Wearable Learning-Reference-Cited by-同舟云学术

Wearable Learning

Published:2017-10-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Annual Symposium on Computer-Human Interaction in Play
language:
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Author:

Arroyo Ivon¹,Micciollo Matthew¹,Casano Jonathan²,Ottmar Erin¹,Hulse Taylyn¹,Rodrigo Ma. Mercedes²

Affiliation:

1. Worcester Polytechnic Institute, Worcester, MA, USA

2. Ateneo de Manila University, Manila, Philippines

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3116595.3116637

Reference40 articles.

1. Embodied design: constructing means for constructing meaning

2. Learning Is Moving in New Ways: The Ecological Dynamics of Mathematics Education

3. Alibali M.W. & Nathan M.J. (2012). Embodiment in Mathematics Teaching and Learning: Evidence From Survey Question (min=1; max=4) Mean (SD) MODE How much did you know about state machines BEFORE you started? (1=Nothing; 4=A lot) 1.65 (0.91) (Nothing) How much do you feel you know about state machines NOW? (1=Nothing; 4=A lot) 2.94 (0.81) (Do know) After looking at Estimate It how difficult was it to determine its underlying state based design? (1=Easy; 4=Hard) 1.71 (0.78) (Easy) How difficult was it to adapt your game to a state based design? (1=Easy; 4=Hard) 1.74 (0.82) (Easy) How difficult was it for you to design a math game given the set of constraints during day 1? (1=Easy; 4=Hard) 1.84 (0.73) (Sort of Easy) How similar was the game you designed to Estimate It by the end of the FIRST day you created the math game? (1=Not Similar; 4=Very Similar) 1.35 (0.71) (Not Similar) How similar was the math game you designed to Estimate It by the end of TODAY after redesigning the game -- (1=Not Very Similar; 4=Very Similar) 1.94 (0.73) (A bit similar) Table 4. Means Standard deviations and Modes of High School Students' Survey Responses about Creating Mathematics Games as Finite State-Machines Learners' and Teachers' Gestures. Journal of the Learning Sciences 21(2) 247--286. 10.1080/10508406.2011.611446 Alibali M.W. & Nathan M.J. (2012). Embodiment in Mathematics Teaching and Learning: Evidence From Survey Question (min=1; max=4) Mean (SD) MODE How much did you know about state machines BEFORE you started? (1=Nothing; 4=A lot) 1.65 (0.91) (Nothing) How much do you feel you know about state machines NOW? (1=Nothing; 4=A lot) 2.94 (0.81) (Do know) After looking at Estimate It how difficult was it to determine its underlying state based design? (1=Easy; 4=Hard) 1.71 (0.78) (Easy) How difficult was it to adapt your game to a state based design? (1=Easy; 4=Hard) 1.74 (0.82) (Easy) How difficult was it for you to design a math game given the set of constraints during day 1? (1=Easy; 4=Hard) 1.84 (0.73) (Sort of Easy) How similar was the game you designed to Estimate It by the end of the FIRST day you created the math game? (1=Not Similar; 4=Very Similar) 1.35 (0.71) (Not Similar) How similar was the math game you designed to Estimate It by the end of TODAY after redesigning the game -- (1=Not Very Similar; 4=Very Similar) 1.94 (0.73) (A bit similar) Table 4. Means Standard deviations and Modes of High School Students' Survey Responses about Creating Mathematics Games as Finite State-Machines Learners' and Teachers' Gestures. Journal of the Learning Sciences 21(2) 247--286. 10.1080/10508406.2011.611446

4. What the body knows: Exploring the benefits of embodied metaphors in hybrid physical digital environments

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