The co-construction of data in-time: collaborative pedagogical encounters of golf instructors and students with data

Turcotte, Nate; Hollett, Ty; Merrell, Dan; Wager, Steve; Handley, Eric

doi:10.1080/17439884.2021.1960560

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…In our findings, we pay particular attention to how the environment in each of these episodes becomes part of the data encounter, and how data is integrated into instruction and is “felt” by the students (see also Lupton et al , 2018; Turcotte et al , 2021). Although these two episodes occur in two separate scenarios, the pedagogical trouble that distinguishes them is similar in nature.…”

Section: Resultsmentioning

confidence: 99%

“…We acknowledge that not all athletes leverage performance data equally, especially those who are not considered elite athletes. However, we suggest that the ability to "sense" performance data supports athletes' interpretation of personal data (Lupton, 2017;Rapp and Tirabeni, 2018;Turcotte et al, 2021). As such, we identify how athletes (golfers) use reflections of their bodies (and their data) to bring awareness to suboptimal aspects of their golf swing (e.g.…”

Section: Introductionmentioning

confidence: 82%

“…We argue that this article contributes to a growing area of scholarship focused on athletes' experiences with data (Clegg et al, 2020) through a microanalysis of how coaches and students use embodied data to inform training and instruction. Significantly, Matt and David's interactions with students can point those looking to leverage data-capturing technologies in learning settings to pedagogical practices that foster collective and qualitative uses of data (see also Turcotte et al, 2021). In sport, having an embodied awareness helps to achieve specific performances (Bäckström, 2014;Bergentoft, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…To pinpoint these moments, we follow Lupton et al (2018) who describe data sense-making as an embodied and sensory experience that provides athletes a “way of knowing about their bodies” and data (p. 661). One example of golfer’s data sense-making practices is Turcotte et al (2021) exploration of how golf coaches enact their bodies to support their students’ (golfers) understanding of club and ball flight data. More specifically, their work identified the scale of coaches’ and students’ embodied reenactments of performance in-the-moment.…”

Section: Introductionmentioning

confidence: 99%

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“Over 800 data points”: how coaches and athletes collectively navigate data-rich learning encounters

Turcotte

Hollett

2023

ILS

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Purpose The datafication of teaching and learning settings continues to be of broad interest to the learning sciences. In response, this study aims to explore a non-traditional learning setting, specifically two Golf Teaching and Research Programs, to investigate how athletes and coaches capture, analyze and use performance data to improve their practice. Athletic settings are well known for spurring the proliferation of personal data about performance across a range of contexts and ability levels. In these contexts, interest in athletes’ experiences with data has often been overshadowed by a focus on the technologies capturing the data and their capabilities. Design/methodology/approach This ethnographic research focuses on the data-rich experiences of golf coaches and students during two pedagogical encounters. Using Balka and Star’s (2015) concept of shadow bodies, this article explores how golfing bodies can become infused with data, creating partial representations of a lived experience that can be augmented and manipulated for pedagogical purposes, depending on the context and the individuals involved. Findings Interaction analysis helps the authors to examine the embodied and interactional nature of coach-golfer pedagogical encounters across two sites, a local Professional Golf Association golf course and a Swing Analysis Lab. The authors also split these encounters into two episodes to identify how coaches and golfers use partial representations of their bodies to analyze performance and interpret data. Originality/value This research suggests that as data-driven practices continue to engulf athletic settings, and teaching and learning settings broadly, emphasis needs to be placed on ensuring that athletes (learners) – from the most recreational to elite users – have an embodied understanding of their performance to improve their ability. Furthermore, this article raises questions about what data gets shared between instructors and athletes and how that data is used.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 82%