Introduction of a social constructivist approach to statistics teaching in psychology

There is increasing impetus towards a reform of statistics teaching in the face of a widespread recognition that traditional approaches to statistics education focussed on computational and analytical skills does not provide students with the ability to apply statistical thinking in real world situations (Garfield et al 2002). In response to these concerns I have changed the way statistics is taught to second year psychology students aiming to encourage “statistical thinking and literacy”. Moore (1997) argues that the crisis in statistics education derives from a traditional approach that teaches statistics to non-statisticians as if the students were trainee statisticians - the ‘professional’s fallacy’. He argues instead for a revised approach requiring ‘the abandonment of an "information transfer" model in favor of a "constructivist" view of learning:..” (Moore, 1997) and that non-specialist statistics education should focus on emphasising statistical thinking, incorporate a more data-focussed and conceptual approach, be less formulaic, emphasise graphical concepts and the automation of calculations, and foster active learning because “the most effective learning takes place when content (what we want students to learn), pedagogy (what we do to help them learn), and technology reinforce each other in a balanced manner.” (Moore, 1997). Furthermore, recent research suggests that there are clear benefits of collaborative working involving opportunities for peer review and feedback, particularly to female students who tend to use such opportunities most effectively (Wessa, 2008).

I have introduced support for constructivist learning in statistics education in psychology at Aston University by adopting methods introduced by Wessa using the internet based reproducible computing framework available via http://www.freesatistics.org and a supporting online peer review software system (Wessa, 2009). Students receive instruction via traditional lectures and supporting workshops, but the workshop material is presented as a ‘compendium’, an enhanced document form containing all the data and computations necessary to fully reproduce and communicate the results of a statistical analysis. The students’ task is to complete workshop assignments and create a new compendium based on their own analysis and interpretation that is then ‘blogged’ (i.e. archived); this document is then circulated anonymously to students’ peers for review. The students’ assessed assignment is then to provide constructive peer review feedback on up to 5 workshop compendiums they receive. This aspect provides the social constructivist framework within which independent learning can flourish.


Garfield J; Hogg B, Schau C &  Whittinghill D. “First Courses in Statistical Science: The Status of Educational Reform Efforts.” Journal of Statistics Education Volume 10, Number 2 (2002)

Moore, DS (1997) “New Pedagogy and New Content: The Case of Statistics” International Statistical Review  vol. 65 (2), pp123-165

Wessa, P. (2008). How Reproducible Research Leads to non-Rote Learning Within a Socially Constructivist e-Learning Environment. 7TH European Conference on E-Learning, Vol 2, 651-658.  

Wessa, P.A (2009) A framework for statistical software development, maintenance, and publishing within an open-access business model. Computational Statistics, 24(2), 183-193


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