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
APA
Vancouver
Author
Bibtex
}
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