Network positions in active learning environments in physics

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Network positions in active learning environments in physics. / Traxler, Adrienne L.; Suda, Tyme; Brewe, Eric; Commeford, Kelley.

In: Physical Review Physics Education Research, Vol. 16, No. 2, 020129, 2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Traxler, AL, Suda, T, Brewe, E & Commeford, K 2020, 'Network positions in active learning environments in physics', Physical Review Physics Education Research, vol. 16, no. 2, 020129. https://doi.org/10.1103/PhysRevPhysEducRes.16.020129

APA

Traxler, A. L., Suda, T., Brewe, E., & Commeford, K. (2020). Network positions in active learning environments in physics. Physical Review Physics Education Research, 16(2), [020129]. https://doi.org/10.1103/PhysRevPhysEducRes.16.020129

Vancouver

Traxler AL, Suda T, Brewe E, Commeford K. Network positions in active learning environments in physics. Physical Review Physics Education Research. 2020;16(2). 020129. https://doi.org/10.1103/PhysRevPhysEducRes.16.020129

Author

Traxler, Adrienne L. ; Suda, Tyme ; Brewe, Eric ; Commeford, Kelley. / Network positions in active learning environments in physics. In: Physical Review Physics Education Research. 2020 ; Vol. 16, No. 2.

Bibtex

@article{6ace0fdace9a4bd5bc63aeddbe2d68b8,
title = "Network positions in active learning environments in physics",
abstract = "This study uses positional analysis to describe the student interaction networks in four research-based introductory physics curricula. Positional analysis is a technique for simplifying the structure of a network into blocks of actors whose connections are more similar to each other than to the rest of the network. This method describes social structure in a way that is comparable between networks of different sizes and densities and can show large-scale patterns such as hierarchy or brokering among actors. We detail the method and apply it to class sections using Peer Instruction, SCALE-UP, ISLE, and context-rich problems. At the level of detail shown in the blockmodels, most of the curricula are more alike than different, showing a late-term tendency to form coherent subgroups that communicate actively among themselves but have few inter-position links. This pattern may be a network signature of active learning classes, but wider data collection is needed to investigate.",
author = "Traxler, {Adrienne L.} and Tyme Suda and Eric Brewe and Kelley Commeford",
year = "2020",
doi = "10.1103/PhysRevPhysEducRes.16.020129",
language = "English",
volume = "16",
journal = "Physical Review Physics Education Research",
issn = "2469-9896",
publisher = "American Physical Society",
number = "2",

}

RIS

TY - JOUR

T1 - Network positions in active learning environments in physics

AU - Traxler, Adrienne L.

AU - Suda, Tyme

AU - Brewe, Eric

AU - Commeford, Kelley

PY - 2020

Y1 - 2020

N2 - This study uses positional analysis to describe the student interaction networks in four research-based introductory physics curricula. Positional analysis is a technique for simplifying the structure of a network into blocks of actors whose connections are more similar to each other than to the rest of the network. This method describes social structure in a way that is comparable between networks of different sizes and densities and can show large-scale patterns such as hierarchy or brokering among actors. We detail the method and apply it to class sections using Peer Instruction, SCALE-UP, ISLE, and context-rich problems. At the level of detail shown in the blockmodels, most of the curricula are more alike than different, showing a late-term tendency to form coherent subgroups that communicate actively among themselves but have few inter-position links. This pattern may be a network signature of active learning classes, but wider data collection is needed to investigate.

AB - This study uses positional analysis to describe the student interaction networks in four research-based introductory physics curricula. Positional analysis is a technique for simplifying the structure of a network into blocks of actors whose connections are more similar to each other than to the rest of the network. This method describes social structure in a way that is comparable between networks of different sizes and densities and can show large-scale patterns such as hierarchy or brokering among actors. We detail the method and apply it to class sections using Peer Instruction, SCALE-UP, ISLE, and context-rich problems. At the level of detail shown in the blockmodels, most of the curricula are more alike than different, showing a late-term tendency to form coherent subgroups that communicate actively among themselves but have few inter-position links. This pattern may be a network signature of active learning classes, but wider data collection is needed to investigate.

U2 - 10.1103/PhysRevPhysEducRes.16.020129

DO - 10.1103/PhysRevPhysEducRes.16.020129

M3 - Journal article

VL - 16

JO - Physical Review Physics Education Research

JF - Physical Review Physics Education Research

SN - 2469-9896

IS - 2

M1 - 020129

ER -

ID: 332624890