Teacher enactment of the Geospatial Inquiry cycle in classrooms following scaled up professional learning and development

The current study examined the effects of a nationally scaled up Professional Learning and Development (PLD) model on teachers’ classroom implementation of the Geospatial Inquiry instructional framework. Geospatial Inquiry is defined as: asking and answering a research question through the analysis and communication of data that is linked to a geographic location on, above, or near Earth. These data are often represented visually via maps and explored with geospatial technologies. It also examined the relationships between Geospatial Inquiry Teacher Workshop (GITW) implementation and teacher implementation of the Geospatial Inquiry Cycle. Situated cognition provided a theoretical framework for the design, development, and implementation of the GITWs and lessons. Surveys, technology assessments, lessons, and artifacts were analysed using a-priori coding, descriptive statistics, and a generalised linear modelling approach that included hierarchical analysis. Results indicated teachers implemented Geospatial Inquiry lessons with integrity to the principles of Geospatial Inquiry and utilised research-based pedagogical practices. Format of GITWs (e.g. face-to-face or blended) resulted in differences during teachers’ lesson implementation. In addition, whether GITWs were delivered by an individual facilitator or a team of facilitators impacted teachers’ lessons. The findings have several implications for the design and scaling of PLD

Chatbots as a novel access method for government open data

IIn this discussion paper, we propose to employ chatbots as a user-friendly interface for open data published by organizations, specifically focusing on public administrations. Open data are especially useful in e-Government initiatives but their exploitation is currently hampered to end users by the lack of user-friendly access methods. On the other hand, current UX in social networks have made people used to chatting. Building on cognitive technologies, we prototyped a chatbot on top of the OpenCantieri dataset published by the Italian Ministero delle Infrastrutture e Trasporti, and we argue that such a model can be extended as a generally available access method to open data

Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus

In Web search, entity-seeking queries often trigger a special Question Answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other extreme, a large corpus may provide broader coverage, but in an unstructured, unreliable form. We present AQQUCN, a QA system that gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of query syntax, between well-formed questions to short `telegraphic' keyword sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals from KGs and large corpora to directly rank KG entities, rather than commit to one semantic interpretation of the query. AQQUCN models the ideal interpretation as an unobservable or latent variable. Interpretations and candidate entity responses are scored as pairs, by combining signals from multiple convolutional networks that operate collectively on the query, KG and corpus. On four public query workloads, amounting to over 8,000 queries with diverse query syntax, we see 5--16% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems. Our system is also competitive at entity set retrieval, almost doubling F1 scores for challenging short queries.Comment: Accepted to Information Retrieval Journa