Latency as a predictor of originality in divergent thinking

Acar, Selçuk; Alabbasi, Ahmed M. Abdulla; Runco, Mark A.; Beketayev, Kenes

doi:10.1016/j.tsc.2019.100574

Cited by 37 publications

(39 citation statements)

References 61 publications

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“…The lack of students' skills in this indicator of originality is possible because they need a lot of time in generating original ideas. This is in line with the research conducted by Acar, Alabbasi, Runco, & Beketayev, (2019) that students need and use more time to be able to produce original ideas.…”

Section: Discussion Conclusionsupporting

confidence: 88%

ERCoRe Learning Model to Improve Creative-Thinking Skills of Preservice Biology Teachers

Nur

Zubaidah

Mahanal

et al. 2020

Journal for the Education of Gifted Young Scientists

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It is a challenge for higher education in the 21st century to prepare the students with various skills to master, including creative-thinking skills. The present study aims to test the effect of Eliciting, Restructuring, Confirming, Reflecting (ERCoRe) learning model in improving the creative-thinking skills of preservice biology teachers. This quasi-experimental research employed a pretest-posttest control-group design. The samples came from a university in West Sulawesi Province, which was located in Indonesia. They were enrolled in the even semester of the 2018/2019 academic year with the Animal Structure course. The subjects of this research were 84 preservice teachers in 4 classes, which were divided into two experimental classes and two control classes. The number of preservice teachers in the experimental class was 44 and in the control class 40. The experimental class was taught with the ERCoRe learning model and the control class with the lecture and question and answer methods. The instrument of the research consisted of an essay test of creative-thinking skills totaling 13 items. The research data were analyzed using one-way ANCOVA. It was found that the creative-thinking skills of the preservice teachers generated by the ERCoRe learning model were higher than those generated by the conventional model. The use of the ERCoRe learning model influences and can improve the creative-thinking skills of preservice biology teachers at the university in West Sulawesi. The authors recommend that the ERCoRe learning model be used for other subjects as considered to have the potential in developing 21st century skills, especially creative thinking.

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Section: Discussion Conclusionsupporting

confidence: 88%

ERCoRe Learning Model to Improve Creative-Thinking Skills of Preservice Biology Teachers

Nur

Zubaidah

Mahanal

et al. 2020

Journal for the Education of Gifted Young Scientists

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show abstract

“…We again tested whether semantic distance values correlated with human creativity ratings and other measures associated with creativity (i.e., openness and metaphor production); we also reassessed the relation between semantic AUT semantic distance and fluid intelligence. Furthermore, we tested an established experimental effect in the divergent thinking literature known as the serial order effect, the tendency for ideas to be rated as more original over time (Acar, Abdulla Alabbasi, Runco, & Beketayev, 2019;Christensen, Guilford, & Wilson, 1957;Hass & Beaty, 2018). Although prior work has reported serial order effects on the AUT with LSA (Hass, 2017b), we sought to replicate this effect and extend it by using a broader range of compositional semantic models.…”

Section: Studymentioning

confidence: 99%

Automating creativity assessment with SemDis: An open platform for computing semantic distance

2020

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Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants' responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitationslabor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Studies 1-3) and novelty/creativity ratings from two word association tasks (Studies 4-5). We find that a latent semantic distance factor-comprised of the common variance from five semantic models-reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation (https://osf.io/gz4fc/).

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“…We again tested whether semantic distance values correlated with human creativity ratings and other measures associated with creativity (i.e., openness and metaphor production); we also reassessed the relation between semantic AUT semantic distance and fluid intelligence. Furthermore, we tested an established experimental effect in the divergent thinking literature known as the serial order effect, the tendency for ideas to be rated as more original over time (Acar, Alabbasi, Runco, & Beketayev, 2019;Christensen, Guilford, & Wilson, 1957;Hass & Beaty, 2018). Although prior work has reported serial order effects on the AUT with LSA (Hass, 2017b), we sought to replicate this effect and extend it by using a broader range of compositional semantic models.…”

Section: Studymentioning

confidence: 99%

Automating Creativity Assessment with SemDis: An Open Platform for Computing Semantic Distance

Beaty¹,

Johnson²

2020

Preprint

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Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitations—labor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)—raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Study 1-3) and novelty/creativity ratings from two word association tasks (Study 4-5). We find that a latent semantic distance factor—comprised of the common variance from five semantic models—reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation (semdis.wlu.psu.edu).

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Latency as a predictor of originality in divergent thinking

Cited by 37 publications

References 61 publications

ERCoRe Learning Model to Improve Creative-Thinking Skills of Preservice Biology Teachers

ERCoRe Learning Model to Improve Creative-Thinking Skills of Preservice Biology Teachers

Automating creativity assessment with SemDis: An open platform for computing semantic distance

Automating Creativity Assessment with SemDis: An Open Platform for Computing Semantic Distance

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