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Recent work has reintroduced ontology as a research topic in learning theories, as a mean to make explicit the differences and links between existing approaches to the design of learning programs. In the context of technology-supported information systems, ontologies can be represented in machine-understandable form to serve as a basis for automation and assessment. The notion of change is in some form part of any ontology of learning, but the interpretations attributed to the term differ between them both in scope and characterization. Since change is central to the evolving behaviour of learning organizations, it is worth the effort of specifying it, especially for the sake of objective measurement and automation. This paper describes ontological structures for generic constructivist and socio-cultural learning frameworks, stating the differences in their overall concepts of change, and their implications for practice and assessment. The ontological definitions provided are intended to motivate further work in more specific approaches for learning technology-supported experiences.

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Feedback Evaluation is a necessary part of any institute to maintain and monitor the academic quality of the system. Traditionally, a questionnaire based system is used to evaluate the performance of teachers of an institute. Here, we propose an automatic evaluation system based on sentiment analysis, which shall be more versatile and meaningful than existing system. In our proposed system, feedback is collected in the form of running text and sentiment analysis is performed to identify important aspects along with the orientations using supervised and semi supervised machine learning technique

During the last years, we faced a tremendous development of mobile sensing applications powered by innovative technologies related to ubiquitous and pervasive computing, volunteered geographic information, crowdsourcing and social networks. Nowadays, we are living in the next digitally enriched generation of social media in which communication and interaction for user-generated content is mainly focused on improving the sustainability of smart cities. Thus, urban computing is defined as the technology for acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, for tackling the major issues that cities face. Moreover, this technology is seeking ways to reduce inefficiencies and to be more agile in responding to citizens' needs in order to create smart cities. In this position paper, we address the content to describe the urban applications and the challenges for open research problems that are presented in the big cities.

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