Dept of Mathematics Education seminar: 19 October 2022

40 mins Presentation + 20 mins Q&A: Natalie Flint

“Talking counts: What is Conversation Analysis and how can we use it to understand more about early years mathematics learning?

(Loughborough University) []


There are several phrases and idioms we regularly come across, such as:

            “They can talk the talk, but can they walk the walk”

            “They’re all talk, and no action”

            “A little less conversation, a little more action”

And they’re all used to demonstrate how talk is insignificant and not equivalent to action. However, talk is action, and is used to achieve all kinds of actions. In this talk, I will consider a few of these actions, such as diagnosing, warning, advising, arguing, teaching and learning, as well as other outcomes such as building relationships and doing identity work.

In this talk, I will discuss several projects I have worked on using Conversation Analysis (CA). CA is a method used to explore the details of talk and interaction. Naturally occurring talk is transcribed in detail, taking note of phonetic details, gestures, facial expressions and more, to consider both what is being said, but also how it is being said.

The projects I will discuss in this talk come from a variety of contexts, namely, clinical settings, public disputes, family interactions, and early years education settings. These projects will demonstrate how conversation analysis can be used, covering how conversation analysis can be applied to produce guidance, spotlighting how individuals defend and justify hatred and violence and to advise on how bigotry can be undermined and challenged, and finally, how children experience mathematics learning in early years education settings.

These interactional data will be analysed to explore how children learn and engage with mathematical thinking in their interactions with each other and with early years practitioners. The aim of this is to present the opportunities made available to us by Conversation Analysis, and demonstrate how this methodology can be used for research within the Centre for Early Mathematics Learning.

40 mins Presentation + 20 mins Q&A: Lilly Roth

The Ironman SNARC: Intra-individual stability of (numerical) cognition phenomena

(University of Tuebingen, Germany) []


In (numerical) cognition, many phenomena are investigated on a group level. Researchers often implicitly draw conclusions from their presence on group level to their presence on participant level and assume that single observations reflect typical behaviour of the participants. Sometimes researchers are even considering these individual scores as predictors for skills / achievements in other domains. However, as suggested by relatively low test-retest reliabilities of some (numerical) cognition effects, temporal stability of these individual scores should not be taken for granted. It can well be that the presence and magnitude of typical (numerical) cognition effects such as the SNARC effect (Spatial-Numerical Association of Response Codes, i.e., faster left-/right-sided responses to small/large number magnitudes, respectively; Dehaene et al., 1993) vary within participants or covary with the participant’s state at the time of measurement.

In a preregistered study, we explored the intra-individual stability of the SNARC effect, which is highly replicable on group level. In a unique approach, we asked ten participants to perform the same parity judgment task once a day on 30 within 40 consecutive days. We replicated the SNARC effect on a group level, resulting from a reliable SNARC effect in at least four of the ten participants, which is in line with the literature.

However, variations in the SNARC effect on participant level were dramatic. Crucially, no systematic trend could be observed for nine out of ten participants according to time-series analysis. Moreover, the SNARC effect did not correlate with our measures of participants’ state during the session: sleep duration the night before, tiredness, time of day, and consumption of stimulants. At the same time, we observed regularities in terms of general reaction time characteristics – participants consistently got faster across sessions.

These results put into question the validity of cognitive measures as predictors of mathematic skills and achievement. Thus, in follow-up studies we will investigate whether other (numerical) cognition phenomena that are highly robust on group level are stable within participants.

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Krzysztof Cipora
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