Call for Papers

First International Workshop on Multimodal Learning Analytics

The 1st International Workshop on Multimodal Learning Analytics (MLA 2012) will be held on October 26th, 2012 in Santa Monica, California as a satellite event of the 14th International Conference on Multimodal Interaction (ICMI 2012).

Important dates
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July 27th, 2012 Submission deadline
August 31st, 2012 Notification of acceptance
September 15th, 2012 Camera ready papers due
October 26th, 2012 Workshop

Motivation and workshop topics
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Multimodal learning analytics, learning analytics, and educational data mining all are emerging disciplines concerned with developing techniques to more deeply explore unique data in educational settings. They use the results based on these analyses to understand how students learn. Among other things, this includes how they communicate, collaborate, and use digital and non-digital tools during learning activities, and what the impact of these tools is on acquiring new skills and knowledge. Past research on learning analytics has aimed to model students’ knowledge and motivation during learning, to contribute to understanding learning dynamics during computer-supported and collaborative exchange, to diagnose students’ failure to progress during learning activities, and to adapt learning activities to students’ limitations and learning status. More generally, advances in learning analytics are expected to contribute new empirical findings, theories, methods, and metrics for understanding how students learn. They also can contribute to improving pedagogical support for students’ learning through assessment of new digital tools, teaching strategies, and curricula.

The most recent direction within this area is multimodal learning analytics, which emphasizes the analysis of natural rich modalities of communication during situated learning activities. This includes students’ speech, writing, and nonverbal interaction (e.g., gestures, facial expressions, gaze). A primary objective of multimodal learning analytics is to analyze coherent signal and activity patterns in order to uncover entirely new learning-oriented phenomena. Another is to develop a better collection of converging metrics for learning-related behavior and landmarks. These include metrics that eventually could be analyzed unobtrusively, continuously, automatically, and in natural classroom environments and mobile settings.

Progress in these areas will transform our ability to identify and stimulate effective learning, support more rapid feedback and responsive intervention, and facilitate learning in more diverse students and contexts.

Keynote Speakers
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Prof. Sharon Oviatt (Incaa Designs) She is internationally known for her work in human-centered interface design and evaluation, educational interfaces, mobile interfaces, and pen, speech, and multimodal interfaces. She has been a lifelong educator and prolific scientist, with over 130 scientific publications in a wide range of venues. In 2000, she received a National Science Foundation Creativity Award for pioneering research on interfaces.

Prof. Paulo Blikstein (Stanford University) He is an Assistant Professor at the Stanford University School of Education and (by courtesy) the Computer Science department. He directs the Transformative Learning Technologies Lab which focuses on researching how new technologies can deeply transform the learning of science, engineering, and mathematics. His work involves cutting-edge, multi-modal technologies for constructionist learning environments where students learn science and mathematics by building sophisticated projects and devices. Paulo is a recipient of the prestigious NSF Early Career Award and is also the director of the Lemann Center for Educational Entrepreneurship and Innovation in Brazil.

Workshop topics include, but are not limited to:
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1. Multimodal Behavior Analysis
Audiovisual nonverbal behavior analysis
Electronic pen analysis and feature extraction
Detection and analysis of behavioral cues
Multimodal feature development and analysis
Social signals in learning
2. Performance Evaluation and Prediction
Wrong/Correct answer prediction
Multimodal group performance evaluation
Confidence, expertise, motivation, attention and dominance recognition
Recognition of dominant expert in group
3. Multimodal Educational Data Mining
Analysis of Group Behavior and Group Dynamics
Visualization of Group Behavior and Group Dynamics
4. Perceptual Studies
Estimation of group/individual performance by humans vs. automatic
Perception of confidence, expertise and dominance
5. Machine Learning Algorithms for Multimodal Learning Algorithms
Multimodal information fusion
Multi-classifier systems and multi-view classifiers
Spatiotemporal information integration
6. Applications Relevant to Multimodal Learning Analytics
Assistive systems
Unobtrusive situated learning analytics
Teacher training
Learning diagnostics
Evaluation of educational interfaces, teaching materials, strategies and curricula
7. Related Benchmark Data Sets