Workshop objectives:
- Unite interdisciplinary researchers to share, explain, and discuss conceptual, theoretical, methodological, analytical issues related to multimodal multichannel data and learning analytics.
- Share advanced statistical, data mining, and machine-learning methods for analyzing complex, multimodal multichannel process data and discuss implications for education and training via data visualizations (e.g., dashboards, intelligent virtual humans, etc.).
- Present and discuss strengths and weaknesses associated with collecting multimodal data, coding schemes, data pipelines, algorithms, synchronizing data, etc. and creating and extending an international network that allow for sharing of resources between researchers across disciplines.
Topic areas and submissions could include:
We are seeking workshop papers that seek to:
- Multimodal data and analysis from various learning settings/contexts, including formal, informal and corporate settings.
- Self-regulated and goal-oriented behaviour such as setting meaningful learning goals, strategy selection, progress measurement toward goal achievement, and self evaluation of emerging understanding of the topic.
- Self-monitoring understanding and modification of learning plans, goals, strategies, and effort in relation to contextual conditions (e.g., cognitive, motivational, resources, and task conditions).
- Challenges for researchers, educators, instructional designers, learning engineers, and data scientists in collecting, tracking, modeling a myriad of complex processes using a variety of methods, tools, and sensors (e.g., synchronizing time, matching trace data to cognitive processes, making instructional decisions to optimize learning).
- Evaluating complex processes through the measurement, analysis, and modeling of multimodal multichannel data (e.g., log files, eye tracking, physiological sensors, facial expressions of emotions) during learning and problem solving across formal (e.g., school) and real-world contexts (e.g., online learning, military, industry, informal learning).
- Understanding the complex nature of unfolding SRL processes has recently been addressed by emerging interdisciplinary research using online trace methods (e.g., log files, eye tracking, think-aloud protocols, physiological sensors, screen recording of human-machine interactions, classroom discourse).
We invite short papers (2-4 pages) addressing the above topics, both conceptual and research results.
Please submit papers to roger.azevedo@ucf.edu.
Important Dates:
- Submission opens: Nov 1, 2019
- Submission deadline: December 15, 2019
- Acceptance Notification: January 5, 2020
- Camera ready version 07 February 2020
- Workshop - 23 March 2020
Submissions should be formatted using the LAK companion proceedings template.
All submitted papers are reviewed by at least two members of the Program Committee. Accepted papers will be published in the Companion Proceedings of the LAK 2020 conference.