Oral Communication Competency Across the Virginia Community College System: A Faculty-Designed Assessment

A full-scale oral communication assessment was conducted by the Virginia Community College System (VCCS) during the academic year 2013-14. Key components of this assessment included faculty involvement at all stages of assessment and collaboration with assessment coordinators and lead staff personnel. The VCCS Office of Institutional Research and Effectiveness partnered with CST faculty experts to design the assessment. The Competent Speaker Speech Evaluation Form, a standardized and tested instrument used to assess public speaking competency in higher education, was used to evaluate student speeches. Designed by the National Communication Association, this instrument identifies eight competencies for measurement. The results of this assessment indicate that over 70% of students assessed who graduated with an associate degree from a Virginia community college have proficient oral communication skills. This project demonstrates that assessment can be a shared endeavor in which results can be understood and used to inform curricular planning by all major stakeholders

Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis

Practical demands and academic challenges have both contributed to making sentiment analysis a thriving area of research. Given that a great deal of sentiment analysis work is performed on social media communications, where text frequently ignores the rules of grammar and spelling, pre-processing techniques are required to clean the data. Pre-processing is also required to normalise the text before undertaking the analysis, as social media is inundated with abbreviations, emoticons, emojis, truncated sentences, and slang. While pre-processing has been widely discussed in the literature, and it is considered indispensable, recommendations for best practice have not been conclusive. Thus, we have reviewed the available research on the subject and evaluated various combinations of pre-processing components quantitatively. We have focused on the case of Twitter sentiment analysis, as Twitter has proved to be an important source of publicly accessible data. We have also assessed the effectiveness of different combinations of pre-processing components for the overall accuracy of a couple of off-the-shelf tools and one algorithm implemented by us. Our results confirm that the order of the pre-processing components matters and significantly improves the performance of naïve Bayes classifiers. We also confirm that lemmatisation is useful for enhancing the performance of an index, but it does not notably improve the quality of sentiment analysis

The nature of twitter trending topics: Analysing intrinsic factors associated with the twitter ecosystem

© 2018 The authors and IOS Press. All rights reserved. We are inundated with data—companies such as Twitter deal with petabytes of information on a daily basis. However, some users, especially the new ones, often find it difficult to cope with the overwhelming and disorganised deluge of information. Scientists have already worked out ways to identify Twitter trending topics, as a means to index information and make sense of it. However, we know little about the impact on trending topics of various intrinsic factors associated with the Twitter ecosystem. For example, anecdotal evidence suggests that trending topics are characterised by highly polarised tweets, or that large audiences typically host the emergence of trending topics. However, no study has yet addressed these issues formally. To remedy this situation, we have launched an investigation on the nature of trending topics. Our initial observations indicate that there is a correlation between strong sentiment polarity and the emergence of trending topics—we can also confirm that the strength of the polarity drops as the trending topics fade away. Conversely, our experiments highlight that there is no correlation between the size of a Twitter audience and the rise of trending topics

Analysing the Sentiment Expressed by Political Audiences on Twitter: The Case of the 2017 UK General Election

© 2017 IEEE. A significant amount of research on the intersection of sentiment analysis and social media platforms has been published in the past few years. While previous studies have focused on methods to identify the polarity of online posts, little has been done in terms of using the impact of such posts to enhance the discovery and description of trends in real time. Here, we present a tool for the retrieval and analysis of microblogging posts in real time. We have gathered a large sample of tweets related to the 2017 UK General Election. We introduce a novel classification of the polarity of sentiments, considering the correlation between words, events and sentiments

Fish oil supplementation reverses the effect of cholesterol on apoptotic gene expression in smooth muscle cells

<p>Abstract</p> <p>Background</p> <p>Nutritional control of gene regulation guides the transformation of smooth muscle cells (SMC) into foam cells in atherosclerosis. Oxidative stress has been reported in areas of lipid accumulation, activating proliferation genes. Suppression of oxidative stress by antioxidant administration reduces this activation and the progression of lesions. We hypothesized that fish oil consumption may protect against atherosclerotic vascular disease. The study objective was to determine the effects of dietary cholesterol and fish-oil intake on the apoptotic pathways induced by 25-hydroxycholesterol (25-HC) in SMC cultures.</p> <p>Methods</p> <p>An <it>in vivo/in vitro </it>cell model was used, culturing SMC isolated from chicks exposed to an atherogenic cholesterol-rich diet with 5% of cholesterol (SMC-Ch) alone or followed by an anti-atherogenic fish oil-rich diet with 10% of menhaden oil (SMC-Ch-FO) and from chicks on standard diet (SMC-C). Cells were exposed to 25-HC, studying apoptosis levels by flow cytometry (Annexin V) and expressions of caspase-3, c-myc, and p53 genes by quantitative real-time reverse transcriptase-polymerase chain reaction. Results: Exposure to 25-HC produced apoptosis in all three SMC cultures, which was mediated by increases in caspase-3, c-myc, and p53 gene expression. Changes were more marked in SMC-Ch than in SMC-C, indicating that dietary cholesterol makes SMC more susceptible to 25-HC-mediated apoptosis. Expression of p53 gene was elevated in SMC-Ch-FO. This supports the proposition that endogenous levels of p53 protect SMC against apoptosis and possibly against the development of atherosclerosis. Fish oil attenuated the increase in c-myc levels observed in SMC-C and SMC-Ch, possibly through its influence on the expression of antioxidant genes.</p> <p>Conclusion</p> <p>Replacement of a cholesterol-rich diet with a fish oil-rich diet produces some reversal of the cholesterol-induced changes, increasing the resistance of SMC to apoptosis.</p

Stereoselective titanium-mediated aldol reactions of a chiral lactate-derived ethyl ketone with ketones

Aldol reactions of titanium enolates of lactate-derived ethyl ketone 1 with other ketones proceed in a very efficient and stereocontrolled manner provided that a further equivalent of TiCl4 is added to the reacting mixture. The scope of these reactions encompasses simple ketones such as acetone or cyclohexanone as well as other ketones that contain potential chelating groups such as pyruvate esters or α- and β-hydroxy ketones

A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process

Despite the unabated growth of algorithmic decision-making in organizations, there is a growing consensus that numerous situations will continue to require humans in the loop. However, the blending of a formal machine and bounded human rationality also amplifies the risk of what is known as local rationality. Therefore, it is crucial, especially in a data-abundant environment that characterizes algorithmic decision-making, to devise means to assess performance holistically. In this paper, we propose a simulation-based model to address the current lack of research on quantifying algorithmic interventions in a broader organizational context. Our approach allows the combining of causal modeling and data science algorithms to represent decision settings involving a mix of machine and human rationality to measure performance. As a testbed, we consider the case of a fictitious company trying to improve its forecasting process with the help of a machine learning approach. The example demonstrates that a myopic assessment obscures problems that only a broader framing reveals. It highlights the value of a systems view since the effects of the interplay between human and algorithmic decisions can be largely unintuitive. Such a simulation-based approach can be an effective tool in efforts to delineate roles for humans and algorithms in hybrid contexts

A subword-based deep learning approach for sentiment analysis of political tweets

© 2018 IEEE. The successful use of online material in political campaigns over the past two decades has motivated the inclusion of social media platforms - such as Twitter - as an integral part of the political apparatus. Political analysts are increasingly turning to Twitter as an indicator of public opinion. We are interested in learning how positive and negative opinions propagate through Twitter and how important events influence public opinion. In this paper, we present a neural network-based approach to analyse the sentiment expressed on political tweets. First, our approach represents the text by dense vectors comprising subword information to better detect word similarities by exploiting both morphology and semantics. Then, a Convolutional Neural Network is trained to learn how to classify tweets depending on sentiment, based on an available labelled dataset. Finally, the model is applied to perform the sentiment analysis of a collection of tweets retrieved during the days prior to the latest UK General Election. Results are promising and show that the neural network approach represents an improvement over lexicon-based approaches for positive/negative sentence classification

Substrate-controlled Michael additions of titanium enolates from chiral α-benzyloxy ketones to conjugated nitroalkenes

Lewis acid-mediated substrate-controlled reactions of the titanium(IV) enolates of chiral a-benzyloxy ketones with conjugated nitroalkenes give the 2,4-anti-4,5-syn Michael adducts in good yields and diastereomeric ratios. The supplementary Lewis acid plays a key role in the outcome of these transformations, probably as a consequence of the formation of bimetallic enolates that increase the reactivity of the enolate and direct the approach of the nitroalkene. Importantly, the most appropriate Lewis acid depends on the electrophilic partner: TiCl4 is the most suitable Lewis acid for b-aryl nitroalkenes while the best results for b-alkyl nitroalkenes are obtained with SnCl4. Finally, the nitro group of the resultant compounds can be converted into the corresponding amino, oxime, and nitrile groups under mild conditions, which permits the synthesis of a variety of enantiomerically pure derivatives with excellent yields