Questioning the stability of academic buoyancy.

Back in October I conducted a small-scale exploratory study into three constructs (academic self-concept, academic buoyancy and implicit theories of intelligence). You can read the details here. A few weeks ago I asked the same students toStressed-Student complete the questionnaires again to confirm that these constructs remain stable over time. I was particularly interested in academic buoyancy (day-to-day resilience) due to the forthcoming AS exams. What I wanted to confirm was that those students who considered themselves resilient at time 1 (October 2013) still considered themselves resilient at time 2 (May 2014). This would be measured using the Academic Buoyancy Scale (Martin and Marsh, 2007), a four-item measure of academic buoyancy (AB) that has proved reliable over time and within different settings.

Let’s get some of the problems with the ‘study’ out of the way now.

At time 1, the sample consisted of 41 year 12 students. At time 2, and due to a number of factors (including subject/school drop-out and a lower volunteer rate) this had dropped to 27. The final sample is therefore very low and is far from representative.

The sample is small and unrepresentative – predominantly white, middle-class and with a higher percentage of female participants.

However, as this was an exploratory study, I was looking for general patterns needed to establish possible further avenues of investigation.

Ethical Issues.

The study was conducted in line with ethical procedures of the University of York. Participants were volunteers and gave informed written consent (all participants were over the age of 16). They had the right to withdraw from the study as any time (including the withdrawal of their data).

What did the data show?

Data analysis was conducted using the R statistical package. The results of the t-test found a significant difference between AB at time 1 and AB at time 2 (p<0.01). Further analysis found an effect size of 0.675. If we apply Cohen’s (1988) conventions for effect size, we also find a highly significant difference between time 1 and time 2 (so we can be pretty confident that timing was a major factor).

What does all this mean?

Results would suggest that AB isn’t stable and is mitigated by other factors. The timing of the second data collection activity (a week before the start of AS exams) could play a role in the difference between the two sets of scores, begging the question “Do students feel less confident about their abilities at different times?” Outcome measures (in the form of AS results) can be examined in August and could (but only ‘could’) yield more information.

Where now?

The plan now is to use experience-sampling methods (ESM) to collect data on a number of factors ‘as they happen’. The problem with much of the research into academic buoyancy is that participants are asked to complete measures in isolation (i.e. “I am good at dealing with setbacks”). ESM allows for participants to think about these measures in a more realistic and moment-by-moment way via electronic ‘prompts’ sent to mobile devices. ESM tends to result in large data sets, dependent upon the number of prompts and length of the study, so sample sizes can be smaller (and, for practical reasons, need to be). An additional possibility would be to supplement the ESM data with a end of day/end of week questionnaire to investigate the difference between immediate and retrospective self-assessments.

What’s the point?

Emotion appears to impact on learning. Research has suggested that factors such as self-concept, boredom, anxiety and resilience can have both positive and negative effects on academic outcomes, as well as cognitive functions like attention. Understanding the nature of these factors could help to develop interventions to stabilise some of them. Emotion impacts on cognition, for example, stress can heighten recall to a point but too much anxiety leads to inaccurate recall. The so-called Yerkes-Dodson suggests that performance increases as physiological and mental arousal increases to an optimum level, at which point cognitive functions begin to decline. Although the Yerkes-Dodson law in somewhat dated, more recent research appears to support its validity.

In a system where more and more of our young people are suffering from heightened levels of anxiety (the reason for which is highly debatable), examining their daily classroom lives can be provide rich data into how, when and why they do and do not learn.

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