Setting a personal best goal within every-day classroom activities can improve student performance and, significantly, it can be achieved through a very simple change to an existing classroom exercise.
Results of our latest research study on setting personal best goals, by our team from the University of New South Wales and University of Sydney, may have a broad application across all classrooms.
We used classroom-based problem-solving exercises and made just one simple change to instructions given to students. While we used a mathematic exercise in this instance, we believe any teacher of any subject could use this technique in any classroom.
What does setting a personal best goal involve?
Definition of personal best (PB) goals
Scientia Professor and Professor of Educational Psychology at the University of New South Wales, Andrew Martin, has defined personal best (PB) goals as specific, challenging, and competitively self-referenced goals that involve a level of performance that meets or exceeds an individual’s previous best.
Setting goals and our previous studies
Most of the available research on personal best goals has been survey-based. For example, in an earlier study we surveyed 249 high school students twice across a year about their use of PB goals using responses to PB questions (e.g., “When I do my schoolwork I try to do it better than I’ve done before”). We found that the relationships between PB goals and deep learning, academic flow, and positive teacher relationship remained significant beyond their previous scores on these factors. We believe these results support the importance of PB goals in school settings.
More recent investigations have tested the effects of PB goal setting using experimental research methods. In the context of an annual mathematics contest, Professor Martin and professor of psychology at the University of Rochester, Andrew Elliot, invited some students to set a PB goal based on their previous year’s performance, while others were simply reminded of their previous year’s performance. Students in the PB goal condition substantially outperformed the control condition.
Experimental investigations such as this provide strong tests of causal hypotheses (where you can predict what will happen when you change one thing in a relationship). In this case, we could predict that PB goals act to enhance learning processes and/or outcomes for students.
Our latest study
Our latest study aimed to test whether PB goals can enhance mathematics performance in a primary school context, using an existing class activity, SuperSpeed Math, developed by Chris Biffle of Whole Brain Teaching.
In this study, we aimed to boost short-term improvement in arithmetical problem-solving fluency, across a range of arithmetical skills, under time pressure to build number fluency.
Describing how the design of SuperSpeed Math incorporates PB goals, Biffle argues:
Students love to play SuperSpeed Math because they love to strive for goals and to set and break personal records. Players are never competing against each other, but against their own previous best effort. Thus, the learning objective is set at exactly the right level, no matter a player’s ability.
What we did
Our study involved 68 children across Years 5 (35 students) and Year 6 (33 students) who chose to participate. A research assistant worked with each child for approximately 35-40 minutes.
Students began by completing a short paper-based questionnaire to gauge
(a) their prior ability in mathematics,
(b) their use of and the classroom emphasis on Personal Best, Mastery, and Performance (competitive) goals in learning mathematics;
(c) their mathematics self-concept; and
(e) students’ valuing of and interest in mathematics.
Students were then randomly assigned to the Personal Best condition (compete against your own previous Personal Best score on mental maths questions), and a No Goal comparison condition. In the first round of a set of mental maths, students would follow these instructions:
“We’re now going to do some mental maths questions. This is how we’ll work together.
I’m going to give you some sheets of paper with mental maths questions on them. For each sheet of questions, your task is to answer as many questions as you can in 60 seconds.
I’ll give you some feedback on how many you got correct, then we’ll go through that sheet of questions again. Please start with the questions on the top row, going from left to right, then the second row from left to right, and so on.”
For the PB condition, after each initial 1-minute attempt at a set of initial test questions (e.g., on addition), the researcher gave the following instructions:
“OK, your score on these addition questions was [X]. This is your Personal Best score. Now we’re going to do these questions again, and I would like you to set a goal where you aim to do better on these questions than you did before.”
For the students in the comparison condition, the only difference to the experimental (PB) condition was that students in the condition were informed,
“OK, your score on these [addition] questions was [X]. Now we’re going to do these questions again”.
All students completed 10 sets of two rounds of arithmetic problem-solving, that is 20 rounds in total.
As expected, there were no statistically reliable differences between the two groups on the “pre-experiment” prior mathematics ability test or self-reports on Personal Best, Mastery, and Performance goals.
Our primary research question focused on the number of mental mathematics questions solved by the two groups. We found a small but statistically reliable difference between the two groups in favour of the Personal Best goals group.
Our study shows that setting a Personal Best goal during classroom-based problem-solving exercises improves performance.
What does this mean for teaching and learning?
We believe these results may have broad application in classrooms. Although the effect of setting a PB goal was relatively small, it was achieved through a very simple change to an existing classroom exercise.
John Hattie has argued small effects may still be practically important when considered in context. In our study, we aimed to boost short-term improvement in arithmetical problem-solving fluency, across a range of arithmetical skills, under time pressure to build number fluency.
A large change to such a complex skill-set probably won’t happen through a ‘one-shot’ intervention. Instead, students will engage in such fluency-building activities many times over the course of weeks or even months. We believe the small effect found in our study can be the start of a “virtuous cycle” of enhanced learning and performance. Beyond exercises like SuperSpeed Math, PB goals can potentially play a number of roles in students’ goal-setting and school reporting.
Our study focused on Personal Bests in mental maths, but there are many opportunities to set and strive for Personal Bests in other subjects and skill-sets. For example, students may strive to spell more words correctly in a forthcoming spelling test than they had in the previous test; they may aim to read an extra book or resource for an assignment than they had done before; before a test they may do some revision over the weekend when they previously had done none; or, they may aim to ask a teacher for help when they had previously been reluctant to do so. In all such cases, students are their own benchmark and their own point of reference for self-improvement.
More broadly, these findings are consistent with a “growth mindset”, which holds that every child is capable of learning given the right opportunities, access to productive strategies, effort, and support.
Here is our paper in the Australian Education Researcher Personal Best (PB) goal-setting enhances arithmetical problem-solving
If you would like to know more about our Growth Mindset work please visit the Growth Mindset site
Paul Ginns is Associate Professor of Educational Psychology in the School of Education and Social Work at the University of Sydney. Paul uses numerous research methodologies (for example, experimental and survey-based research) and analytic methods, including general linear models, exploratory and confirmatory factor analysis, structural modelling and meta-analysis, to investigate student learning.
Professor Andrew Martin, PhD, is Scientia Professor, Professor of Educational Psychology, and Co-Chair of the Educational Psychology Research Group in the School of Education at the University of New South Wales, Australia. He specialises in student motivation, engagement, achievement, and quantitative research methods.