Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system

Abstract

The informal setting of learning at work give rise for unique challenges to the field of technology enhanced learning systems. Personalized recommendations taking into account the current context of the individual knowledge worker are a powerful approach to overcome those challenges and effectively support the knowledge workers to meet their individual information needs. Basis for these recommendations to adopt to the current context of a knowledge worker can be provided by user models which reflects the topics knowledge workers are dealing with and their corresponding knowledge levels, but research has only focused on user modeling in settings with a static underlying domain model so far. We suggest to model the users' context based on the emergent topics they are dealing with and their individual current knowledge levels within these topics by extracting the necessary information from the user's past activities within the system. Based on data from an experiment with students learning a new topic with the help of a collaborative tagging system, we started to evaluate this approach and report on first results.

Towards Learning Object Recommendations Based on Teachers' ICT Competence Profiles

“…Bozo et al [11] incorporate the teacher profile in their proposal, but build it on mostly demographic data. Schoefegger et al [17] apply heuristics to determine user work-related knowledge levels. This approach is interesting, but is based on knowledge topics that are professionspecific and, therefore, would require specific ontologies to capture teachers' actions.…”

Technology-enhanced learning in the workplace

“…The language was originally developed at the Open University of the Netherlands (OUNL) and is now IMS-Global standardized http://www.imsglobal.org/learningdesign/ increased opportunities for personalized learning will have a two-folded effect; reduction of delivery costs accompanied by more effective learning experiences, accelerating competence development, and increasing collaboration between learners. This matters in higher education as well as in workplace learning scenarios (Schoefegger, Seitlinger & Ley, 2010).…”

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

“…Other examples (e.g., Derntl, Günnemann, & Klamma, 2013;Southavilay, Yacef, Reimann, & Calvo, 2013) can be found that follow the knowledge creation metaphor, which deals with the collaborative and systematic development of common objects of activity. These LA applications model how learning materials and conceptual artifacts are collaboratively created (Schoefegger, Seitlinger, & Ley, 2010;Thüs, Chatti, Brandt, & Schroeder, 2015). The group of learners is taken as the unit of analysis, considering also their tools and common artifacts (Berendt, Vuorikari, Littlejohn, & Margaryan, 2014;Buckingham-Shum & Ferguson, 2012).…”