Sixth Multimodal Learning Analytics Workshop

Call for Papers in PDF

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, high-frequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. This year, the 6th MMLA workshop will be organised in conjunction with LAK 2017 at Simon Fraser University, Vancouver (Canada). The aim of this workshop is twofold: first, to provide hands-on experiences and help build up different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.

Important Dates

  • 15 Nov 2016: Start open submission
  • 22 Dec 2016: Submission deadline
  • 13 Jan 2017: Acceptance notification
  • 14 or 15 Mar 2016: Workshop at LAK17 Vancouver

Challenges

The field of learning analytics is shifting from an emerging area of research to a vital component of educational research and practice. Moreover, the inherently blended nature of learning makes it essential to move beyond analyses that rely solely on a single data source (usually log files). Multimodal learning analytics (MMLA) provides insights into such learning processes that happen across multiple contexts between people, devices and resources (both physical and digital), which often are hard to model and orchestrate.

MMLA leverages the increasingly widespread availability of sensors and high-frequency data collection technologies to enrich the existing data available. Using such technologies in combination with machine learning and artificial intelligence techniques, LA researchers can now perform text, speech, handwriting, sketch, gesture, affective, neurophysical, or eye gaze analyses.

This workshop has three main inter-related objectives that frame the emerging interest in MMLA: (1) disseminate the state of MMLA research; (2) facilitate and share datasets; and (3) discuss the current and future MMLA challenges and define an agenda that guides MMLA in advancing both research and practice. For that purpose, the workshop will offer the opportunity to explore MMLA techniques and data in pre-made virtual environments, using sample datasets. Besides, the participants will be able to present their own datasets or dataset proposals and discuss their challenges with the community, setting the stage for current challenges and drafting the main lines of the MMLA research agenda.

This workshop aims to join participants with different backgrounds: existing and new MMLA researchers, as well as LA researchers and practitioners that can use the workshop to discover how to incorporate multimodal data into their ongoing research.

Submission Guidelines and Dates

To contribute to the workshop, authors should submit a 2-page poster (paper) following the ACM conference guidelines. The papers can follow any of the following options:

  1. (1) A description of a multimodal data set and the challenges found in any of the analytical phases (data gathering, integration, analyses, or visualization).
  2. (2) A MMLA approach applied to a multimodal data set, including the challenges found during the process.

In both cases, the organisers encourage the authors to share their datasets with the community. The submissions will be reviewed in a single-blind way, so identifying information can be used in the review version. The contributions should be submitted using the Easychair platform.

All the accepted papers will have to prepare a poster which will be presented during the workshop. After the conference, the authors will be invited to submit an extended version of the papers taking into considerations the feedback received during the process. The extended papers will be published in the CEUR workshop proceedings.

If you have any further questions, we encourage you to contact the organisers.

Schedule (tentative)

We are working with the CrossLAK workshop to coordinate some joint activities.

  • Morning Datasets (9-12)
  • Coffee
  • Introductions
  • Presentation and Exploration of Dataset

Lunch

  • Afternoon Future Challenges (13-16)
  • Discussion and Wrap up with Datasets
  • Brainstorm Future Workshop and Defining the MMLA Futures
  • ID the Grand Challenges
  • Wrap-up and Action List

Organization

Daniel Spikol, Malmö University, Sweden ( daniel.spikol@mah.se)
Spikol is Assistant Professor at Malmö University in the Faculty of Technology and Society. He is group leader for Smart Learning at Malmö's Internet of Things and People Research Center (http://iotap.mah.se/) and a principal investigator in the PELARS project (http://pelars.eu/). His current research interests are in ubiquitous media that explores how people learn and what technologies can be used to support these personalised experiences with a strong focus on learning analytics.

María Jesús Rodríguez-Triana, École Polytechnique Fédérale de Lausanne, Switzerland / Tallinn University, Estonia (maria.rodrigueztriana@epfl.ch)
María Jesús Rodríguez-Triana is a Senior Researcher at École Polytechnique Fédérale de Lausanne and Tallinn University. Her research lines address classroom orchestration, learning design, and learning analytics in ubiquitous and distributed learning environments. Currently, she is investigating the challenges towards the adoption of learning analytics (among practitioners, institutions, and policy makers) and how the integration of heterogeneous data (coming from the learning technologies, provided ad-hoc by the participants, and automatically collected from the environment) may contribute to overcome such barriers.

Luis P. Prieto, Tallinn University, Estonia (lprisan@tlu.ee )
Luis P. Prieto is a Senior Researcher Fellow and former Marie Curie Fellow, at the School of Educational Sciences of Tallinn University (Estonia). His research deals with the design and evaluation of educational technologies for everyday use in the classroom, including distributed learning environments, tangible UIs, and their analysis using mobile eye-tracking and ubiquitous sensors. He has co-organized multiple research and teacher professional development workshops on the topics of orchestration of learning and learning analytics.

Emanuele Ruffaldi, Scuola Superiore Sant'Anna, Italy (e.ruffaldi@sssup.it)
Dr. Ruffaldi is Assistant Professor at PERCRO of Scuola Superiore Sant'Anna. He received the PhD in Perceptual Robotics from SSSA in 2006. Inside PERCRO he is leading the group "Sensing, Modelling and Learning for Humans". He is PI of the H2020 robotic project RAMCIP, FP7 project PELARS, participating to FP7 REMEDI, and was WP leader in IP SKILLS. His research interests are in the field of virtual environments for robotics, machine learning and Human-Robot interaction.

Mutlu Cukurova, UCL Knowledge Lab, UK (m.cukurova@ucl.ac.uk)
Mutlu Cukurova is a Research Fellow at UCL Knowledge Lab, University College London.His is investigator in the PELARS project and his current work focuses on the investigation of collaborative learning processes in technology-enhanced learning environments with the use of mixed-method research methodologies including multimodal learning analytics. He is particularly interested in the potential of technology to continuously support and evaluate learning processes "in situ". His PhD was about the impact of innovative teaching approaches on students' knowledge and skill acquisition in STEM subjects.

Bahtijar Vogel, Malmö University, Sweden (m.cukurova@ucl.ac.uk)
Bahtijar Vogel is a Senior Lecturer at the Faculty of Technology and Society, Malmö University. He holds a PhD in Computer Science from Linnaeus University. His current research lies in the area of Mobile and Web engineering and software architecture, and Internet of Things (IoT), where he builds the concept of an open architecture for deploying flexible, evolvable and usable web and mobile systems in different application domains and contexts. He is a researcher in the PELARS project.

Ulla Lunde Ringtved, University College Nordjylland, Denmark (ulr@ucn.dk)
Ulla Ringtved is senior lecturer at UCN, dept. of Technology and Ph.D. student at Aalborg University. Her research focus is on data informed learning and innovative feedback and assessment design within learning designs in Technology Enhanced Learning and Teaching. Her background is in Danish and international Constructing Education, she also teaches in Learning Analytics on Master of Information technology and learning at AAU, and is the organizer of the Learning Analytics Summer Institute 2013. 2015 and 2016 in Denmark.

Marcelo Worsley, Northwestern University, USA (mw@northwestern.edu)
Marcelo Worsley is an Assistant Professor of Electrical Engineering and Computer Science and Education and Social Policy at Northwestern University. Previously, he was a postdoctoral researcher at the University of Southern California, and completed his PhD at Stanford University. Marcelo's research employs multimodal interfaces and multimodal analysis to study complex learning environment.

Xavier Ochoa, ESPOL, Ecuador (xavier@cti.espol.edu.ec)
Xavier Ochoa is a Principal Professor at the Faculty of Electrical and Computer Engineering at Escuela Superior Politécnica del Litoral (ESPOL) in Guayaquil, Ecuador. He is the coordinator of the Research Group on Teaching and Learning Technologies (TEA) at ESPOL. He obtained his Ph.D at the University of Leuven in 2008 for his work on Learnometrics. Xavier has served in many coordination bodies in the field: the ARIADNE Foundation, the Latin American Community on Learning Technologies (LACLO), the Latin American Open Textbook Initiative (LATIn), the Global Brokered Exchange of Learning Objects (GLOBE) and the Society for Learning Analytics Research. He coordinates several international and regional projects in the field of Learning Technologies. His main research interests revolve around Multimodal Learning Analytics, Curricular Analytics and Personalized Learning.