Proceedings Seventh International Workshop on Groupware. CRIWG 2001, 2001
... Domain specific visual tools like STELLA [lo] or Rational Rose [ 1 I] provide visual interfac... more ... Domain specific visual tools like STELLA [lo] or Rational Rose [ 1 I] provide visual interfaces for existing model semantics, here system dynamics (STELLA) or UML (Rational Rose). ... [5] Streitz, N., Haake, J., Hannemann, J., Lemke, A., Schuler, W., Schiitt, H. & Thiiring, M. (1992). ...
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
ABSTRACT Traditional recommender systems are well established in scenarios in which "eno... more ABSTRACT Traditional recommender systems are well established in scenarios in which "enough"items, users and ratings are available for the algorithms to operate on. However, automatic recommendations are also desirable in smaller online communities which only contain several hundred items and users. Collaborative filters, as one of the most successful technologies for recommender systems, do not perform well here. This paper argues that recommender systems can make use of contextual information and domain specific semantics in order to be able to generate recommendations also for these smaller usage scenarios. The new hybrid recommendation approach presented in the paper enhances traditional neighborhood-based collaborative filtering techniques through the use of new kinds of data and a combination of different recommendation methods (rule, demographic, and average based). While the algorithmic techniques presented in this paper are suitable (especially) for smaller online communities, they can also be applied to improve the quality of recommendations in larger communities. The approach was implemented and evaluated in a small regional bound parent education community. A multi-staged evaluation was conducted in order to determine the quality of recommendations: A cross-validation (recall), an expert questionnaire (recommendation quality) and a field study (user satisfaction). The results show that recommenders even for smaller communities are possible and can produce high quality recommendations.
2013 IEEE 13th International Conference on Advanced Learning Technologies, 2013
ABSTRACT Building an Intelligent Tutoring System (ITS) from scratch usually requires technologica... more ABSTRACT Building an Intelligent Tutoring System (ITS) from scratch usually requires technological skills, expertise about the domain and tasks, and pedagogical knowledge. We propose a middleware which facilitates the construction of intelligently supported learning systems independently from the underlying (formalized) domain knowledge using typical re-usable components of ITSs, exchangeable plug-ins, and machine learning techniques running in Matlab. We proved our concept using a web-based programming environment as an example of an user interface component interacting with our ITS middleware.
International Journal of Learning Technology, 2014
Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain... more Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.
Proceedings Seventh International Workshop on Groupware. CRIWG 2001, 2001
... Domain specific visual tools like STELLA [lo] or Rational Rose [ 1 I] provide visual interfac... more ... Domain specific visual tools like STELLA [lo] or Rational Rose [ 1 I] provide visual interfaces for existing model semantics, here system dynamics (STELLA) or UML (Rational Rose). ... [5] Streitz, N., Haake, J., Hannemann, J., Lemke, A., Schuler, W., Schiitt, H. & Thiiring, M. (1992). ...
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
ABSTRACT Traditional recommender systems are well established in scenarios in which "eno... more ABSTRACT Traditional recommender systems are well established in scenarios in which "enough"items, users and ratings are available for the algorithms to operate on. However, automatic recommendations are also desirable in smaller online communities which only contain several hundred items and users. Collaborative filters, as one of the most successful technologies for recommender systems, do not perform well here. This paper argues that recommender systems can make use of contextual information and domain specific semantics in order to be able to generate recommendations also for these smaller usage scenarios. The new hybrid recommendation approach presented in the paper enhances traditional neighborhood-based collaborative filtering techniques through the use of new kinds of data and a combination of different recommendation methods (rule, demographic, and average based). While the algorithmic techniques presented in this paper are suitable (especially) for smaller online communities, they can also be applied to improve the quality of recommendations in larger communities. The approach was implemented and evaluated in a small regional bound parent education community. A multi-staged evaluation was conducted in order to determine the quality of recommendations: A cross-validation (recall), an expert questionnaire (recommendation quality) and a field study (user satisfaction). The results show that recommenders even for smaller communities are possible and can produce high quality recommendations.
2013 IEEE 13th International Conference on Advanced Learning Technologies, 2013
ABSTRACT Building an Intelligent Tutoring System (ITS) from scratch usually requires technologica... more ABSTRACT Building an Intelligent Tutoring System (ITS) from scratch usually requires technological skills, expertise about the domain and tasks, and pedagogical knowledge. We propose a middleware which facilitates the construction of intelligently supported learning systems independently from the underlying (formalized) domain knowledge using typical re-usable components of ITSs, exchangeable plug-ins, and machine learning techniques running in Matlab. We proved our concept using a web-based programming environment as an example of an user interface component interacting with our ITS middleware.
International Journal of Learning Technology, 2014
Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain... more Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.
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