Home / Event / ALASI2019: Multimodal Analytics for Classroom Proxemics

ALASI2019: Multimodal Analytics for Classroom Proxemics

When:
November 28, 2019 @ 9:00 am – 9:30 am
2019-11-28T09:00:00+11:00
2019-11-28T09:30:00+11:00
Where:
University of Wollonging
Northfields Ave
New South Wales 2522
Australia

Roberto Martinez-Maldonado1 and Gloria Fernandez Nieto2

Abstract

We use the term Classroom Proxemics to refer to how teachers and students use the classroom space, and the impact of this and the spatial design on learning and teaching. The increasing progress in ubiquitous technology makes it easier and cheaper to track students’ and teacher’s  physical actions unobtrusively, making it possible to consider such data for supporting research, educator interventions, and the provision of feedback regarding the use of the classroom space. This workshop is aimed at provoking reflection on potential ways in which teachers can effectively use positioning traces to gain insight into their classroom practice. The workshop will include hands-on ideation activities to explore potential ways in which positioning and other sources of proxemics data can support professional development and research in learning spaces. Indoor positioning sensors along other multimodal learning analytics technologies will be demonstrated during the workshop to facilitate understanding of the broader opportunities of such technologies for learning analytics.

Keywords

multimodal learning analytics, sensors, visualisation, positioning, teaching

Corresponding author 1 Email: Roberto.MartinezMaldonado@monash.edu Address: Faculty of Information Technologies, Monash University, VIC, Australia.

2 Email:Gloria.m.Fernandez-Nieto@student.uts.edu.au Address:  Connected Intelligence Centre, University of Technology Sydney, 2007, NSW Australia.

1. Focus of the workshop

Previous research has found that teachers’ positioning and mobility strategies in the classroom can strongly influence students’ engagement, motivation, disruptive behaviour and self-efficacy (see review by O’Neill & Stephenson, 2014). Inspired by work on instructional proxemics (Chin et al., 2017; McArthur, 2015), the term Classroom Proxemics is proposed to refer to the research space targeted in this workshop. First, this term points at foundational work by Hall (1966) who defined proxemics as the study of culturally dependent ways in which people use interpersonal distance to mediate their interactions. This work has been widely used in architecture and interior design, including the design of learning spaces (Thompson, 2012). Using proxemics as a theoretical lens is highly relevant, because teachers and students make use of the space, furniture, objects and various kinds of technology to interact among themselves.

Second, inspired by work on orchestration (Dillenbourg et al., 2011), the classroom can be considered as the ecological unit of analysis. The classroom includes social, epistemic and physical aspects, that are quite intertwined (Goodyear et al., 2018) and teachers may have varied degrees of control over these aspects according to their pedagogical approach and the tasks unfolding in them.

Feedback and visual representation of movement and positioning traces captured in physical spaces has been studied in previos work. For instance, (Chin, Mei, and Taib 2017) presents the approach of instructional proxemics to generate personal and pedagogical undestanding from how teachers of a second language use their spaces, body movement and positioning and its impact on learning and teaching by using human observations, video and audio.  Other researchers, by using indoor localization (Bdiwi et al. 2019),  and real case studies(Martinez-Maldonado 2019) explore visual representation of positioning data to explore its potencial in spacial pedagogy.

The focus of this workshop is at the intersection between work that has used classroom observations to generate understanding of classroom dynamics (McArthur, 2015) and emerging work focused on creating interfaces to enhance teachers’ awareness, using automatic position tracking (An et al., 2018; Martinez-Maldonado, 2019). Much work needs to be done to identify the kind of reflections that teacher’s positioning data can provoke, and the metrics that may be useful for sensemaking.

2. Participants

The intended audience includes participants interested in developing adaptive and flexible ways to investigate how learners and teachers use the learning spaces. We expect to conduct a 3-hours workshop with at least 10 participants representing different research communities, including the learning sciences (LS)/ education, technology-enhanced learning (TEL) and, also, more data intensive communities such as learning analytics and artificial intelligence in education (AIED). The participation of practitioners and educators will be also encouraged.

Participants will gain first-hand experience in using wearable sensors to track their positioning and in interacting with the data generated from these sensors.

3. Workshop activities

The workshop will follow a JIGZAW collaboration pattern according to the following schedule:

  • Introduction, the workshop will start with short introductions by all the participants and a brief summary of the focus and scope of the workshop (20 minutes);
  • Community Groups, will be formed with the aim of scoping the problem and identifying gaps which will be shared with all the workshop participants (60 minutes);
  • Design Groups, members from the community groups will be re-grouped into design groups to define future scenarios (60 minutes);
  • Consolidation and Reflection, a lead debrief will be facilitated with the goal of consolidating a group of researchers and practitioners interested in the topic (20 minutes).

Breaks and transitions are already considered in this plan

4. Organisers

Roberto Martinez-Maldonado is Senior Lecturer at Monash University, in Melbourne. His areas of research include Human-Computer Interaction, Learning Analytics, Artificial Intelligence in Education, and Collaborative Learning (CSCL). In the past years, his research has focused on applying artificial intelligence and visualisation techniques to help understand how people learn and collaborate in collocated environments. He currently is co-director of the CrossMMLA SIG, the special interest group on Multimodal Learning Analytics Across Spaces.

Gloria Fernandez Nieto is a second year PhD student at University of Technology Sydney in the Connected Intelligence Centre. She is currently supervised by Professor Simon Buckingham Shum, PhD Kirsty Kitto and PhD Roberto Martinez. Her current research focuses on exploring alternatives of feedback to understand traces from data collected in the CSCL classroom to prompt reflection in teaching and lerning practices. She also has focused her previos research on Learning Analytics, Technology Enhance Learning and Knowledge Management.

References

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 International Conference on Tangible, Embedded, and Embodied Interaction, TEI’18, (pp. 357-367). Stockholm, Sweden. 3173243: ACM.

Bdiwi, Rawia, Cyril de Runz, Sami Faiz, and Arab Ali Cherif. 2019. “Smart Learning Environment: Teacher’s Role in Assessing Classroom Attention.” Research in Learning Technology 27(0). https://journal.alt.ac.uk/index.php/rlt/article/view/2072 (September 6, 2019).

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), 1-20.

Dillenbourg, P., Zufferey, G., Alavi, H., Jermann, P., Do-Lenh, S., Bonnard, Q., Cuendet, S., & Kaplan, F. (2011). Classroom orchestration: The third circle of usability. In Proceedings of the International Conference on Computer Supported Collaborative Learning, CSCL’11, (pp. 510-517). Hong Kong. New York: Springer

Goodyear, P., Ellis, R. A., & Marmot, A. (2018). Learning spaces research: Framing actionable knowledge. In R. A. Ellis & P. Goodyear (Eds.), Spaces of Teaching and Learning, (pp. 221-238). Singapore: Springer.

Hall, E. T. (1966). The hidden dimension (Vol. 609). Garden City, NY, United States: Doubleday.

Martinez-Maldonado, R. (2019). I Spent More Time with that Team: Making Spatial Pedagogy Visible Using Positioning Sensors. In Proceedings of the International Conference on Learning Analytics & Knowledge, LAK’19, (pp. 21-25). ACM.

McArthur, J. A. (2015). Matching Instructors and Spaces of Learning: The Impact of Space on Behavioral, Affective and Cognitive Learning. Journal of Learning Spaces, 4(1), 1-16.

Thompson, S. (2012). The applications of proxemics and territoriality in designing efficient layouts for interior design studios and a prototype design studio. Masters dissertation. California State University, Northridge, United States

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