweck’s approach and discussing the importance of statistics anxiety we moved on to explore the potential of these techniques. Again, it is beyond the present scope to explore all of these but I would like to highlight two that I have not only found personally useful, but that can be easily applied in the statistics classroom and that seem to be resonating really strongly with staff and students: role-modelling and providing opportunities for reflection.
Early in my own (ongoing) statistical learning journey, I read a passage in Discovering Statistics for SPSS (Field, 2013) in which Field discloses an early school report with some pretty poor maths scores. I was aghast. Anyone who has taken an introductory statistics class will be familiar with Field’s books – to many of us, he is THE statistics person and yet here he was shattering my belief that he was born doing logistic regressions. This hailed both good and bad news for me. The good news was that if he could do it, perhaps I could too. The bad news was that it was going to take considerable effort. In this example, Field was acting as a role model.
Field seems particularly good at role-modelling for, in 2016’s An Adventure in Statistics, the story’s protagonist, Zach, goes on a pretty hair-raising adventure to track down his missing scientist-girlfriend, Alice. Zach isn’t a statistics person and not only has he lost his girlfriend but must now overcome his fear of statistics (and zombies) if he is ever to find out what happened to her. I won’t spoil the rest of the story but let’s just say that Zach is now a pretty proficient statistician. Thanks to a cat.
Upon talking to my statistics-teaching colleagues yesterday, I learned that they too don’t see themselves as statistics people and we all came to the conclusion that such a thing didn’t actually exist. I urged them to share this with their students. It is important for students to see that, whilst some people may take to statistics quicker than others (for reasons other than just ‘good maths genes’), the vast majority of us find it tricky and have had to work hard to get to grips with it. It is probable that, as I had, many students will assume us stats teachers are statistics-people and that they, by contrast, are not. By role-modelling our own learning journeys, they’ll be more likely to see that they are not incapable, just at the beginning of that same journey – in the same way that Field’s poor maths report showed me.
Usually when we speak of reflection with regards to research methods we think of qualitative research but it is actually something that lends itself particularly well to quantitative psychology too. It is good practice to keep a research journal or lab notebook to keep track of the challenges faced, decisions made, and skills developed as well as various technical aspects of our work. Looking back over what we have done not only provides us with a record to help us write up a report, but an opportunity to see for ourselves just how far we’ve come – to notice our own learning journeys.
Reflection can also be done in much more bite-sized, immediately impactful ways. For example, at the start of a seminar or workshop, give students three post-its. On one, ask them to write what they already know about the topic you’ll be teaching them and on the second they should write what they hope to learn. At the end of the session, they will note on the third post-it what they have learned. We ask staff to do this in our intervention workshops to demonstrate a simple way to make salient just how much knowledge they’ve acquired in a short space of time.
However you choose to do reflection, the purpose is to make something visible that is too often invisible. We tend to focus on the end result, not stopping to recall where we started. By encouraging students to reflect on their own learning they’ll pay more attention to how they learn, notice they have learned, and begin to develop a stronger belief in their ability to do so. In this way, they could eventually become their own role-models.
These are just two of the strategies that we can adopt to help alleviate statistics anxiety. By showing students that getting to grips with challenging material isn’t something that only a certain type of person can do, we’re moving away from a fixed mindset approach. It may help to reduce some of the anxiety they feel around not being able to ‘get it’ first time around and show them that with practise, they might just feel like a statistics person too. I look forward to exploring this further. With statistics.
Bessant, K. C. 1992. Instructional design and the development of statistical literacy. Teaching Sociology, 20, 143-149.
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), 246-263.
Blalock, H. M. 1987. Some general goals in teaching statistics. Teaching Sociology, 15, 164-172.
Conners, F. A., Mccown, S. M. & Roskos-Ewoldson, B. 1998. Unique challenges in teaching undergraduates statistics. Teaching of Psychology, 25, 40-42.
Field, A. P. 2010. Teaching Statistics. In: Upton, D. & Trapp, A. (Eds.) Teaching Psychology in Higher Education. Chichester, UK: Wiley-Blackwell.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th Ed). London: Sage.
Field, A. (2016). An Adventure in Statistics: The Reality Enigma. London: Sage.
Macher, D., Papousek, I., Ruggeri, K., & Paechter, M. (2015). Statistics anxiety and performance: blessings in disguise. Frontiers in Psychology, 6, 1116. http://doi.org/10.3389/fpsyg.2015.01116
Onwuegbuzie, A. J. & Wilson, V. A. 2003. Statistics Anxiety: nature, etiology, antecedents, effects, and treatments – a comprehensive review of the literature. Teaching in Higher Education, 8, 195-209.
Paxton, P. 2006. Dollars and sense: Convincing students that they can learn and want to learn statistics. Teaching Sociology, 34, 65-70.
Schutz, P. A., Drogosz, L. M., White, V. E. & Distefano, C. 1998. Prior knowledge, attitude, and strategy use in an introduction to statistics course. Learning and Individual Differences, 10, 291-308.
Zonnefeld, V. L. (2016). Effects of Growth Mindset Training on Undergraduate Statistics Students. Faculty Work: Comprehensive List. Paper 550. http://digitalcollections.dordt.edu/faculty_work/550
Disclaimer: the views, thoughts, and opinions expressed in this blog post belong solely to the author, and do not necessarily reflect the values of the University of Portsmouth or the extended Partnership.