Topics
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Conference organizers are seeking submissions that cover various topics related to learning analytics including (but not limited to):
Technical
- Software development and use in analytics
- The role of knowledge representation and ontologies in learning analytics
- The semantic web and linked data: meaning in connections
- Data mining in learning analytics
- Artificial intelligence in learning analytics
- Internet of things (sensors) and learning applications
- “Big Data” applications and opportunities in learning and education
- Latent semantic analysis/natural language processing
- Attention metadata
- Architecture of learning environments and implications to learning analytics
- Software needed to advance learning analytics as a field
Application
- Visualization: data, learner networks, conceptual knowledge
- Predictive applications of data
- Interventions based on analytics
- Social and technical systems to manage information abundance
- Personalization and adaptivity in the learning process
- Corporate and higher education case studies of learning analytics
- Learning analytics for intelligent tutoring systems
- Open data: data access for learners
- Harmonizing individual learning with organizational learning
- Importing insights for existing analytics
- Use of learning analytics in centralized (learning management systems) and decentralized (personal learning environments) settings
Conceptual & Pedagogical
- The relationship between learning analytics and existing theories and approaches (such as pedagogical models and learning sciences)
- Social network analysis
- Cognitive modelling
- Harnessing the power of context and location aware systems
- Informal learning: integrating learning and knowledge systems
- Privacy & ethics in learning analytics
- The influence of analytics on designing for learning
- The influence of analytics on delivery and support of learning
Paper Categories
The following types of original papers are solicited:
- Research papers: These should report a substantial research contribution to learning analytics or the application of analytics. Full paper submissions should not exceed 20 pages.
- Vision or conceptual papers: These may describe interesting, visionary, or thought-provoking concepts that are not yet fully developed or evaluated, make an initial contribution to challenging research issues in learning analytics. These papers should not exceed 10 pages.
- Mini-tutorial papers: Learning analytics is a young field of research with contributions from technical, social, and pedagogical domains. As such, there is a need for illustrations, examples, and cross-discipline discussion to unite these domains. Mini-tutorials are solicited that provide discussion points for mapping common ideas between related and complementary research topics of learning analytics. A mini-tutorial submission should be between 15 and 20 pages.
- Tool demonstration papers: Learning analytics focuses on new technologies and tools. As a result, we seek papers that present software tools related to the field of learning analytics. These papers will accompany a tool demonstration to be given at the conference. These papers must not exceed 10 pages. The selection criteria include the originality of the tool, its innovative aspects, the relevance of the tool to learning analytics, and the maturity of the tool.
Paper Submission and Publication
All the accepted papers will be published in a post proceedings open volume in Springer’s Lecture Notes in Computer Science series (pending Springer’s approval). Selected and revised papers will be published in a special issue of a leading international journal.
Please submit papers through Learning Analytics EasyChair conference system:
http://www.easychair.org/conferences/?conf=la_11
All papers must be formatted according to the Springer’s Lecture Notes in Computer Science style: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0