W tej krótkiej pracy omawiamy doświadczenia ze zdalnego nauczania studentów matematyki oraz studentów kierunku inżynierskiego z użyciem metody odwróconej klasy oraz z wykorzystaniem technologii do aktywizacji i zwiększenia zaangażowania studentów w proces nauczania.
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In this note we want to share our experiences gathered during some courses in mathematics taught completely online due to COVID 19 pandemic restrictions. Our students were mathematics majors and engineering majors. We discuss briefly similarities and differences in working with both groups of students. Somewhat surprisingly similarities are predominating. In the courses we applied the method of the flipped classroom. This method is relatively well known and spread to some extend in middle and high schools but it is rarely applied in university courses. We found that the methods works very effectively in courses taught completely online allowing for diversion from a rather dull routine of a traditional lecture implemented in the online setting and providing place for engaging students in the learning process. The method was complemented by some elements from the peer instruction approach. Most notably, we used technology to implement good questions in the spirit developed by the second author in his PhD thesis, stimulating thus discussions and increasing the attractiveness of our classes.
The aim of this paper is to present the Erotetic Reasoning Corpus (ERC) which constitutes a data set for research on natural question processing. We describe the theoretical background, linguistic data and tags used for the annotation process. We also discuss the potential areas in which the ERC can be exploited.
In this article we consider the phenomenon of answering a query with a query. Although such answers are common, no large-scale, corpusbased characterization exists, with the exception of clarification requests. After briefly reviewing different theoretical approaches on this subject, we present a corpus study of query responses in the British National Corpus and develop a taxonomy for query responses. We identify a variety of response categories that have not been formalized in previous dialogue work, particularly those relevant to adversarial interaction. We show that different response categories have significantly different rates of subsequent answer provision. We provide a formal analysis of the response categories within the framework of KoS.
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