Applying Argumentation Analysis to Assess the Quality of University Oceanography Students' Scientific Writing

This article describes the methods and results of an assessment of students' scientific writing. The study was conducted in an introductory oceanography course in a large public university that used an interactive CD-ROM entitled, "Our Dynamic Planet." The CD provided students with geological data, which they used to build written arguments regarding plate tectonics. Twenty-four student papers from this course were analyzed for quality of written arguments by using both a grading rubric and an argumentation analysis model. Three implications were drawn from these initial studies. First, there is a clear need to help students understand how to use data representations as evidence for more theoretical arguments. Second, student writers need experiences receiving critiques of their own writing and analyzing others' scientific arguments. Third, the actual grading is dependent upon the socialization of the graders themselves (in this case, graduate students). Educational levels: Graduate or professional

Anticipation and Risk – From the inverse problem to reverse computation

Abstract. Risk assessment is relevant only if it has predictive relevance. In this sense, the anticipatory perspective has yet to contribute to more adequate predictions. For purely physics-based phenomena, predictions are as good as the science describing such phenomena. For the dynamics of the living, the physics of the matter making up the living is only a partial description of their change over time. The space of possibilities is the missing component, complementary to physics and its associated predictions based on probabilistic methods. The inverse modeling problem, and moreover the reverse computation model guide anticipatory-based predictive methodologies. An experimental setting for the quantification of anticipation is advanced and structural measurement is suggested as a possible mathematics for anticipation-based risk assessment

Using Twitter to learn about the autism community

Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals' carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD -- their behaviour, concerns, needs etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.Comment: Social Network Analysis and Mining, 201