Foundations of Creativity Assessment

The article explores the specificity of linguistic creativity in the discourse of children's English-language adventure fiction of the 1950s. The aim of the research is to develop the parametrization and vector-space method of discourse and text linguistic creativity assessment to evaluate the linguistic creativity potential of individual texts displaying similar discourse features. To serve as the research data three discourse fragments were selected, which represent three basic narrative types, Orientation, Complicating Actions, Evaluation and Resolution. To achieve the aim, the author applies the procedure of parametrization analysis followed by general and analytic statistics analysis and vector-space modelling. With the system of 52 parameters featuring linguistic creativity in phonology, word-formation, morphology, lexicology and phraseology, syntax, and graphics, the author manually annotates and processes the discourse fragments of similar size exemplifying three narrative types of adventure fiction literature, with the total sample size of 55,000 characters. General statistics analysis allowed revealing the absolute and relative parameter values in three discourse fragments and defining the relative parametric activity of single parameters and parameter levels. Analysis of variance helped define the correlation indices of parameter paired combinations, which resulted in detecting significant binary parameter groups . Individual parameter values and their binary groups served to construe the vector-space models of discourse and text linguistic creativity for the discourse narrative types under consideration. Thus, the author obtained an efficient instrument for discourse linguistic creativity evaluation and, furthermore, for assessing the potential of each individual text in terms of displaying stronger or weaker correlation with the vector coordinates of the discourse linguistic creativity vector-space model. With the frequency and variance analysis, the author disclosed two types of discourse linguistic creativity performance techniques, that is the individual parameter activation and the parameter synchronization. Both must be considered when the decision on linguistic creativity assessment in a concrete text is made. The resulting model shows that the parameter values of linguistic creativity in individual texts can manifest themselves in appearing both higher and lower than the reference parameter values of discourse creativity, which can contribute to disclosing new directions in creativity processing and understanding.

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Analytic Creativity Assessment Scale for Digital Game Story Design

PsycTESTS Dataset ◽

10.1037/t50331-000 ◽

2015 ◽

Author(s):

Tsung-Yen Chuang ◽

Yun-Hsuan Huang

Keyword(s):

Assessment Scale ◽

Digital Game ◽

Creativity Assessment

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Automated Scoring of Figural Creativity using a Convolutional Neural Network

10.31234/osf.io/8qe7y ◽

2021 ◽

Author(s):

David H Cropley ◽

Rebecca L Marrone

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Creative Thinking ◽

Divergent Thinking ◽

Reliability And Validity ◽

Semantic Distance ◽

Self Report ◽

Creativity Assessment ◽

Creativity Research ◽

Creative Self

One of the abiding challenges in creativity research is assessment. Objectively scored tests of creativity such as the Torrance Tests of Creativity (TTCT) and the Test of Creative Thinking - Drawing Production (TCT-DP) offer high levels of reliability and validity but are slow and expensive to administer and score. As a result, many creativity researchers default to simpler and faster self-report measures of creativity and related constructs (e.g., creative self-efficacy, openness). Recent research, however, has begun to explore the use of computational approaches to address these limitations. Examples include the Divergent Association Task (DAT) that uses computational methods to rapidly assess the semantic distance of words, as a proxy for divergent thinking. To date, however, no research appears to have emerged that uses methods drawn from the field of artificial intelligence to assess existing objective, figural (i.e., drawing) tests of creativity. This paper describes the application of machine learning, in the form of a convolutional neural network, to the assessment of a figural creativity test – the TCT-DP. The approach shows excellent accuracy and speed, eliminating traditional barriers to the use of these objective, figural creativity tests and opening new avenues for automated creativity assessment.

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Creativity Assessment in Higher Education

Handbook of Research on Assessment Technologies, Methods, and Applications in Higher Education ◽

10.4018/978-1-60566-667-9.ch005 ◽

2011 ◽

pp. 78-96 ◽

Cited By ~ 1

Author(s):

Christine Charyton ◽

Zorana Ivcevic ◽

Jonathan A. Plucker ◽

James C. Kaufman

Keyword(s):

Higher Education ◽

Divergent Thinking ◽

Educational Programs ◽

Comprehensive Overview ◽

Creativity Assessment ◽

Individualize Instruction ◽

Historical View ◽

Creative Person ◽

Measurement And Evaluation ◽

Process Product

This chapter discusses creativity assessment as a means for evaluating skills required in higher education. Creativity is assessed in the context of the creative person, process, product and press or environment. Creativity is also measured differently in various domains, which we illustrate using divergent thinking tests. A historical view of creativity assessment is addressed with a substantive approach to understanding the construct of creativity, its measurement and evaluation, and the broader implications for use in higher education settings. The authors provide a comprehensive overview of the different ways creativity is assessed and hope to inform researchers concerned about finding ways to better individualize instruction and to evaluate the effectiveness of educational programs.

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