Topics

We invite submissions on topics including but not limited to the following:

Conceptual

  • New models of learning enabled by analytics
  • Personalization and adaptation in the learning process through analytics
  • Learner modeling
  • The analysis of emotion, flow, and affective data in learning environments
  • Ethical considerations (e.g., privacy and ownership)
  • Learning analytics for accreditation
  • The influence of analytics on designing for learning
  • Learning analytics patterns
  • Organizational dynamics and adoption strategies
  • Educational research methods and learning analytics
  • Learning analytics in relationship to other fields (e.g., educational research,  educational data mining, web science, etc.)

Technical Innovations for Sensemaking

  • Network analysis methods for understanding learning
  • Visualization techniques
  • Attention metadata for learning
  • Data mining and machine learning techniques in learning analytics
  • Natural language processing and text mining in learning analytics
  • The role of knowledge representation and ontologies in learning analytics
  • The semantic web and linked data applied to learning analytics
  • Analytic tools that could be used for learning
  • “Big Data” applications and opportunities in learning and education
  • Learning environments enhanced with analytics
  • Architecture of learning environments and implications for learning analytics
  • Recommendation Engines
  • Interfaces for learning analytics
  • Decision-support systems for learning
  • Interventions based on analytics
  • Visualizations to support awareness and reflection
  • Social and technical systems to manage information abundance
  • Personalization and adaptation of the learning process
  • Corporate and higher education case studies of learning analytics
  • Learning analytics for intelligent tutoring systems
  • Open data and data access for learners
  • Harmonizing individual learning with organizational learning
  • Organizational learning and knowledge sharing models
  • Use of learning analytics in centralized (learning management systems) and decentralized (personal learning environments) settings

Applications and Use Cases

  • Interventions based on analytics
  • Visualizations to support awareness and reflection
  • Social and technical systems to manage information abundance
  • Personalization and adaptation of the learning process
  • Corporate and higher education case studies of learning analytics
  • Learning analytics for intelligent tutoring systems
  • Open data and data access for learners
  • Harmonizing individual learning with organizational learning
  • Organizational learning and knowledge sharing models
  • Use of learning analytics in centralized (learning management systems) and decentralized (personal learning environments) settings
  • Planning, deploying, and evaluating enterprise-wide learning analytics
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