3 LAYERS track at the 15th iKnow Conference

Learning Layers sponsors three special tracks at the the 15th anniversary of the I-Know Conference (http://i-know.tugraz.at/call-for-papers/#specialtracks). The topics will cover social knowledge management, education for smart industry and recommender systems. Submissions are welcome until the 22nd of June (abstract by 8th of June).


Social Knowledge Management: from collections of documents to connections of people and physical objects

Chairs: Ronald Maier, Andreas Schmidt

Although knowledge has always been considered as inherently situated in the heads of people, knowledge management has started out with a technocratic focus on representations of knowledge in documents. The manifold developments of knowledge management since then include aspects of semantics and ontologies, activities and processes, assessment and evaluation, integration and visualization and a focus on the dynamics of knowledge maturing from emergent knowledge created by individuals to standardized knowledge applied by societies at large. With the advent of social software and the recent developments in networking of physical objects, knowledge management has shifted its attention more recently to collaboration and social relationships in collectives of people, from small teams to large crowds, termed social knowledge management, as well as to the inclusion of representations of and the interaction with physical objects, termed the Internet of things.

We solicit submissions that address the following topics:

  • Knowledge creation and knowledge maturing – from teams to crowds
  • Social collaboration, social networks, social spaces
  • Boundary spanning and the boundaryless organization
  • Connectivity in teams and organizations
  • Role of physical objects in knowledge management
  • Motivational aspects of social knowledge management

Industry 4.0: educating the workforce for smart industries

Chairs: Martin Wolpers, Ralf Klamma

The introduction of smart factories, internet of things and cyber-physical systems (in Germany coined “Industry 4.0″) changes the industrial manufacturing and production workplaces dramatically.
Companies and employees are faced with new requirements regarding the workplace. It is questionable if existing pedagogical and technological concepts address the new emands on the workforce sufficiently and adequately. Today, and in spite of political wishes, industry 4.0 related workplaces are still at the verge of emergence.

This track aims to foster discussions on new learning and training challenges in smart industries where products will „know“ the workforce. Papers are invited that discuss Industry
4.0 related learning scenarios and how they are being addressed.

Topics of interest include, but are not limited to, the following:

  • Collaborative learning
  • Micro-format learning
  • Just-in-time learning
  • Education with/cyber-physical systems
  • Wearables for learning purposes
  • Smart factory learning scenarios
  • Personalization and adaptation
  • Recommendation
  • Learning resource creation and management

Recommender Systems: from algorithms to Big Data recommendation systems

Chairs: Alexander Felfernig, Elisabeth Lex

Recommender systems combine historical data on user preferences, (user) similarities and past behavior to suggest and predict items a user might be looking for. While they have been proven successful in e.g. e-commerce applications, they can also support organizations in better identifying competences, help engage users in a continuous and dynamic knowledge exchange, and customize dissemination of knowledge as much as possible.

The objective of this special track is to bring together researchers and practitioners involved in developing, testing, and fielding recommender systems, especially in the area of knowledge management. The special track focuses on all aspects of recommender systems and it will provide a forum for discussing current practice and recent research results

Topics include but are not limited to:

  • Personalization and recommendations in knowledge management
  • Recommender algorithms
  • Case studies of real-world implementations
  • Evaluation methodologies for recommender systems
  • Field and user studies of recommender systems
  • Context-aware recommenders
  • Cold-start problem
  • Expert recommenders
  • New trends and challenges in recommender systems
  • Machine learning for recommendation
  • Social recommenders
  • Semantic technologies for recommendations
  • Recommendations in TEL
  • Trust and reputation in recommender systems
  • User modelling for recommendations
  • User interfaces for recommender systems
  • Scalability of recommender systems


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