Techniques for Investigating Collaboration with Multimodal Approaches
Workshop: Data Explorations and Techniques ISLS 2023 Annual Meeting Montréal – June 11, 2023
Call for Participation
We are delighted to announce that the Techniques for Investigating Collaboration with Multimodal Approaches workshop will be held on June 12 2023, in conjunction with the .
Collaborative learning is based on multimodal interactions between people. This Multimodal Learning Analytics (MMLA) workshop aims to bring together diverse fields that combine educational, computational, psychological, and related research into how people learn and how this complex process can be supported with technology. The workshop will explore methods and techniques for how various stakeholders can capture, analyze, and make sense of these multimodal learning interactions. The core challenge is to capture these interactions meaningfully for learners and teachers for improved learning and teaching at scale. MMLA combines the power of affordable sensor technologies and advances in machine learning to observe and analyze learning activities. The workshop is planned for a full day and will include data collection, processing, analysis, visualization, and knowledge exchange. The workshop is designed for researchers interested in collaboration analytics that range from theoretical to methodological and technical.
The workshop explores different methods and techniques for capturing, analyzing, and making sense of complex human interactions that generate quantitative and qualitative data for improved student learning and teacher support. MMLA combines affordable sensor technologies and advances in machine learning to observe and analyze learning activities. Real-time and automatic video and audio analysis can support learning by automating the study of activities. At the same time, researchers work to make the stream of multimodal data into meaningful layers that explain critical insights to teachers and students.
The workshop is for a full day. Participants can submit datasets (see participation). During the workshop, small groups will be formed to work on the different datasets. Additionally, we will supply some datasets and scenarios to work with. The workshop is structured as follows:
Collection of Data: Discuss and demo different technologies and methods for collecting data that combine human and machine observations.
Processing of Data: Explore techniques for fusing data from observational frameworks to different sensor streams from learning analytic systems.
Analysis: Explore data analysis methods, including epistemic network analysis (Shaffer & Ruis, 2016)), machine learning approaches, and descriptive statistics.
Visualization: Explore different techniques for visually representing multimodal collaborative data.
Privacy & Ethics: Discuss human-centered approaches to data protection, privacy, and ethics.
Knowledge Exchange: Share results and discuss insights, challenges, and future opportunities.
10 May 2023 Deadline for submission of abstracts and dataset descriptions
12 May 2023: Notification of acceptance
Workshop Date: 11 June : See FULL DAY | 9:00 AM TO 5:00 PM EST | IN-PERSON
The workshop requires a 1-page abstract that needs to include one or more of the following, but not limited to:
Description of Open Datasets from their work that they are willing to share (Open Data means the kind of data which is open for anyone and everyone for access, modification, reuse, and sharing)
A description of Sensor/Data gathering setups and prototypes: Data analysis/annotation methods and tools (e.g., Visual Inspection Tool and coding schemas that can be used), willingness to demo and collect data
Description of Learning activities and Pedagogical designs that could benefit significantly from MMLA techniques
Examples of MMLA research designs or case studies
Participation in the workshop will be prioritized for participants that submit the 1-page abstract and reviewed by the program committee. You can also submit a dataset that can be used in the workshop, however please also submit a description of the dataset to easychair and we will provide a git repository for sharing.
However, some spots will be open for people to join without submissions.