Learning Concepts and Language for a Baby Designer

Dabbeeru, Madan Mohan; Mukerjee, Amitabha

doi:10.1007/978-94-007-0510-4_24

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Certainly, in actual problemsolving conversations, there can be numerous other nodes. For example, polysemy appears during designers' verbalizations, and it shows the multiplicity of significations that an object can have (Dabbeeru & Mukerjee 2011). Polysemy has been identified as an essential manifestation of the richness, flexibility and adaptability in meaning potential in thinking (Fauconnier & Turner 2011).…”

Section: Ai-based Creativity Supportmentioning

confidence: 99%

Waterfalls as a form of AI-based feedback for creativity support

Georgiev¹,

Barhoush²

2022

Making Data

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Section: Ai-based Creativity Supportmentioning

confidence: 99%

Waterfalls as a form of AI-based feedback for creativity support

Georgiev¹,

Barhoush²

2022

Making Data

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“…Hence, polysemy is identified as manifestation of the flexibility of meaning potential in human thinking (Fauconnier & Turner, 2003;Georgiev & Taura, 2014). This measure can reveal the multiplicity of significations in a design object (Dabbeeru & Mukerjee, 2011). Moreover, it can be a source of creative inspiration, allowing for the exploration of different meanings of related concepts (Zhang & Saunders, 2014).…”

Section: Polysemymentioning

confidence: 99%

Design creativity and the semantic analysis of conversations in the design studio

Casakin

Georgiev

2020

International Journal of Design Creativity and Innovation

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Design creativity and the semantic analysis of conversations in the design studioThe analysis of conversations during design activity can facilitate deeper insights into design thinking and its relation to creativity. A semantic analysis approach was employed to explore the semantic content of communication and information exchange between students and instructors. The goal was to examine design conversations in terms of Abstraction, Polysemy, Information Content and Semantic Similarity measures, and analyse their relation to the creativity of final solutions. These design outcomes were assessed according to their Originality, Usability, Feasibility, Overall Value, and Overall Creativity. Consequently, 35 design conversations from the 10th Design Thinking Research Symposium (DTRS10) dataset were analysed. The main results showed that Information Content and Semantic Similarity predicted Originality, and Information Content alone predicted Overall Creativity. Likewise, Abstraction predicted Feasibility, while Semantic Similarity, Information Content, and Polysemy predicted Overall Value. In context of instructors, Semantic Similarity predicted Usability, and Polysemy predicted Feasibility. For students, Semantic Similarity predicted Overall Value. On the whole, Semantic Similarity and Information Contentwere the most prolific measures, and therefore could be considered for promoting creativity in the design studio. The implications of using support tools such as automated systems are also discussed.

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“…[46] identified polysemy as a characteristic feature of successful ideas considered in the final solution in design conversations. Polysemy demonstrates the multiplicity of significations in a designed object [60].…”

Section: Polysemy In Design Thinking and Problem Solvingmentioning

confidence: 99%

Shape of a crack growth front under fatigue in the range of 106 - 107cycles

Georgiev

2020

AJTUV

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The paper explores the shape of a fatigue crack initiation in the interval of 106-107cycles of duplex stainless steel, commercially designated as SAF 2507. Particular emphasis is placed upon the development of the crack’s growth front and its subsequent expansion in three directions x, y, z. Created, accordingly, on the basis of the experimentally obtained results, is a 3D computer model to help provide a further prediction for the physical endurance of similar materials. The growth of a fatigue crack is modeled by using The SolidWorks and AutoCAD software tools for constructing the model of fatigue crack growth.

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Learning Concepts and Language for a Baby Designer

Cited by 6 publications

References 28 publications

Waterfalls as a form of AI-based feedback for creativity support

Waterfalls as a form of AI-based feedback for creativity support

Design creativity and the semantic analysis of conversations in the design studio

Shape of a crack growth front under fatigue in the range of 106 - 107cycles

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