Bruun, Jesper

Traxler, Adrienne

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Copenhagen University Research Information System

u n i ve r s i t y  o f  co pe n h ag e n  Københavns UniversitetInvestigating physics learning with layered student interaction networksBruun, Jesper; Traxler, AdriennePublication date:2016Document versionPeer reviewed versionDocument license:GNU GPLCitation for published version (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.Download date: 03. Feb. 2020 Figure 1: Layered networks. The leftmost figure shows sample student interaction network layers for one week. A student potentially has a presence in each layer depending on the connections in that layer. The rightmost figure shows that layered networks can also be understood as a time sequence.Pagerank time model and category modelIn a layered network, a random walker move both within and between layers [6,7]. The rank of a given  node is the percentage of times that node is visited by the random walker.Layered category networks can be seen tomodel modelling how individuals may engage with different kinds of knowledge and/or practices. Layered time networks within a single category al-low us to model individuals varying engagementwith knowledge and/or practices. Target entropy time modelTarget entropy can be seen to model how likely a student is to engage with, for example, new ideas, new ways of doing og being, or concepts - here modelled as “messages”. With our time model, we introduce the idea that a student might not only engage with other students but also has a memory of previous weeks. I nves t i ga t i ng  phys i cs  l ea rn ing  w i t h  l aye red  s tuden t  i n te rac t i on  ne two rks :Combin ing t ime and modes of  in teract ionJesper Bruun, University of Copenhagen and Adrienne Traxler, Wright State UniversityContactJesper BruunAssistant Professorjbruun@ind.ku.dkDepartment of Science EducationFaculty of ScienceUniversity of CopenhagenAdrienne TraxlerAssistant Professoradrienne.traxler@wright.eduDepartment of PhysicsWright State University1. Interaction in layered networksCentrality 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 as mathematical objects [4]. These networks can be explored via 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 represent aspects of student interactions. Using longitudinal self-reported interactions from Danish university students,this study investigates how target entropy [5,1] and page-rank [6,7] are affected when we take time and modes ofinteraction into account. We present our preliminary models and  results and outline our future work in this area.2. Models of interaction and engagement with categories and time3. Preliminary results and ideas for next stepsSelected Literature[1]Bruun, J., & Brewe, E. (2013). Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores. Physical Review Special Topics-Physics Education Research, 9(2), 020109.[2] Forsman, J. (2015). Complexity Theory and Physics Education Research: The Case of Student Retention in Physics and Related Degree Programmes. PhD thesis. University of Uppsala.[3] Brewe, E., Kramer, L., & Sawtelle, V. (2012). Investigating student communities with network analysis of interactions in a physics learning center. Physical Review Special Topics-Physics Education Research, 8(1), 010101.[4] Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of complex networks, 2(3), 203-271.[5] Sneppen, K., Trusina, A., & Rosvall, M. (2005). Hide-and-seek on complex networks. EPL (Europhysics Letters), 69(5), 853.[6] Brin, S., & Page, L. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer networks, 56(18), 3825-3833.[7] De Domenico, M., Lancichinetti, A., Arenas, A., & Rosvall, M. (2015). Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Physical Review X, 5(1), 011027. AEBFC DGAEBFC DGAEBFC DGProblem solvingConcept discussionSocializing during classAEBFC DGAEBFC DGTimett+1The analysis is based on problem solving (PS) and concept discussion (CD) SINs. We use the same dataset as in [1] and focus on PS and CD interactions. For each week, students have indicated with whom they remember having discussed problem solving and concepts in physics respectively. We use the correlations pagerank~PS,CD and target entropy~CD [1] as benchmarks. For each model we calculate the layered centralitiy and compare with the corresponding benchmark. We found that PageRank on the agglomorated PS-CD layered network did worse than the benchmark for both CD or PS networks. Target entropy ofnode iFraction of “messages”from node j to node iTotal “messages” to node i at time t. δ isa memory-effect. iit-1jkln=8n=5n=4n=1Figure 2: Illustration of our timemodel for target entropy.Next steps● Try out other models and combinations for memory and layered models● Extend analysis to other cohorts ● Investigate layered networks with respect to student communities● Use records of of student course comple-tion as they are registered at UCPH0.00.20.4Week combinationsCorrellation with Sum of GradesBenchmark1 1-7 72-7 3-7 4-7 5-7 6-71-61-51-41-31-2Figure 3: How multilayer pagerank compares to the benchmark for PS SINs.Numbers on x-axis refer to combinations of weeksFigure 4: How target entropy time model compares to the benchmark for CD SINs.Numbers on  x-axisrefer to different settings of parameter δ. Place paper with graph here

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

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

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