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Purpose This paper aims to identify the learning benefits and the challenges of Web 2.0 educational activities when applied in typical learning settings and as perceived by pioneer educators with extensive Web 2.0 experience. Design/methodology/approach The testimonies of 26 Greek primary and secondary education teachers were collected. All teachers had an extensive involvement with Web 2.0 in their classrooms. The interviews were semi-structured and focused on personal case studies, students' views of Web 2.0, problems and prerequisites and educational opportunities of Web 2.0. Findings The teachers indicated that Web 2.0 learning activities promote the learner to the center of the learning process, open the schools’ doors to society and help students learn how to cooperate and create digital content, while enabling them to reflect more on their thoughts, extend the time-space of the educational dialogue and promote trust between students and teachers. The participants had also to cope with challenges which concerned their colleagues’ attitude and the educational environment, the parents’ attitude, the amount of time and effort required, the unpredictable character of the activities, the limitations imposed by the curriculum, the overestimation of students’ skills and the lack of training opportunities. Practical implications The findings can be transformed to a set of critical guidelines for policy makers and for educating the educators. Originality/value The set of findings are derived from teachers with a long-term, intensive, daily practice with Web 2.0 and offer an holistic systematic view of problems and opportunities.

ABSTRACT:The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations) consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.