ICT-enhanced Teacher training for Lifelong Competence

In this paper we are giving an example of how Information and Communication Technologies (ICT) can enhance the process of Teacher training, and how this can be used for Lifelong Competence Development of teachers. We show how one particular methodology for teaching and training soft skills can be further enhanced by the use of ICT. We show how the use of Learning Design centred software platform for lifelong competence development can enhance the in-service training of teachers

ICT-enhanced Teacher training for Lifelong Competence Development

Paper accepted for publication for the Joint IFIP Conference: WG3.1 Secondary Education, WG3.5 Primary Education: Informatics, Mathematics, and ICT: a 'golden triangle' IMICT 2007, Boston, USAIn this paper we are giving an example of how Information and Communication Technologies (ICT) can enhance the process of Teacher training, and how this can be used for Lifelong Competence Development of teachers. We show how one particular methodology for teaching and training soft skills can be further enhanced by the use of ICT. We show how the use of Learning Design centred software platform for lifelong competence development can enhance the in-service training of teachers.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Development of an electronic course based on the i*teach methodology with the use of the ims learning design standard

The paper is a demonstration of the use of active methods of learning and IMS Learning design (IMS LD) specification for the development of portable and reusable electronic course. It demonstrates how Information and Communication Technologies (ICT) can enhance the process of teacher training, and how this can be used for Lifelong Competence Development of teachers. The paper shows how units of learning developed according to the IMS LD specification can integrate the contemporary active methods of learning in a web-based learning platform for project-based and problem-based team learning

Development of an electronic course based on the i*teach methodology with the use of the ims learning design standard

The paper is a demonstration of the use of active methods of learning and IMS Learning design (IMS LD) specification for the development of portable and reusable electronic course. It demonstrates how Information and Communication Technologies (ICT) can enhance the process of teacher training, and how this can be used for Lifelong Competence Development of teachers. The paper shows how units of learning developed according to the IMS LD specification can integrate the contemporary active methods of learning in a web-based learning platform for project-based and problem-based team learning

ICT-enhanced Teacher training for Lifelong Competence Development

In this paper we are giving an example of how Information and Communication Technologies (ICT) can enhance the process of Teacher training, and how this can be used for Lifelong Competence Development of teachers. We show how one particular methodology for teaching and training soft skills can be further enhanced by the use of ICT. We show how the use of Learning Design centred software platform for lifelong competence development can enhance the in-service training of teachers

ICT-enhanced Teacher training for Lifelong Competence Development

In this paper we are giving an example of how Information and Communication Technologies (ICT) can enhance the process of Teacher training, and how this can be used for Lifelong Competence Development of teachers. We show how one particular methodology for teaching and training soft skills can be further enhanced by the use of ICT. We show how the use of Learning Design centred software platform for lifelong competence development can enhance the in-service training of teachers

ICT-enhanced Teacher training for Lifelong Competence Development

In this paper we are giving an example of how Information and Communication Technologies (ICT) can enhance the process of Teacher training, and how this can be used for Lifelong Competence Development of teachers. We show how one particular methodology for teaching and training soft skills can be further enhanced by the use of ICT. We show how the use of Learning Design centred software platform for lifelong competence development can enhance the in-service training of teachers

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach