Are You Wanted for Poor Listening Habits?

In order to begin a semester or unit on effective listening with some basic theory and knowledge and to serve as an icebreaker, students are asked to design and share a “Wanted Poster” describing their poor listening habits. The significance of this assignment was guided by the ubiquitous nature of listening. Research verifies listening as the most utilized form of communication

Effective Listening Project: A Constructivist Activity

Constructivist learning allows learners to synthesize and understand new ideas and concepts based on their own current and past knowledge and experiences. This paper describes the constructivist philosophy of learning. The constructivist teaching and learning model is applied to a unit used in an effective listening course or a class with a unit in listening. Students construct a listening campaign demonstrating the importance of effective listening for a target audience

Developing a Senior Capstone and Portfolio Course

Our purpose in this essay is to explain how the Speech Communication Department at Minnesota State University, Mankato developed a senior capstone and portfolio course. We describe how this course helped the department improve its curriculum and teaching, and helped its students enhance their learning of the discipline

Micromechanics analysis of fibre reinforced cement composites

Fibre Science and Technology20299-12

An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit

© 2019 Elsevier Ltd Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures