Curriculum Development for Enhancing the Imagination in the Technology Commercialization Process

Huang, Chi Yo; Kao, Yu Sheng; Lu, Hsueh Hsin; Wu, Ming Jenn

doi:10.12973/eurasia.2017.01062a

Cited by 5 publications

(3 citation statements)

References 92 publications

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“…Chen et al [68] configured the knowledge diffusion policy portfolio of higher education institutes by MCDM methods. Huang et al [69] developed a curriculum to enhance the imagination in the technology commercialization process. Yang et al [70] defined an HMCDM framework for deriving key success factors of public-private partnerships.…”

Section: Analytic Framework and Methods For Tamentioning

confidence: 99%

Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods

et al. 2018

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Air pollution poses serious problems as global industrialization continues to thrive. Since air pollution has grave impacts on human health, industry experts are starting to fathom how to integrate particulate matter (PM) sensors into portable devices; however, traditional micro-electro-mechanical systems (MEMS) gas sensors are too large. To overcome this challenge, experts from industry and academia have recently begun to investigate replacing the traditional etching techniques used on MEMS with semiconductor-based manufacturing processes and materials, such as gallium nitride (GaN), gallium arsenide (GaAs), and silicon. However, studies showing how to systematically evaluate and select suitable materials are rare in the literature. Therefore, this study aims to propose an analytic framework based on multiple criteria decision making (MCDM) to evaluate and select the most suitable materials for fabricating PM sensors. An empirical study based on recent research was conducted to demonstrate the feasibility of our analytic framework. The results provide an invaluable future reference for research institutes and providers.

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Section: Analytic Framework and Methods For Tamentioning

confidence: 99%

Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods

et al. 2018

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“…Imagination analysis has become popular in recent decades (Safar and Alkhezzi, 2016;Huang et al, 2017) because imagination is a major driving force of human progress in stimulating creativity and innovation, and a critical factor promoted by education researchers (Eisner, 2001). For boosting imagination, the primary task is comprehending imagination cognition by measuring imagination with an imagination scale.…”

Section: Introductionmentioning

confidence: 99%

Using Localized Features for Analyzing College Students’ Imagination

Liu

Liao

2019

EURASIA J MATH SCI T

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The analysis of imagination has become popular in recent years because imagination is one of the key components of creativity and innovation. For extracting students' implicit degrees and thought processes of imagination, we use frequent pattern mining and association rule extraction to localize the features and explain the deep meanings of imagination in the study. By our observations, these two methods may sometimes explore meaningless frequent patterns and rules on a global sparse dataset. In order to eliminate such phenomena when mining with these two methods, we use a localized feature approach called forecast with clustering and integration (FCI) to improve the drawbacks of two methods on a sparse dataset. The approach consists of two strategies. One is clustering and the other is the prediction based on integration from (1) frequent patterns, (2) association rule pruning with correlation, and (3) forecast with linear regression. The former strategy can reduce the number of samples and highlight the features of imagination and the latter strategy can prune meaningless information and predict the trend of scores from imagination input data. Experimental results show both proposed approaches can localize special features, thereby providing supervisors with meaningful information about students' degrees and thought processes of imagination. Localized Features for Analyzing Imagination 2 / 16 relative supports, we concentrate on localized feature strategies to evaluate frequent items and, thereby, infer the kernel association rules for enhancement.The first localized feature strategy is top-K clustering. By top-K clustering, the data can be separated into several groups, and the representative features can be explored from the largest K groups individually. Generally, clustering, an unsupervised learning, can aggregate similar data into several groups and use in constructing the data model from a dataset with unknown categories. The clustered groups can be expressed as the localized features of all input data because clustering can separate global data into several local data. To localize students' imagination, in this study, we use the density-based clustering scheme, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), for aggregating input data. Unlike centroid-based clustering approaches such as kmeans approaches, DBSCAN aggregates input data without restricting circle-shaped clusters because it considers the relationships such as Euclidean distance or cosine similarity between two data points. This feature of relationship results in grouping the similar input data into the same clusters. By contrast, the results obtained using a centroid-based clustering approach may be different due to the locations of the initial centroids. Hence, clustering using DBSCAN can yield more stable results than that with any centroid-based clustering approach. After clustering, researchers can choose the largest K clusters for analysis. These K clusters contain the features that most students have. Therefore, ...

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“…Some researchers applied SCAMPER to creative training and enhancing creative thinking skills (Çelikler & Harman, 2015;Ritter & Mostert, 2017;Suciu, 2014). SCAMPER has also contributed to developing a curriculum for improving the imagination in the technology commercialization process (Huang, Kao, Lu, & Wu, 2017). In the engineering design field, SCAMPER is employed to measure the influence of the design outcome when applying different design methods (Chulvi, González-Cruz, Mulet, & Aguilar-Zambrano, 2013;Chulvi, Mulet, Chakrabarti, López-Mesa, & González-Cruz, 2012).…”

Section: Introductionmentioning

confidence: 99%

New SCAMPERS

Tsai

2019

Int J Innov Educ Res

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It has long been known that the SCAMPER checklist is a useful tool for developing creative thinking. However, the meanings of some words might change based on the different interpretations of the users. Here we aimed to reorganize this model to avoid placing different thinking directions into the same category. We designed a new checklist, including Substitute, Combine, Adjust, Modify, Permute, Eliminate, Repurpose, and Size, and renamed it NEW SCAMPERS. Using a questionnaire, we tested the participants’ understanding of the checklist’s meanings and its application. We recruited 194 students from three campuses and analyzed 191 completed answers. We found that students can comprehend the new classified category, and most do well on the application inspection. Thus, the new checklist was proven to be an appropriate technique for group creative thinking.

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Curriculum Development for Enhancing the Imagination in the Technology Commercialization Process

Cited by 5 publications

References 92 publications

Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods

Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods

Using Localized Features for Analyzing College Students’ Imagination

New SCAMPERS

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