A dual process account of creative thinking

This article explicates the potential role played by type 1 thinking (automatic, fast) and type 2 thinking (effortful, logical) in creative thinking. The relevance of Evans's (2007) models of conflict of dual processes in thinking is discussed with regards to creative thinking. The role played by type 1 thinking and type 2 thinking during the different stages of creativity (problem finding and conceptualization, incubation, illumination, verification and dissemination) is discussed. It is proposed that although both types of thinking are active in creativity, the extent to which they are active and the nature of their contribution to creativity will vary between stages of the creative process. Directions for future research to test this proposal are outlined; differing methodologies and the investigation of different stages of creative thinking are discussed. © Taylor & Francis Group, LLC

Why do less creative student writers write longer texts?

When teaching academic writing in English the issue of creativity understood as experimenting, exploring and transforming language and ideas within the required writing task while keeping the audience and purpose in mind is rarely, if ever, considered. Still, there seems to be a relationship between the creative potential of a writer and the quality and quantity of their writing. The main aim of this article is to ponder upon the results of a small scale study on the relationship between students’ creativity as measured by KANH questionnaire and their lexical fluency in academic writing. The results seem to suggest that student writers who appear less creative write longer texts. The author discusses possible reasons for such a case, finding answers in research on creativity as such and creativity in writing specifically

Lessons from children in Māori medium for teachers: Encouraging greater efficiency when learning to multiply.

This research explores the responses of 44 Year 7-8 students from four Māori medium schools who were asked to solve a multiplication word problem. The findings show that there was a range of mental strategies displayed by the children, 29 of whom were able to solve the problem. However, data also indicates that 15 children were not able to either access the problem or utilise an appropriate strategy to solve it. This paper discusses the strategies shared by all of these children and suggests avenues to further support learners to become multiplicative thinkers

Підхід критичного мислення до формування інформаційно-аналітичних умінь

В статті розглядаються можливі шляхи поетапного формування інформаційно-аналітичних умінь майбутніх вчителів, які відповідають інноваційним освітнім тенденціям. Автор аналізує поняття інформаційно-аналітичних умінь та пропонує можливі шляхи їх поетапного формування у контексті підходу критичного мислення

Transhumanism and/as Whiteness

Transhumanism is interrogated from critical race theoretical and decolonial perspectives with a view to establishing its ‘algorithmic’ relationship to historical processes of race formation (or racialization) within Euro-American historical experience. Although the Transhumanist project is overdetermined vis-à-vis its raison-d’être, it is argued that a useful way of thinking about this project is in terms of its relationship to the shifting phenomenon of ‘whiteness’. It is suggested that Transhumanism constitutes a techno-scientific response to the phenomenon of ‘White Crisis’ at least partly prompted by ‘critical’ posthumanist contestation of Eurocentrically-universal humanism

Case board, traces, & chicanes: Diagrams for an archaeology of algorithmic prediction through critical design practice

This PhD thesis utilises diagrams as a language for research and design practice to critically investigate algorithmic prediction. As a tool for practice-based research, the language of diagrams is presented as a way to read algorithmic prediction as a set of intricate computational geometries, and to write it through critical practice immersed in the very materials in question: data and code. From a position rooted in graphic and interaction design, the research uses diagrams to gain purchase on algorithmic prediction, making it available for examination, experimentation, and critique. The project is framed by media archaeology, used here as a methodology through which both the technical and historical "depths" of algorithmic systems are excavated. My main research question asks: How can diagrams be used as a language to critically investigate algorithmic prediction through design practice? This thesis presents two secondary questions for critical examination, asking: Through which mechanisms does thinking/writing/designing in diagrammatic terms inform research and practice focused on algorithmic prediction? As algorithmic systems claim to produce objective knowledge, how can diagrams be used as instruments for speculative and/or conjectural knowledge production? I contextualise my research by establishing three registers of relations between diagrams and algorithmic prediction. These are identified as: Data Diagrams to describe the algorithmic forms and processes through which data are turned into predictions; Control Diagrams to afford critical perspectives on algorithmic prediction, framing the latter as an apparatus of prescription and control; and Speculative Diagrams to open up opportunities for reclaiming the generative potential of computation. These categories form the scaffolding for the three practice-oriented chapters where I evidence a range of meaningful ways to investigate algorithmic prediction through diagrams. This includes, the 'case board' where I unpack some of the historical genealogies of algorithmic prediction. A purpose-built graph application materialises broader reflections about how such genealogies might be conceptualised, and facilitates a visual and subjective mode of knowledge production. I then move to producing 'traces', namely probing the output of an algorithmic prediction system|in this case YouTube recommendations. Traces, and the purpose-built instruments used to visualise them, interrogate both the mechanisms of algorithmic capture and claims to make these mechanisms transparent through data visualisations. Finally, I produce algorithmic predictions and examine the diagrammatic "tricks," or 'chicanes', that this involves. I revisit a historical prototype for algorithmic prediction, the almanac publication, and use it to question the boundaries between data-science and divination. This is materialised through a new version of the almanac - an automated publication where algorithmic processes are used to produce divinatory predictions. My original contribution to knowledge is an approach to practice-based research which draws from media archaeology and focuses on diagrams to investigate algorithmic prediction through design practice. I demonstrate to researchers and practitioners with interests in algorithmic systems, prediction, and/or speculation, that diagrams can be used as a language to engage critically with these themes

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material