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CROKODIL provides support for
the collaborative acquisition and
management of web resources
for learning purposes by
offering:
 Semantic tagging (tag types)
 Activity hierarchies to
structure these resources
 Learner groups
Application Scenario
Towards Ranking
in Folksonomies for
Personalized Recommender
Systems in E-Learning
Mojisola Anjorin, Christoph Rensing, Ralf Steinmetz
Contact: mojisola.anjorin@kom.tu-darmstadt.de
Personalized recommender systems
support learners by suggesting relevant
resources for learning purposes.
The challenge however, is identifying
these relevant resources considering the
learner’s personal context and needs.
To this aim, additional semantics found
in folksonomies can be exploited to
enhance the ranking of resources
Motivation
 Create a folksonomy-based
recommender system
 Rank resources considering
context semantics (Extension of
FolkRank)
 Integrate User Feedback and
Relevance Ranking (Rochio’s
approach)
 Offer explanations of
recommendations to stimulate
reflection
 Enrich recommendations with
external learning resources (OER)
Conceptual Approach
 Implement and
evaluate algorithms
 Integrate approach in
application scenario
 Evaluate approach
 Conduct usability
study to evaluate user
acceptance
Future Work

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Towards Ranking in Folksonomies for Personalized Recommender Systems in E-Learning.

  • 1. CROKODIL provides support for the collaborative acquisition and management of web resources for learning purposes by offering:  Semantic tagging (tag types)  Activity hierarchies to structure these resources  Learner groups Application Scenario Towards Ranking in Folksonomies for Personalized Recommender Systems in E-Learning Mojisola Anjorin, Christoph Rensing, Ralf Steinmetz Contact: mojisola.anjorin@kom.tu-darmstadt.de Personalized recommender systems support learners by suggesting relevant resources for learning purposes. The challenge however, is identifying these relevant resources considering the learner’s personal context and needs. To this aim, additional semantics found in folksonomies can be exploited to enhance the ranking of resources Motivation  Create a folksonomy-based recommender system  Rank resources considering context semantics (Extension of FolkRank)  Integrate User Feedback and Relevance Ranking (Rochio’s approach)  Offer explanations of recommendations to stimulate reflection  Enrich recommendations with external learning resources (OER) Conceptual Approach  Implement and evaluate algorithms  Integrate approach in application scenario  Evaluate approach  Conduct usability study to evaluate user acceptance Future Work