In this work, the design and implementation of several physics scenarios using haptic devices are presented and discussed. Four visuo-haptic applications were developed for an undergraduate engineering physics course. Experiments with experimental and control groups were designed and implemented. Activities and exercises related to classical mechanics concepts were applied in virtual scenarios....

The emergence of fault prediction and health management (PHM) technology has proposed a new solution is suitable for implementing the functions improving intelligent control system. However, research application PHM model in system electronic equipment are few at present, there many problems that need to be solved urgently itself. In order solve such problems, this paper studies equipment-statu...

The DIN Didactical Object Model extends the approaches of existing Educational Modeling Languages introducing specifications for contexts and experiences. In this paper, we show how the Didactical Object Model can be used for sharing didactical expertise. Educational Modeling Languages change the design paradigm from content orientation towards process-/ activity orientation. Especially in the ...

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Crosscompany defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness. key words: machine learning,...

The European Go-Lab project offers Inquiry Learning Spaces (ILSs) as open educational resources to support Inquiry-based Learning (IBL). To successfully exploit ILSs and implement IBL, proper support for orchestration is needed. Researchers have highlighted the complexity of orchestrating Technology Enhanced Learning (TEL) scenarios and the need for supporting participants in this endeavour. In...

This paper provides a systematic study of inductive inference of indexable concept classes in learning scenarios in which the learner is successful if its final hypothesis describes a finite variant of the target concept – henceforth called learning with anomalies. As usual, we distinguish between learning from only positive data and learning from positive