Submission deadline: CLOSED
Publication: July–September 2018
Guest Editors:
- Michail N.Giannakos, Norwegian University of Science and Technology
- Demetrios G. Sampson, Curtin University, Australia
- Kinshuk, Athabasca University, Canada
- Maggie M. Wang, The University of Hong Kong
Multimedia analytics is a new and exciting research area that combines multimedia analysis and visual analytics to create systems that analyze large-scale multimedia collections. Multimedia analysis focuses on images, video, and audio and has made progress in analyzing individual media types. Visual analytics provides technology for human-centric analysis through dynamic, active visual interfaces for all forms of data, offering scale-independent analytics. The size and complexity of multimedia collections is ever increasing, as is the desire to discover useful knowledge from these collections, with the anticipated impact ranging from the advancement of science to increased company profits. Today’s emerging demands to comprehend and articulate the big picture require constructing and representing the context, content, and relationships from multimedia information sources. Therefore, multimedia big data analytics is a core research challenge of the multimedia research community.
Using digital technologies in teaching and learning has created new challenges for data-based, evidence-driven teaching and learning analytics methods and tools. Such tools can provide better insight into the learning process for individual learners or groups of learners, providing them with learning experiences customized to their profiles and needs. Furthermore, such tools can empower educational organizations and teachers to make more informed educational decisions at the organizational level as well as at curriculum, course, and lesson design levels. At the core of these challenges is multimedia educational content and the interactions of learners (and their tutors) with this content. Collecting, processing, and analyzing multimedia educational data in different formats (including video and images, 2D graphs and 3D virtual worlds, speech and audio, and text and documents) produced from multiple sources (video lectures, digital games, virtual worlds, and social media) is an emerging and promising research area. Topics of interest for this special issue include, but are not limited to, the following:
- Visual analytics of multimedia educational data
- Multimedia content processing and visualization to support education
- Semantic retrieval of multimedia big data for learning
- Infrastructure and technological issues of multimedia analytics in teaching and learning
- Privacy, security and ethical issues related with multimedia analytics in teaching and learning
- Multimedia analytics for user modeling, adaptation, and personalization
- Multimedia analytics for decision making, awareness, and reflection in educational environments
- Cross-media and multimodal analysis of multimedia big data in education
- Theories and applications for heterogeneous multimedia big data analytics in education
- Content analysis and mining for multimedia big data in education
- Semantic retrieval from multimedia educational data
Submission Guidelines
See the author guidelines for submission requirements. Submissions should not exceed 6,500 words, including all text, the abstract, keywords, bibliography, biographies, and table text. Each table and figure counts for 200 words. Submit electronically through ScholarOne Manuscripts, selecting this special-issue option.
Questions?
Please contact the guest editors at mm3-2018@computer.org.