research-article

SeNA: Modelling Socio-spatial Analytics on Homophily by Integrating Social and Epistemic Network Analysis

Authors:

Roberto Martinez-Maldonado,

Dragan GasevicAuthors Info & Claims

LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference

Pages 22 - 32

https://doi.org/10.1145/3576050.3576054

Published: 13 March 2023 Publication History

Abstract

Homophily is a fundamental sociological theory that describes the tendency of individuals to interact with others who share similar attributes. This theory has shown evident relevance for studying collaborative learning and classroom orchestration in learning analytics research from a social constructivist perspective. Emerging advancements in multimodal learning analytics have shown promising results in capturing interaction data and generating socio-spatial analytics in physical learning spaces through computer vision and wearable positioning technologies. Yet, there are limited ways for analysing homophily (e.g., social network analysis; SNA), especially for unpacking the temporal connections between different homophilic behaviours. This paper presents a novel analytic approach, Social-epistemic Network Analysis (SeNA), for analysing homophily by combining social network analysis with epistemic network analysis to infuse socio-spatial analytics with temporal insights. The additional insights SeNA may offer over traditional approaches (e.g., SNA) were illustrated through analysing the homophily of 98 students in open learning spaces. The findings showed that SeNA could reveal significant behavioural differences in homophily between comparison groups across different learning designs, which were not accessible to SNA alone. The implications and limitations of SeNA in supporting future learning analytics research regarding homophily in physical learning spaces are also discussed.

References

[1]

Stephanie Bell. 2010. Project-based learning for the 21st century: Skills for the future. The clearing house 83, 2 (2010), 39–43.

[2]

Dale Bowman, Zachari Swiecki, Zhiqiang Cai, Yeyu Wang, Brendan Eagan, Jeff Linderoth, and David Williamson Shaffer. 2021. The mathematical foundations of epistemic network analysis. In International Conference on Quantitative Ethnography. Springer, 91–105.

[3]

Simon Buckingham Shum, Rebecca Ferguson, and Roberto Martinez-Maldonado. 2019. Human-centred learning analytics. JLA 6, 2 (2019), 1–9.

[4]

Ling Cen, Dymitr Ruta, Leigh Powell, Benjamin Hirsch, and Jason Ng. 2016. Quantitative approach to collaborative learning: performance prediction, individual assessment, and group composition. ijCSCL 11, 2 (Jun 2016), 187–225.

[5]

Bodong Chen and Oleksandra Poquet. 2020. Socio-temporal dynamics in peer interaction events. In LAK20. 203–208.

[6]

Edwin Chng, Mohamed Raouf Seyam, William Yao, and Bertrand Schneider. 2020. Using Motion Sensors to Understand Collaborative Interactions in Digital Fabrication Labs. In AIED. Springer, 118–128.

[7]

Yi Han Victoria Chua, Justin Dauwels, and Seng Chee Tan. 2019. Technologies for automated analysis of co-located, real-life, physical learning spaces: Where are we now?. In LAK’19. 11–20.

[8]

National Research Council 2011. Assessing 21st century skills: Summary of a workshop. (2011).

[9]

Shane Dawson, Dragan Gašević, George Siemens, and Srecko Joksimovic. 2014. Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the fourth international conference on learning analytics and knowledge. 231–240.

Digital Library

[10]

Silvia Dewiyanti, Saskia Brand-Gruwel, Wim Jochems, and Nick J Broers. 2007. Students’ experiences with collaborative learning in asynchronous computer-supported collaborative learning environments. Computers in Human Behavior 23, 1 (2007), 496–514.

[11]

Vanessa Echeverria, Roberto Martinez-Maldonado, Tamara Power, Carolyn Hayes, and Simon Buckingham Shum. 2018. Where is the nurse? Towards automatically visualising meaningful team movement in healthcare education. In AIED. Springer, 74–78.

[12]

Yizhou Fan, Yuanru Tan, Mladen Raković, Yeyu Wang, Zhiqiang Cai, David Williamson Shaffer, and Dragan Gašević. 2022. Dissecting Learning Tactics in MOOC using Ordered Network Analysis. Journal of Computer Assisted Learning(2022), in press.

[13]

Gloria Fernandez-Nieto, Roberto Martinez-Maldonado, Vanessa Echeverria, Kirsty Kitto, Pengcheng An, and Simon Buckingham Shum. 2021. What Can Analytics for Teamwork Proxemics Reveal About Positioning Dynamics In Clinical Simulations?PACM HCI 5, CSCW1 (2021), 1–24.

[14]

Gloria Milena Fernandez-Nieto, Roberto Martinez-Maldonado, Kirsty Kitto, and Simon Buckingham Shum. 2021. Modelling spatial behaviours in clinical team simulations using epistemic network analysis: methodology and teacher evaluation. In LAK21. 386–396.

[15]

LS Fuchs, D Fuchs, CL Hamlett, and K Karns. 1998. High-achieving students’ interactions and performance on complex mathematical tasks as a function of homogeneous and heterogeneous pairings. American Educational Research Journal 35, 2 (SUM 1998), 227–267.

[16]

Dragan Gašević, Srećko Joksimović, Brendan R Eagan, and David Williamson Shaffer. 2019. SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior 92 (2019), 562–577.

Digital Library

[17]

Dragan Gašević, Amal Zouaq, and Robert Janzen. 2013. “Choose your classmates, your GPA is at stake!” The association of cross-class social ties and academic performance. American Behavioral Scientist 57, 10 (2013), 1460–1479.

[18]

S. Greenberg, S. Boring, J. Vermeulen, and J. Dostal. 2014. Dark patterns in proxemic interactions: a critical perspective. In DIS’14. 523–532.

[19]

Daniel Z Grunspan, Benjamin L Wiggins, and Steven M Goodreau. 2014. Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE—Life Sciences Education 13, 2 (2014), 167–178.

[20]

Edward Twitchell Hall. 1966. The hidden dimension. Vol. 609. Garden City, NY: Doubleday.

[21]

Eleanor L Higgins, Marshall H Raskind, Roberta J Goldberg, and Kenneth L Herman. 2002. Stages of acceptance of a learning disability: The impact of labeling. Learning Disability Quarterly 25, 1 (2002), 3–18.

[22]

Srećko Joksimović, Areti Manataki, Dragan Gašević, Shane Dawson, Vitomir Kovanović, and Inés Friss De Kereki. 2016. Translating network position into performance: importance of centrality in different network configurations. In LAK’16. 314–323.

[23]

Martin H Jones, Joyce M Alexander, and David B Estell. 2010. Homophily among peer groups members’ perceived self-regulated learning. The Journal of Experimental Education 78, 3 (2010), 378–394.

[24]

Ichiro Kawachi and Lisa F Berkman. 2001. Social ties and mental health. Journal of Urban health 78, 3 (2001), 458–467.

[25]

Simon Knight, Alyssa Friend Wise, and Bodong Chen. 2017. Time for change: Why learning analytics needs temporal analysis. JLA 4, 3 (2017), 7–17.

[26]

Margaret M Luciano, John E Mathieu, Semin Park, and Scott I Tannenbaum. 2018. A fitting approach to construct and measurement alignment: The role of big data in advancing dynamic theories. Organizational Research Methods 21, 3 (2018), 592–632.

[27]

Roberto Martinez-Maldonado, Vanessa Echeverria, Jurgen Schulte, Antonette Shibani, Katerina Mangaroska, and Simon Buckingham Shum. 2020. Moodoo: indoor positioning analytics for characterising classroom teaching. In AIED. Springer, 360–373.

[28]

Roberto Martinez-Maldonado, Dragan Gaševic, Vanessa Echeverria, Gloria Fernandez Nieto, Zachari Swiecki, and Simon Buckingham Shum. 2021. What Do You Mean by Collaboration Analytics? A Conceptual Model.Journal of Learning Analytics 8, 1 (2021), 126–153.

[29]

Roberto Martinez-Maldonado, Katerina Mangaroska, Jurgen Schulte, Doug Elliott, Carmen Axisa, and Simon Buckingham Shum. 2020. Teacher Tracking with Integrity: What Indoor Positioning Can Reveal About Instructional Proxemics. PACM IMWUT 4, 1 (2020), 1–27.

[30]

Roberto Martinez-Maldonado, Jurgen Schulte, Vanessa Echeverria, Yuveena Gopalan, and Simon Buckingham Shum. 2020. Where is the teacher? Digital analytics for classroom proxemics. Journal of Computer Assisted Learning(2020).

[31]

R. Matsuoka. 2018. Inequality in Shadow Education Participation in an Egalitarian Compulsory Education System. CER 62, 4 (NOV 2018), 565–586.

[32]

Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol (2001), 415–444.

[33]

Natarajan Meghanathan. 2016. Assortativity Analysis of Real-World Network Graphs based on Centrality Metrics.Comput. Inf. Sci. 9, 3 (2016), 7–25.

[34]

Mark EJ Newman. 2003. Mixing patterns in networks. Physical review E 67, 2 (2003), 026126.

[35]

Quan Nguyen, Oleksandra Poquet, Christopher Brooks, and Warren Li. 2020. Exploring homophily in demographics and academic performance using spatial-temporal student networks. In EDM’20. 194–201.

[36]

Oleksandra Poquet and Srećko Joksimović. 2022. Cacophony of Networks in Learning Analytics. In The Handbook of Learning Analytics(2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gasevic, and Agathe Merceron (Eds.). SoLAR, 38–45.

[37]

Quuppa. [n. d.]. Quuppa Intelligent Locating System. https://quuppa.com/technology/products/. Accessed: 2020-07-28.

[38]

Sabine Reh, Kerstin Rabenstein, and Bettina Fritzsche. 2011. Learning spaces without boundaries? Territories, power and how schools regulate learning. Social & Cultural Geography 12, 01 (2011), 83–98.

[39]

Oscar Sanchez Revelo, Cesar Alberto Collazos Ordonez, Miguel Angel Redondo Duque, and Ig Ibert Bittencourt Santana Pinto. 2021. Homogeneous Group Formation in Collaborative Learning Scenarios: An Approach Based on Personality Traits and Genetic Algorithms. IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 14, 4 (AUG 2021), 486–499. https://doi.org/10.1109/TLT.2021.3105008

Digital Library

[40]

Fabián Riquelme, Rene Noel, Hector Cornide-Reyes, Gustavo Geldes, Cristian Cechinel, Diego Miranda, Rodolfo Villarroel, and Roberto Munoz. 2020. Where are you? Exploring micro-location in indoor learning environments. IEEE Access 8(2020), 125776–125785.

[41]

N. Saquib, A. Bose, D. George, and S. Kamvar. 2018. Sensei: Sensing Educational Interaction. PACM IMWUT 1, 4 (2018), 1–27.

[42]

David Williamson Shaffer, Wesley Collier, and Andrew R Ruis. 2016. A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics 3, 3 (2016), 9–45.

[43]

Kshitij Sharma and Michail Giannakos. 2020. Multimodal data capabilities for learning: What can multimodal data tell us about learning?Br J Educ Technol 51, 5 (2020), 1450–1484.

[44]

Simon Buckingham Shum and Rebecca Ferguson. 2012. Social learning analytics. Journal of educational technology & society 15, 3 (2012), 3–26.

[45]

Juliette Stehlé, François Charbonnier, Tristan Picard, Ciro Cattuto, and Alain Barrat. 2013. Gender homophily from spatial behavior in a primary school: A sociometric study. Social Networks 35, 4 (2013), 604–613.

[46]

Zachari Swiecki and David Williamson Shaffer. 2020. iSENS: an integrated approach to combining epistemic and social network analyses. In Proceedings of the tenth international conference on learning analytics & knowledge. 305–313.

Digital Library

[47]

Sharon Thompson. 2012. The applications of proxemics and territoriality in designing efficient layouts for interior design studios and a prototype design studio.Ph. D. Dissertation. California State University, Northridge.

[48]

Kathryn R Wentzel and Deborah E Watkins. 2002. Peer relationships and collaborative learning as contexts for academic enablers. School Psychology Review 31, 3 (2002), 366–377.

[49]

Astrid Wichmann, Tobias Hecking, Malte Elson, Nina Christmann, Thomas Herrmann, and H. Ulrich Hoppe. 2016. Group Formation for Small-Group Learning: Are Heterogeneous Groups More Productive?. In PROCEEDINGS OF THE 12TH INTERNATIONAL SYMPOSIUM ON OPEN COLLABORATION (OPENSYM). Wikimedia Fdn; Google Inc; ACM SIGSOFT; ACM SIGWEB. https://doi.org/10.1145/2957792.2965662 12th International Symposium on Open Collaboration (OPENSYM), Berlin, GERMANY, AUG 17-19, 2016.

Digital Library

[50]

Patricia J. Wyman and Scott B. Watson. 2020. Academic achievement with cooperative learning using homogeneous and heterogeneous groups. SCHOOL SCIENCE AND MATHEMATICS 120, 6 (OCT 2020), 356–363. https://doi.org/10.1111/ssm.12427

[51]

Lixiang Yan, Roberto Martinez-Maldonado, Beatriz Gallo Cordoba, Joanne Deppeler, Deborah Corrigan, Gloria Fernandez Nieto, and Dragan Gasevic. 2021. Footprints at School: Modelling In-class Social Dynamics from Students’ Physical Positioning Traces. In LAK’21. 43–54.

[52]

Lixiang Yan, Roberto Martinez-Maldonado, Beatriz Gallo Cordoba, Joanne Deppeler, Deborah Corrigan, and Dragan Gašević. 2022. Mapping from proximity traces to socio-spatial behaviours and student progression at the school. British Journal of Educational Technology(2022).

[53]

Lixiang Yan, Roberto Martinez-Maldonado, Linxuan Zhao, Joanne Deppeler, Deborah Corrigan, and Dragan Gasevic. 2022. How Do Teachers Use Open Learning Spaces? Mapping from Teachers’ Socio-Spatial Data to Spatial Pedagogy. In LAK22: 12th International Learning Analytics and Knowledge Conference (Online, USA) (LAK22). Association for Computing Machinery, New York, NY, USA, 87–97. https://doi.org/10.1145/3506860.3506872

Digital Library

[54]

Lixiang Yan, Roberto Martinez-Maldonado, Linxuan Zhao, Samantha Dix, Hollie Jaggard, Rosie Wotherspoon, Xinyu Li, and Dragan Gašević. 2022. The Role of Indoor Positioning Analytics in Assessment of Simulation-based Learning. British Educational Research Journal(2022), in press.

[55]

Lixiang Yan, Linxuan Zhao, Dragan Gasevic, and Roberto Martinez-Maldonado. 2022. Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics. In LAK22: 12th International Learning Analytics and Knowledge Conference (Online, USA) (LAK22). Association for Computing Machinery, New York, NY, USA, 13–23. https://doi.org/10.1145/3506860.3506862

Digital Library

[56]

Linxuan Zhao, Lixiang Yan, Dragan Gasevic, Samantha Dix, Hollie Jaggard, Rosie Wotherspoon, Riordan Alfredo, Xinyu Li, and Roberto Martinez-Maldonado. 2022. Modelling Co-located Team Communication from Voice Detection and Positioning Data in Healthcare Simulation. In LAK22: 12th International Learning Analytics and Knowledge Conference. 370–380.

Digital Library

Cited By

Feng SYan LZhao LMaldonado RGašević D(2024)Heterogenous Network Analytics of Small Group Teamwork: Using Multimodal Data to Uncover Individual Behavioral Engagement StrategiesProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636918(587-597)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636918
Zhao LGašević DSwiecki ZLi YLin JSha LYan LAlfredo RLi XMartinez‐Maldonado R(2024)Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analyticsBritish Journal of Educational Technology10.1111/bjet.13476Online publication date: 30-May-2024
https://doi.org/10.1111/bjet.13476

Index Terms

SeNA: Modelling Socio-spatial Analytics on Homophily by Integrating Social and Epistemic Network Analysis
1. Applied computing
  1. Education

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cover image ACM Other conferences

LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference

March 2023

692 pages

ISBN:9781450398657

DOI:10.1145/3576050

Program Chairs:
Isabel Hilliger
Pontificia Universidad Católica de Chile, Chile
,
Hassan Khosravi
University of Queensland, Australia
,
Bart Rienties
Open University, United Kingdom
,
Shane Dawson
University of South Australia, Australia

Copyright © 2023 ACM.

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Published: 13 March 2023

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LAK 2023: 13th International Learning Analytics and Knowledge Conference

March 13 - 17, 2023

TX, Arlington, USA

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Cited By

Feng SYan LZhao LMaldonado RGašević D(2024)Heterogenous Network Analytics of Small Group Teamwork: Using Multimodal Data to Uncover Individual Behavioral Engagement StrategiesProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636918(587-597)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636918
Zhao LGašević DSwiecki ZLi YLin JSha LYan LAlfredo RLi XMartinez‐Maldonado R(2024)Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analyticsBritish Journal of Educational Technology10.1111/bjet.13476Online publication date: 30-May-2024
https://doi.org/10.1111/bjet.13476

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