Investigating physics learning with layered student interaction networks: Combining time and mode of interaction

Research output: Contribution to conference › Poster › Research › peer-review

Standard

Investigating physics learning with layered student interaction networks : Combining time and mode of interaction. / Bruun, Jesper; Traxler, Adrienne.

2016. Poster session presented at Physics Education Research 2016, Sacramento, United States.

Research output: Contribution to conference › Poster › Research › peer-review

Harvard

Bruun, J & Traxler, A 2016, 'Investigating physics learning with layered student interaction networks: Combining time and mode of interaction', Physics Education Research 2016, Sacramento, United States, 21/07/2016 - 22/07/2016.

APA

Bruun, J., & Traxler, A. (2016). Investigating physics learning with layered student interaction networks: Combining time and mode of interaction. Poster session presented at Physics Education Research 2016, Sacramento, United States.

Vancouver

Bruun J, Traxler A. Investigating physics learning with layered student interaction networks: Combining time and mode of interaction. 2016. Poster session presented at Physics Education Research 2016, Sacramento, United States.

Author

Bruun, Jesper ; Traxler, Adrienne. / Investigating physics learning with layered student interaction networks : Combining time and mode of interaction. Poster session presented at Physics Education Research 2016, Sacramento, United States.1 p.

Bibtex

@conference{5171871289fb4cd0b269bfa486d26b37,
title = "Investigating physics learning with layered student interaction networks: Combining time and mode of interaction",
abstract = "Centrality in student interaction networks (SINs) can be linkedto variables like grades [1], persistence [2], and participation[3]. Recent efforts in the field of network science have beendone to investigate layered - or multiplex - networks asmathematical objects [4]. These networks can be exploredvia centrality measures, which then have to be modified tosuit layered networks. In student interaction networks [1],a node represents aspects of a student, and links representaspects of student interactions. Using longitudinal selfreportedinteractions from Danish university students,this study investigates how target entropy [5,1] and pagerank[6,7] are affected when we take time and modes ofinteraction into account. We present our preliminarymodels and results and outline our future work inthis area.",
author = "Jesper Bruun and Adrienne Traxler",
year = "2016",
month = jul,
day = "22",
language = "English",
note = "Physics Education Research 2016 : A Methodological Approach to PER, PERC2016 ; Conference date: 21-07-2016 Through 22-07-2016",
url = "http://www.compadre.org/per/conferences/2016/",

}

RIS

TY - CONF

T1 - Investigating physics learning with layered student interaction networks

T2 - Physics Education Research 2016

AU - Bruun, Jesper

AU - Traxler, Adrienne

PY - 2016/7/22

Y1 - 2016/7/22

N2 - Centrality in student interaction networks (SINs) can be linkedto variables like grades [1], persistence [2], and participation[3]. Recent efforts in the field of network science have beendone to investigate layered - or multiplex - networks asmathematical objects [4]. These networks can be exploredvia centrality measures, which then have to be modified tosuit layered networks. In student interaction networks [1],a node represents aspects of a student, and links representaspects of student interactions. Using longitudinal selfreportedinteractions from Danish university students,this study investigates how target entropy [5,1] and pagerank[6,7] are affected when we take time and modes ofinteraction into account. We present our preliminarymodels and results and outline our future work inthis area.

AB - Centrality in student interaction networks (SINs) can be linkedto variables like grades [1], persistence [2], and participation[3]. Recent efforts in the field of network science have beendone to investigate layered - or multiplex - networks asmathematical objects [4]. These networks can be exploredvia centrality measures, which then have to be modified tosuit layered networks. In student interaction networks [1],a node represents aspects of a student, and links representaspects of student interactions. Using longitudinal selfreportedinteractions from Danish university students,this study investigates how target entropy [5,1] and pagerank[6,7] are affected when we take time and modes ofinteraction into account. We present our preliminarymodels and results and outline our future work inthis area.

M3 - Poster

Y2 - 21 July 2016 through 22 July 2016

ER -

ID: 164113714