Chris read Is it time to rethink the way university lectures are delivered?, a short article about a Science paper from 2011. A class of Canadian physics-major freshmen was split into two and one week of material was delivered differently to the two halves of the class. The first half stuck to the tried and tested lecture-using-powerpoint format, whilst the other half used a more ‘interactive’ approach termed ‘deliberate practice’: discussion groups, preclass reading assignments, in-class clicker-questions, online quizzes. Lo and behold, in a test the following week the second cohort scored 74% on a test about the material and the other half only got 41%, thus illustrating that three days later they could remember the material better. The study has come in for a lot of criticism about methodology – only 211 of 271 students actually took the test (how would the others have altered the results?), and the people that designed it were also the ones that delivered the intervention so may well have been ‘teaching to the test’. However, the general feeling seems to be that though the study is flawed, the conclusions are broadly correct. It also illustrates that having a Nobel Prize allows you to publish anything you like anywhere you want.
Chris also read A better way to practice, 2012 . Written by Noa Kagayame, a Julliard School of Music violinist turned performance psychologist. His argument is that it is better to practice smart than practice hard – take home aphorisms from this article are Practice makes permanent and Perfect practice makes perfect, the implication being that unless you practice correctly you can reinforce bad habits. That seems logical enough. He also argues that more thoughtful study can reduce the time needed for practice and increase the likelihood of successful performance, but I (and many of the commenters below the fold) disagree with him about this. Whilst this might be true at the highest levels, at lower levels when it’s all about training muscle memory there’s simply no substitute for doing it over and over again.
Steve watched The key to success? Grit and read True Grit, Angela Lee Duckworth & Lauren Eskreis-Winkler, 2013. I’d phrase ‘grit’ as perseverance – effort and stamina to achieve something difficult over an extended period of time. In the Tortoise and the Hare, the hare has talent, but the tortoise has grit and achieves more in the end. This summary indicates that talent and grit are often orthogonal, or negatively correlated. In the past persistence was assessed against physical challenges, but this may not relate to long-term mental grit. Modern assessment is by questioning against traits e.g. ‘I finish whatever I begin’. ((to complete)).
Suzi read Stereotype threat and women’s math performance and Mindsets and Math/Science Achievement
Both papers discuss how mindset might affect learning.
Stereotype threat is a stress-induced threat of self-fulfilling a negative and well-known stereotype. For example an elderly man looking for his keys may worry about looking senile, become stressed, and so find it harder to find his keys. The paper puts forward evidence that women’s performance in difficult maths tests can be affected by the threat of fulfilling a negative stereotype: that maths is not a girls subject. Other studies have looked at stereotype threat in relation to racial stereotypes.
Growth mindset is the belief that intelligence can be improved. Not everyone has it, others have a “fixed mindset”. Many people will tell you that they are just not a maths person. The paper states that mindsets can predict maths/science performance over time, and can mitigate for negative effects such as stereotype threat.
Both are interesting and seem plausible. Some of the suggested strategies for reducing stereotype threat and/or increasing growth mindset are:
- feedback should emphasise the high standards of the test, and that the student has the potential to meet them
- frame high-stakes tests as “assessing current skills and not long-term potential to learn”
- praise effort and process, not intelligence
- describe great mathematicians and scientists as people who loved and devoted themselves to the subject (not born geniuses)
