Let's Get Physical: K-12 Students Using Wearable Devices to Obtain and Learn About Data from Physical Activities

Lee, Victor R.; Drake, Joel; Williamson, Kylie

doi:10.1007/s11528-015-0870-x

Cited by 35 publications

(24 citation statements)

References 14 publications

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“…In some situations, youth can be inherently skeptical about what a device tells them. That skepticism can be an important learning opportunity about measurement, approximation, and instrumentation (Lee, Drake, & Williamson, 2015). Regardless, the idea that she must have awoken in the middle of the night provided Melissa a new way to think about sleep and was supported by comments from others.…”

Section: Commentary On Melissa's Casementioning

confidence: 98%

Personal Analytics Explorations to Support Youth Learning

Lee

2018

Digital Technologies and Instructional Design for Personalized Learning

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While personalized learning environments often include systems that automatically adapt to inferred learner needs, other forms of personalized learning exist. One form involves the use of personal analytics in which the learner obtains and analyzes data about himself/herself. More known in informatics communities, there is potential for use of personal analytics for design of instruction. This chapter provides two cases of personal analytics learning explorations to demonstrate their range and potential. One case is of a high school student examining how sleep influences her mood. The other case is of a sixth-grade class of students examining how deviations from typical walking behavior change distributional shape in plotted step data. Both cases show how social support and direct experience with data correction are intimately involved in how youth can learn through personal analytics activities.

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Section: Commentary On Melissa's Casementioning

confidence: 98%

Personal Analytics Explorations to Support Youth Learning

Lee

2018

Digital Technologies and Instructional Design for Personalized Learning

Self Cite

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“…From this perspective, data science education is an expansive domain that includes teaching and learning data science in different contexts (e.g., K-12, postsecondary, industry, online, and informal settings), and examples of data science education include graduate seminars on data science methods (Schneider et al, 2020), workshops and training (Anderson & Rosenberg, 2019), and K-12 courses that engage students in working with data. These K-12 courses are often situated in mathematics and science content or classes (e.g., Hancock et al,1992;Lehrer & Schauble, 2004;Lee et al, 2015), but occasionally in other content areas (e.g., social studies; Drier & Lee, 2008;Lehrer & Romberg, 1996).…”

Section: Data Science For Education: Data Science As a Teaching And Lmentioning

confidence: 99%

“…LDT scholars have been at the forefront of efforts to identify ways to address new and long-standing questions using novel data sources. For example, LDT researchers have found ways to utilize novel datasets from social media to understand the role of social media in teaching and learning (Coughlan, 2019;Greenhalgh et al, 2020;Kimmons & Smith, 2019;Romero-Hall et al, 2018), use telemetric data collected as students interact with educational technology to gain insight into students' motivation and learning (e.g., Bernacki et al, 2015;Peddycord-Liu et al, 2018;Rodriguez et al, 2019), and use datasets from wearable devices to engage and understand the learning of K-12 students about data analysis and interpretation (Lee et al, 2015). However, the LDT field's creative integration of novel datasets also brings with it new challenges.…”

Section: Introductionmentioning

confidence: 99%

Making Data Science Count In and For Education

Rosenberg¹,

Lawson²,

Anderson³

et al. 2019

Preprint

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New data sources and analytic techniques have enabled educational researchers to ask new questions and work to address enduring problems. Yet, there are challenges to those learning and applying these methods. In this chapter, we provide an overview of a nascent area of both scholarship and teaching, educational data science. We define educational data science as the combination of capabilities related to quantitative methods in educational research, computer science and programming capabilities, and teaching, learning, and educational systems. We demonstrate that there are two distinct-but complementary-perspectives on educational data science in terms of being both in education (as a research methodology) and for education (as a teaching and learning content). We describe both of these areas in light of foundational and recent research. Lastly, we highlight three future directions for educational data science, emphasizing the synergies between these two perspectives concerning designing tools that can be used by both learners and professionals, foregrounding representation, inclusivity, and access as first-order concerns for those involved in the growing community, and using data science methodologies to study teaching and learning about data science. We highlight the potential for

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“…Wearable devices have entered the world of K-12 health and physical education. Lee, Drake, and Williamson (2015), for example, describe a number of activities in which students combine fitness activities with analysis of data from wearable trackers, including high school students using heart rate monitors to compare their heart rates while engaged in different types of exercise, and elementary students using activity trackers to record how active they are at recess. Another creative approach, initiative games, is described by Maina, Maina, and Hunt (2016a,b) as combining physical education and critical thinking skills.…”

Section: Health and Physical Dimensionmentioning

confidence: 99%

Supervision's New Challenge: Facilitating a Multidimensional Curriculum

Gordon¹

2018

JES

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In this article I propose that curriculum and instruction are inextricably intertwined, curriculum development should be an important function of educational supervision, and supervision should foster a multidimensional curriculum developed by teachers. The proposed curriculum framework includes cognitive, social-emotional, moral, cultural, democratic, creative-artistic, and health and physical dimensions. I provide a rationale for including each of the seven dimensions, and recommend integrating the seven dimensions within a holistic curriculum. I contend that each dimension of the proposed curriculum will promote learning in the other dimensions. The suggested curriculum development process involves the supervisor facilitating professional development for teachers and curriculum design by teachers. Finally, I recommend a three-phase model for curriculum development leading to the multidimensional curriculum, with continuous curriculum development at the school, team, and individual level.

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Let's Get Physical: K-12 Students Using Wearable Devices to Obtain and Learn About Data from Physical Activities

Cited by 35 publications

References 14 publications

Personal Analytics Explorations to Support Youth Learning

Personal Analytics Explorations to Support Youth Learning

Making Data Science Count In and For Education

Supervision's New Challenge: Facilitating a Multidimensional Curriculum

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