SoLAR Webinar: Socio-spatial Learning Analytics for Embodied Collaborative Learning

SF
Society for Learning Analytics Research
Mon, Jan 16, 2023 3:06 PM

[apologies for cross posting]

Hi all,

It is our pleasure to invite you to SoLAR Webinar "Socio-spatial Learning Analytics for Embodied Collaborative Learning", presented by Lixiang Yan from Monash University, the best paper award winner of LAK22.

Time and date: February 9, 2023, 12:00 PM – 1:00 PM CET

Location: Zoom (meeting URL provided in the registration email)

To register, go to https://www.eventbrite.ca/e/socio-spatial-learning-analytics-for-embodied-collaborative-learning-tickets-516556493377

(Also, make sure you follow SoLAR's Eventbrite page to get updates for the future events).

We are looking forward to seeing you at the webinar!

Kind regards,

Society for Learning Analytics Research (SoLAR)

https://solaresearch.org/

Socio-spatial Learning Analytics for Embodied Collaborative Learning

Embodied collaborative learning (ECL) provides unique opportunities for students to practice key procedural and collaboration skills in co-located, physical learning spaces where they need to interact with others and utilise physical and digital resources to achieve a shared goal. Unpacking the socio-spatial aspects of ECL is essential for developing tools that can support students' collaboration and teachers' orchestration in increasingly complex, hybrid learning spaces. Advancements in multimodal learning analytics and wearable technologies are motivating emerging analytic approaches to tackle this challenge.

This presentation will introduce a conceptual and methodological framework of social-spatial learning analytics that map from social-spatial traces captured through wearable sensors to meaningful educational insights. The framework consists of five primary phases: foundations, feature engineering, analytic approaches, learning analytics, and educational insights. Two illustrative cases will be presented to demonstrate how the framework can support educational research and the formative assessment of students' learning. Finally, the opportunities and challenges regarding socio-spatial learning analytics are discussed.

Bio

Lixiang Yan is a final-year PhD candidate at the Centre for Learning Analytics at Monash University. He researches multimodal learning analytics, focusing on classroom orchestration and collaborative learning in physical learning spaces.

[apologies for cross posting] Hi all, It is our pleasure to invite you to SoLAR Webinar "Socio-spatial Learning Analytics for Embodied Collaborative Learning", presented by Lixiang Yan from Monash University, the best paper award winner of LAK22. Time and date: February 9, 2023, 12:00 PM – 1:00 PM CET Location: Zoom (meeting URL provided in the registration email) To register, go to https://www.eventbrite.ca/e/socio-spatial-learning-analytics-for-embodied-collaborative-learning-tickets-516556493377 (Also, make sure you follow SoLAR's Eventbrite page to get updates for the future events). We are looking forward to seeing you at the webinar! Kind regards, Society for Learning Analytics Research (SoLAR) https://solaresearch.org/ Socio-spatial Learning Analytics for Embodied Collaborative Learning Embodied collaborative learning (ECL) provides unique opportunities for students to practice key procedural and collaboration skills in co-located, physical learning spaces where they need to interact with others and utilise physical and digital resources to achieve a shared goal. Unpacking the socio-spatial aspects of ECL is essential for developing tools that can support students' collaboration and teachers' orchestration in increasingly complex, hybrid learning spaces. Advancements in multimodal learning analytics and wearable technologies are motivating emerging analytic approaches to tackle this challenge. This presentation will introduce a conceptual and methodological framework of social-spatial learning analytics that map from social-spatial traces captured through wearable sensors to meaningful educational insights. The framework consists of five primary phases: foundations, feature engineering, analytic approaches, learning analytics, and educational insights. Two illustrative cases will be presented to demonstrate how the framework can support educational research and the formative assessment of students' learning. Finally, the opportunities and challenges regarding socio-spatial learning analytics are discussed. Bio Lixiang Yan is a final-year PhD candidate at the Centre for Learning Analytics at Monash University. He researches multimodal learning analytics, focusing on classroom orchestration and collaborative learning in physical learning spaces.