Workshop on Feedback from Multimodal Interactions in Learning Management Systems

Located at the 7th International Conference on Educational Data Mining (EDM 2014)
July 4, 2014 - July 7, 2014
Institute of Education, London, UK

Workshop programme:

Friday July 4 2014, 14:00 - 17:30

Interventions During Student Multimodal Learning Activities: Which, and Why?
Beate Grawemeyer, Manolis Mavrikis, Sergio Gutierrez-Santos and Alice Hansen

Multimodal Affect Recognition for Adaptive Intelligent Tutoring Systems
Ruth Janning, Carlotta Schatten and Lars Schmidt-Thieme

Collaborative Assessment
Patricia Gutierrez, Nardine Osman and Carles Sierra

Mining for Evidence of Collaborative Learning in Question & Answering Systems
Johan Loeckx

Creative Feedback: a Manifesto for Social Learning
Mark d'Inverno and Arthur Still

Workshop proceedings:


Virtually all learning management systems and tutoring systems provide feedback to learners based on their time spent within the system, the number, intensity and type of tasks worked on and past performance with these tasks and corresponding skills. Some systems even use this information to steer the learning process by interventions such as recommending specific next tasks to work on, providing hints etc. Often the analysis of learner / system interactions is limited to these high-level interactions, and does not make good use of all the information available in much richer interaction types such speech and video. In the workshop Feedback from Multimodal Interactions in Learning Management Systems (FFMI@EDM’2014) we would like to bring together researchers and practitioners who are interested in developing data-driven feedback and intervention mechanisms based on rich, multimodal interactions of learners within learning management systems, and among learners providing mutual advice and help. We aim at discussing all stages of the process, starting from preprocessing raw sensor data, automatic recognition of affective states to learning to identify salient features in these interactions that provide useful cues to steer feedback and intervention strategies and leading to adaptive and personalized learning management systems.

Full description:

Several techniques have been applied to mine data in Learning Management Systems. The immediate scopes are performance prediction, emotion recognition, speech analysis and many others. Less effort has been dedicated to answer the question how this analysis, especially of multimodal data, could be integrated to ameliorate the experience within Learning Management Systems for adaptive and personalized feedbacks, which are one of the mostly used intervention strategy.
A good feedback design should answer four main questions: when, what, how and why. It is crucial to know when to display the feedback, i.e., not too early or too late, and because the student really needs help (why). Moreover, one has to decide what the feedback should contain and in which format this content should be presented, e.g., in audio or in visual format (how). These questions were answered at the beginning with fixed rule- and content-based strategies. Nowadays, the main research focuses are to solve them through Educational Data Mining and to transfer the problem from structured to unstructured learning environments.
The available data for this kind of task changed over the last 10 years because of several reasons. Log files are automatically registered, without student’s awareness. This information can go from fine grained event detection of mouse movements and clicks, to coarse score records. Thanks to the increasing availability and speed of internet connection this data can be collected in a central storage for later being analyzed. Moreover, cheaper sensors, to be found commonly integrated in laptops, tablets, and cell phones, allow a multi-modal data collection that once was possible only in laboratory with invasive settings. To give an idea of the richness of the available information we can list: web and depth cams for video recordings of facial behavior, gestures and eye-tracking, as well as certain physiological parameters, such as heart- and respiration rate, facial temperature, microphones for speech and other sound recordings, as well wearable inertial devices for movement detection. Also other biometrics sensors can be purchased: commercial easy-on Electroencephalogram (EEG), electrodermal activity, such as skin conductance (EDA), skin temperature, etc.
The main scope of the workshop will be to bring together researchers of the Educational Data Mining community to exchange information about the current state of the art in personalized and adaptive feedback and interventions strategies, as well as about their possible multimodal data-driven extension in Learning Management Systems. Particular focus will be given to multimodal interaction, as novel source of information.

Workshop content and themes:

Important dates:

April 24, 2014: Workshop papers submissions due.
May 22, 2014: Notification of acceptance of Workshop papers.
June 1, 2014: Camera ready papers due.
July 4-7, 2014: Conference EDM 2014.

Submission details:

Workshop papers should not be longer than 8 pages in EDM conference paper format (templates: Word, LaTex) and describe original and unpublished work.

Submissions (PDF) will be accepted through EasyChair: Submission.

Workshop papers will be published in a joint volume in CEUR workshop proceedings (ISSN: 1613-0073).

A selection of accepted workshop papers will be invited to extend their submission (providing a significant contribution beyond the workshop paper) for a special issue in the Journal of Educational Data Mining (JEDM) on Advances and Emerging trends in EDM.

Programme chairs:

Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arvid Kappas, School of Humanities and Social Sciences, Jacobs University Bremen, Germany
Carles Sierra, IIIA, Spanish Research Council, University of Technology, Sydney
Emanuele Ruffaldi, PERCRO, Scuola Superiore Sant’Anna, Pisa, Italy

Programme committee:

Sergio Gutierrez-Santos, Birkbeck, University of London, UK
Mark d'Inverno, Goldsmiths, University of London, UK
Manolis Mavrikis, IOE, University of London, UK
Francois Pachet, Sony Computer Science Laboratory Paris, France
Matthew Yee-King, Goldsmiths, University of London, UK
Helen Hastie, Heriot Watt University, Edinburgh, Scotland
Iolanda Leite, Yale University, Connecticut, United States
Luis de-la-Fuente, International University of La Rioja, Spain
Helen Pain, ILCC, Human Communication Research Centre, University of Edinburgh


Prof. Dr. Dr. Lars Schmidt-Thieme
Ruth Janning, M.Sc.

Information Systems and Machine Learning Lab (ISMLL)
Institute of Computer Science
University of Hildesheim
Marienburger Platz 22
D-31141 Hildesheim, Germany

Fax: +49 (05121) 883 40361