Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:
A Systematic Literature Review
DOI:
https://doi.org/10.18608/jla.2023.7991Keywords:
collaborative learning, socio-spatial learning analytics, ubiquitous computing, systematic literature review, social interactionAbstract
Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to un-
cover valuable educational insights from individuals’ social and spatial data traces. These traces are captured
automatically through sensing technologies in physical learning spaces, and the research is commonly based on
the theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, a
systematic literature review is timely in order to provide educational researchers and practitioners with a detailed
summary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of
25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyses
were conducted to identify the citation networks, essential components, opportunities, and challenges enabled by
SSLA. The findings illustrated that SSLA provides the opportunity to (1) contribute unobtrusive and unsupervised
research methodologies, (2) support educators’ classroom orchestration through visualizations, (3) support learner
reflection with continuous and reliable evidence, (4) develop novel theories about social and collaborative learning,
and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. These
findings could support learning analytics and educational technology scholars and practitioners to better understand
and utilize SSLA to support future educational research and practice.
References
Adrot, A., & Bia Figueiredo, M. (2019). “Lost in digitization”: A spatial journey in emergency response and pragmatic legitimacy. In F. -X. de Vaujany, A. Adrot, E. Boxenbaum, & B. Leca (Eds.), Materiality in institutions: Spaces,
embodiment and technology in management and organization (pp. 151–181). Springer. https://doi.org/10.1007/978-3-
-97472-9 6
Ahuja, K., Kim, D., Xhakaj, F., Varga, V., Xie, A., Zhang, S., Townsend, J. E., Harrison, C., Ogan, A., & Agarwal, Y. (2019).
EduSense: Practical classroom sensing at scale. Proceedings of the ACM on Interactive, Mobile, Wearable and
Ubiquitous Technologies, 3(3), 1–26. https://doi.org/10.1145/3351229
Alterator, S., & Deed, C. (2013). Teacher adaptation to open learning spaces. Issues in Educational Research, 23(3), 315–330.
https://www.iier.org.au/iier23/alterator.pdf
Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2022). The evidence of impact and ethical considerations of multimodal learning analytics: A systematic literature review. In M. Giannakos, D. Spikol, D. Di Mitri, K. Sharma,
X. Ochoa, & R. Hammad (Eds.), The multimodal learning analytics handbook (pp. 289–325). Springer International.
https://doi.org/10.1007/978-3-031-08076-0 12
An, P., Bakker, S., Ordanovski, S., Taconis, R., & Eggen, B. (2018). ClassBeacons: Designing distributed visualization of teachers’ physical proximity in the classroom. In Proceedings of the 12th International Conference on Tangible, Embedded, and Embodied Interaction (TEI 2018), 18–21 March 2018, Stockholm, Sweden (pp. 357–367). ACM.
https://doi.org/10.1145/3173225.3173243
Back, M. D., Schmukle, S. C., & Egloff, B. (2008). Becoming friends by chance. Psychological Science, 19(5), 439. https:
//doi.org/10.1111/j.1467-9280.2008.02106.x
Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3),
–260. https://doi.org/10.1016/j.edurev.2010.06.001
Bandura, A., & Walters, R. H. (1977). Social learning theory (Vol. 1). Prentice Hall.
Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking. In Proceedings of the 2016
IEEE International Conference on Image Processing (ICIP 2016), 25–28 September 2016, Phoenix, AZ (pp. 3464–
. IEEE. https://doi.org/10.1109/ICIP.2016.7533003
Blikstein, P. (2013). Multimodal learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK 2013), 8–13 April 2013, Leuven, Belgium (pp. 102–106). ACM. https://doi.org/10.1145/
2460316
Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018). Open learner models and learning analytics dashboards: A systematic review. In Proceedings of the Eighth International Conference on Learning Analytics and
Knowledge (LAK 2018), 7–9 March 2018, Sydney, Australia (pp. 41–50). ACM. https://doi.org/10.1145/3170358.
Bosch, N., Mills, C., Wammes, J. D., & Smilek, D. (2018). Quantifying classroom instructor dynamics with computer vision. In
C. Penstein Ros ́e, R. Mart ́ınez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren,
& B. du Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED
, 27–30 June 2018, London, UK (pp. 30–42). Springer. https://doi.org/10.1007/978-3-319-93843-1 3
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health,
(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Journal of Educational Technology & Society, 15(3),
–26.
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1
Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022). A transparency index framework for AI in education. In M. M.
Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Proceedings of the 23rd International Conference on
Artificial Intelligence in Education, Part II (AIED 2022), 27–31 July 2022, Durham, UK (pp. 195–198). Springer.
https://doi.org/10.1007/978-3-031-11647-6 33
Chen, B., Chang, Y.-H., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37, 21–30. https://doi.org/10.1016/j.iheduc.2017.12.002
Chen, B., & Teasley, S. D. (2022). Learning analytics for understanding and supporting collaboration. In C. Lang, G. Siemens,
A. F. Wise, D. Ga ˇsevi ́c, & A. Merceron (Eds.), Handbook of learning analytics (pp. 86–95). Society for Learning
Analytics Research. https://solaresearch.org/wp-content/uploads/hla22/HLA22 Chapter 9 Chen.pdf
Chin, H. B., Mei, C. C. Y., & Taib, F. (2017). Instructional proxemics and its impact on classroom teaching and learning.
International Journal of Modern Languages and Applied Linguistics, 1(1), 69–85. https://doi.org/10.24191/ijmal.v1i1.
Chng, E., Seyam, M. R., Yao, W., & Schneider, B. (2020). Using motion sensors to understand collaborative interactions in digital fabrication labs. In I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Mill ́an (Eds.), Proceedings of the
st International Conference on Artificial Intelligence in Education (AIED 2020), 6–10 July 2020, Ifrane, Morocco
(pp. 118–128). Springer. https://doi.org/10.1007/978-3-030-52237-7 10
Chua, Y. H. V., Dauwels, J., & Tan, S. C. (2019). Technologies for automated analysis of co-located, real-life, physical learning spaces: Where are we now? In Proceedings of the Ninth International Conference on Learning Analytics and
Knowledge (LAK 2019), 4–8 March 2019, Tempe, AZ (pp. 11–20). ACM. https://doi.org/10.1145/3303772.3303811
Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. In Proceedings of the Second International
Conference on Learning Analytics and Knowledge (LAK 2012), 29 April–2 May 2012, Vancouver, BC (pp. 134–138).
ACM. https://doi.org/10.1145/2330601.2330636
Coleman, T. E., & Money, A. G. (2020). Student-centred digital game–based learning: A conceptual framework and survey of the state of the art. Higher Education, 79(3), 415–457. https://doi.org/10.1007/s10734-019-00417-0
Crescenzi-Lanna, L. (2020). Multimodal learning analytics research with young children: A systematic review. British Journal
of Educational Technology, 51(5), 1485–1504. https://doi.org/10.1111/bjet.12959
Cukurova, M., Giannakos, M., & Martinez-Maldonado, R. (2020). The promise and challenges of multimodal learning analytics.
British Journal of Educational Technology, 51(5), 1441–1449. https://doi.org/10.1111/bjet.13015
Das, K., Samanta, S., & Pal, M. (2018). Study on centrality measures in social networks: A survey. Social Network Analysis
and Mining, 8(1), 1–11. https://doi.org/10.1007/s13278-018-0493-2
Dawson, S., Gasevic, D., Siemens, G., & Joksimovic, S. (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
(LAK 2014), 24–28 March 2018, Indianapolis, IN (pp. 231–240). ACM. https://doi.org/10.1145/2567574.2567585
Dawson, S., Joksimovic, S., Poquet, O., & Siemens, G. (2019). Increasing the impact of learning analytics. In Proceedings of
the Ninth International Conference on Learning Analytics and Knowledge (LAK 2019), 4–8 March 2019, Tempe, AZ
(pp. 446–455). ACM. https://doi.org/10.1145/3303772.3303784
Defence Science and Technology Group. (n.d.). Technology readiness levels definitions and descriptions [Accessed: 2023-01-20]. https://www.dst.defence.gov.au/sites/default/files/basic pages/documents/TRL%20Explanations 1.pdf
Dewiyanti, S., Brand-Gruwel, S., Jochems, W., & Broers, N. J. (2007). Students’ experiences with collaborative learning in asynchronous computer-supported collaborative learning environments. Computers in Human Behavior, 23(1),
–514. https://doi.org/10.1016/j.chb.2004.10.021
Echeverria, V., Martinez-Maldonado, R., & Buckingham Shum, S. (2019). Towards collaboration translucence: Giving meaning to multimodal group data. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), 4–9 May 2019, Glasgow, UK (pp. 1–16). ACM. https://doi.org/10.1145/3290605.3300269
Echeverria, V., Martinez-Maldonado, R., Power, T., Hayes, C., & Buckingham Shum, S. (2018). Where is the nurse? Towards automatically visualising meaningful team movement in healthcare education. In C. Penstein Ros ́e, R. Mart ́ınez-
Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.),
Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018), 27–30 June
, London, UK (pp. 74–78). Springer. https://doi.org/10.1007/978-3-319-93846-2 14
Estimote. (2023). Estimote UWB Beacons [Accessed: 2023-03-10]. https://www.estimote.com
Fernandes, A. C., Huang, J., & Rinaldo, V. (2011). Does where a student sits really matter? The impact of seating locations on student classroom learning. International Journal of Applied Educational Studies, 10(1).
Fernandez-Nieto, G., An, P., Zhao, J., Buckingham Shum, S., & Martinez-Maldonado, R. (2022). Classroom dandelions:
Visualising participant position, trajectory and body orientation augments teachers’ sensemaking. In S. Barbosa, C.
Lampe, C. Appert, D. A. Shamma, S. Drucker, J. Williamson, & K. Yatani (Eds.), Proceedings of the CHI Conference
on Human Factors in Computing Systems (CHI 2022), 29 April–5 May 2022, New Orleans, LA (pp. 1–17). ACM.
https://doi.org/10.1145/3491102.3517736
Fernandez-Nieto, G., Martinez-Maldonado, R., Echeverria, V., Kitto, K., An, P., & Buckingham Shum, S. (2021). What can analytics for teamwork proxemics reveal about positioning dynamics in clinical simulations? (J. Nichols, Ed.).
Proceedings of the ACM on Human Computer Interaction, 5(CSCW1), 1–24. https://doi.org/10.1145/3449284
Fernandez-Nieto, G., Martinez-Maldonado, R., Kitto, K., & Buckingham Shum, S. (2021). Modelling spatial behaviours in clinical team simulations using epistemic network analysis: Methodology and teacher evaluation. In Proceedings of
the 11th International Conference on Learning Analytics and Knowledge (LAK 2021), 12–16 April 2021, Irvine, CA
(pp. 386–396). ACM. https://doi.org/10.1145/3448139.3448176
Ga ˇsevi ́c, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.
https://doi.org/10.1016/j.iheduc.2015.10.002
Gasevic, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1),
–71. https://doi.org/10.1007/s11528-014-0822-x
Ga ˇsevi ́c, D., Zouaq, A., & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross-class social ties and academic performance. American Behavioral Scientist, 57(10), 1460–1479. https://doi.org/10.1177/
Hall, E. T., Birdwhistell, R. L., Bock, B., Bohannan, P., Diebold Jr, A. R., Durbin, M., Edmonson, M. S., Fischer, J., Hymes,
D., Kimball, S. T., La Barre, W., Lynch, F., S. J., McClellan, J. E., Marshall, D. S., Milner, G. B., Sarles, H. B.,
Trager, G. L., & Vayda, A. P. (1968). Proxemics [and comments and replies]. Current Anthropology, 9(2/3), 83–108.
https://www.jstor.org/stable/2740724
Hall, E. T. (1966). The hidden dimension (Vol. 609). Doubleday.
Ioannou, M., Georgiou, Y., Ioannou, A., & Johnson, M. (2019). On the understanding of students’ learning and perceptions of technology integration in low-and high-embodied group learning. In Proceedings of the 13th International Conference
on Computer Supported Collaborative Learning (CSCL 2019), 17–21 June 2019, Lyon, France (pp. 304–311). ISLS.
https://repository.isls.org/bitstream/1/4418/1/304-311.pdf
Kaliisa, R., Rienties, B., Mørch, A. I., & Kluge, A. (2022). Social learning analytics in computer-supported collaborative learning environments: A systematic review of empirical studies. Computers and Education Open, 100073. https:
//doi.org/10.1016/j.caeo.2022.100073
Kim, B. (2010). Social constructivism. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology (pp. 55–61). https://textbookequity.org/Textbooks/Orey Emergin Perspectives Learning.pdf
Knight, S., & Buckingham Shum, S. (2017). Theory and learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Ga ˇsevi ́c
(Eds.), Handbook of learning analytics (1st edition, pp. 17–22). Society for Learning Analytics Research. https:
//www.solaresearch.org/publications/hla-17/hla17-chapter1/
Lang, C., Wise, A. F., Merceron, A., Ga ˇsevi ́c, D., & Siemens, G. (2022). What is learning analytics? In C. Lang, G. Siemens,
A. F. Wise, D. Ga ˇsevi ́c, & A. Merceron (Eds.), Handbook of Learning Analytics (2nd edition, pp. 8–18). Society for
Learning Analytics Research. https://www.solaresearch.org/publications/hla-22/hla22-chapter1/
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
Lefebvre, H., & Nicholson-Smith, D. (1991). The production of space (Vol. 142). Oxford Blackwell.
Li, X., Yan, L., Zhao, L., Martinez-Maldonado, R., & Ga ˇsevi ́c, D. (2023). CVPE: A computer vision approach for scalable and privacy-preserving socio-spatial, multimodal learning analytics. In Proceedings of the 13th International Conference On Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, TX (pp. 175–185). ACM.
https://doi.org/10.1145/3576050.3576145
Lim, F. V., O’Halloran, K. L., & Podlasov, A. (2012). Spatial pedagogy: Mapping meanings in the use of classroom space.
Cambridge Journal of Education, 42(2), 235–251. https://doi.org/10.1080/0305764X.2012.676629
Luciano, M. M., Mathieu, J. E., Park, S., & Tannenbaum, S. I. (2018). A fitting approach to construct and measurement alignment: The role of big data in advancing dynamic theories. Organizational Research Methods, 21(3), 592–632.
https://doi.org/10.1177/1094428117728372
Ludlow, K., Churruca, K., Ellis, L. A., Mumford, V., & Braithwaite, J. (2021). Decisions and dilemmas: The context of prioritization dilemmas and influences on staff members’ prioritization decisions in residential aged care. Qualitative
Health Research, 31(7), 1306–1318. https://doi.org/10.1177/1049732321998294
Mallavarapu, A., Lyons, L., & Uzzo, S. (2022). Exploring the utility of social-network-derived collaborative opportunity temperature readings for informing design and research of large-group immersive learning environments. Journal of
Learning Analytics, 9(1), 53–76. https://doi.org/10.18608/jla.2022.7419
Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., & Buckingham Shum, S. (2020). From data to insights:
A layered storytelling approach for multimodal learning analytics. In Proceedings of the 2020 CHI Conference
on Human Factors in Computing Systems (CHI 2020), 25–30 April 2020, Honolulu, HI (pp. 1–15). ACM. https:
//doi.org/10.1145/3313831.3376148
Martinez-Maldonado, R., Echeverria, V., Santos, O. C., Santos, A. D. P. D., & Yacef, K. (2018). Physical learning analytics: A
multimodal perspective. In Proceedings of the Eighth International Conference on Learning Analytics and Knowledge
(LAK 2018), 7–9 March 2018, Sydney, Australia (pp. 375–379). ACM. https://doi.org/10.1145/3170358.3170379
Martinez-Maldonado, R., Echeverria, V., Schulte, J., Shibani, A., Mangaroska, K., & Buckingham Shum, S. (2020). Moodoo:
Indoor positioning analytics for characterising classroom teaching. In I. I. Bittencourt, M. Cukurova, K. Muldner,
R. Luckin, & E. Mill ́an (Eds.), Proceedings of the 21st International Conference on Artificial Intelligence in Education
(pp. 360–373). Springer. https://doi.org/10.1007/978-3-030-52237-7 29
Martinez-Maldonado, R., Mangaroska, K., Schulte, J., Elliott, D., Axisa, C., & Buckingham Shum, S. (2020). Teacher tracking with integrity: What indoor positioning can reveal about instructional proxemics. Proceedings of the ACM on
Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1–27. https://doi.org/10.1145/3381017
Martinez-Maldonado, R., Schulte, J., Echeverria, V., Gopalan, Y., & Buckingham Shum, S. (2020). Where is the teacher?
Digital analytics for classroom proxemics. Journal of Computer Assisted Learning, 36(5), 741–762. https://doi.org/10.
/jcal.12444
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/
BM.2012.031
Montague, M., & Rinaldi, C. (2001). Classroom dynamics and children at risk: A followup. Learning Disability Quarterly,
(2), 75–83. https://doi.org/10.2307/1511063
Nguyen, Q., Poquet, O., Brooks, C., & Li, W. (2020). Exploring homophily in demographics and academic performance using spatial-temporal student networks. In Proceedings of the 13th International Conference on Educational Data Mining
(EDM 2020), 10–13 July 2020, online (pp. 194–201). https://educationaldatamining.org/files/conferences/EDM2020/
papers/paper 170.pdf
Ochoa, X. (2022). Multimodal learning analytics—Rationale, process, examples, and direction. In C. Lang, G. Siemens,
A. F. Wise, D. Gaˇsevic, & A. Merceron (Eds.), Handbook of learning analytics (2nd edition, pp. 54–65). Society for
Learning Analytics Research. https://www.solaresearch.org/publications/hla-22/hla22-chapter6/
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M.,
Akl, E. A., Brennan, S. E., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88. https://doi.org/10.1016/j.ijsu.2021.105906
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341. https://doi.org/10.1007/s10648-006-9029-9
Poquet, O., & Joksimovi ́c, S. (2022). Cacophony of networks in learning analytics. In C. Lang, G. Siemens, A. F. Wise,
D. Ga ˇsevi ́c, & A. Merceron (Eds.), Handbook of Learning Analytics (2nd edition, pp. 38–45). Society for Learning
Analytics Research. https://solaresearch.org/wp-content/uploads/hla22/HLA22 Chapter 4 Poquet.pdf
Poquet, O., Lim, L., Mirriahi, N., & Dawson, S. (2018). Video and learning: A systematic review (2007–2017). In Proceedings
of the Eighth International Conference on Learning Analytics and Knowledge (LAK 2018), 7–9 March 2018, Sydney,
Australia (pp. 151–160). ACM. https://doi.org/10.1145/3170358.3170376
Prieto, L. P., Magnuson, P., Dillenbourg, P., & Saar, M. (2020). Reflection for action: Designing tools to support teacher reflection on everyday evidence. Technology, Pedagogy and Education, 29(3), 279–295. https://doi.org/10.1080/
X.2020.1762721
Quuppa. (2023). Quuppa intelligent locating system [Accessed: 2023-03-10]. https://quuppa.com/technology/products/
Riquelme, F., Noel, R., Cornide-Reyes, H., Geldes, G., Cechinel, C., Miranda, D., Villarroel, R., & Munoz, R. (2020).
Where are you? Exploring micro-location in indoor learning environments. IEEE Access, 8, 125776–125785. https:
//doi.org/10.1109/ACCESS.2020.3008327
Saquib, N., Bose, A., George, D., & Kamvar, S. (2018). Sensei: Sensing educational interaction. Proceedings of the ACM on
Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4), 1–27. https://doi.org/10.1145/3161172
Shapiro, L., & Stolz, S. A. (2019). Embodied cognition and its significance for education. Theory and Research in Education,
(1), 19–39. https://doi.org/10.1177/1477878518822149
Sharma, K., & Giannakos, M. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology, 51(5), 1450–1484. https://doi.org/10.1111/bjet.12993
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Sorokowska, A., Sorokowski, P., Hilpert, P., Cantarero, K., Frackowiak, T., Ahmadi, K., Alghraibeh, A. M., Aryeetey, R.,
Bertoni, A., Bettache, K., Blumen, S., Bła ̇zejewska, M., Bortolini, T., Butovskaya, M., Castro, F. N., Cetinkaya, H.,
Cunha, D., David, D., David, O. A., . . . Pierce, Jr., J. D. (2017). Preferred interpersonal distances: A global comparison.
Journal of Cross-Cultural Psychology, 48(4), 577–592. https://doi.org/10.1177/0022022117698039
Stehl ́e, J., Charbonnier, F., Picard, T., Cattuto, C., & Barrat, A. (2013). Gender homophily from spatial behavior in a primary school: A sociometric study. Social Networks, 35(4), 604–613. https://doi.org/10.1016/j.socnet.2013.08.003
van Leeuwen, A., Rummel, N., & Van Gog, T. (2019). What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations? International Journal of Computer-Supported Collaborative Learning, 14(3),
–289. https://doi.org/10.1007/s11412-019-09299-x
Whyte, B. (2017). Collaborative teaching in flexible learning spaces: Capabilities of beginning teachers. Journal of Educational
Leadership, Policy and Practice, 32(1), 84–96. https://doi.org/10.21307/jelpp-2017-008
Wyman, P. J., & Watson, S. B. (2020). Academic achievement with cooperative learning using homogeneous and heterogeneous groups. School Science and Mathematics, 120(6), 356–363. https://doi.org/10.1111/ssm.12427
Yan, L., Martinez-Maldonado, R., Cordoba, B. G., Deppeler, J., Corrigan, D., Nieto, G. F., & Gasevic, D. (2021). Footprints at school: Modelling in-class social dynamics from students’ physical positioning traces. In Proceedings of the
th International Conference on Learning Analytics and Knowledge (LAK 2021), 12–16 April 2021, Irvine, CA
(pp. 43–54). ACM. https://doi.org/10.1145/3448139.3448144
Yan, L., Martinez-Maldonado, R., Gallo Cordoba, B., Deppeler, J., Corrigan, D., & Ga ˇsevi ́c, D. (2022). Mapping from proximity traces to socio-spatial behaviours and student progression at the school. British Journal of Educational Technology,
(6), 1645–1664. https://doi.org/10.1111/bjet.13203
Yan, L., Martinez-Maldonado, R., Zhao, L., Deppeler, J., Corrigan, D., & Gasevic, D. (2022). How do teachers use open learning spaces? Mapping from teachers’ socio-spatial data to spatial pedagogy. In Proceedings of the 12th International
Conference on Learning Analytics and Knowledge (LAK 2022), 21–25 March 2022, online (pp. 87–97). ACM.
https://doi.org/10.1145/3506860.3506872
Yan, L., Martinez-Maldonado, R., Zhao, L., Dix, S., Jaggard, H., Wotherspoon, R., Li, X., & Ga ˇsevi ́c, D. (2023). The role of indoor positioning analytics in assessment of simulation-based learning. British Educational Research Journal, 54(1),
–292. https://doi.org/10.1111/bjet.13262
Yan, L., Martinez-Maldonado, R., Zhao, L., Li, X., & Gasevic, D. (2023). SeNA: Modelling socio-spatial analytics on homophily by integrating social and epistemic network analysis. In Proceedings of the 13th International Conference
on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, TX (pp. 22–32). ACM. https:
//doi.org/10.1145/3576050.3576054
Yan, L., Zhao, L., Gasevic, D., & Martinez-Maldonado, R. (2022). Scalability, sustainability, and ethicality of multimodal learning analytics. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK
, 21–25 March 2022, online (pp. 13–23). ACM. https://doi.org/10.1145/3506860.3506862
Zhao, L., Swiecki, Z., Gasevic, D., Yan, L., Dix, S., Jaggard, H., Wotherspoon, R., Osborne, A., Li, X., Alfredo, R., & Martinez-
Maldonado, R. (2023). METS: Multimodal learning analytics of embodied teamwork learning. In Proceedings of the
th International Conference on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, TX
(pp. 186–196). ACM. https://doi.org/10.1145/3576050.3576076
Zhao, L., Yan, L., Gasevic, D., Dix, S., Jaggard, H., Wotherspoon, R., Alfredo, R., Li, X., & Martinez-Maldonado, R. (2022).
Modelling co-located team communication from voice detection and positioning data in healthcare simulation. In
Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK 2022), 21–25 March
, online (pp. 370–380). ACM. https://doi.org/10.1145/3506860.3506935
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
