Analysis of the Scientific Imagination Process

Ho, Hsiao-Chi; Cheng, Ying

doi:10.1016/j.tsc.2013.04.003

Cited by 40 publications

(48 citation statements)

References 28 publications

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“…Nowadays, there exist popular imagination models for training and defining students' imagination such as IDeAL training models (Wang et al, 2010), IDV (Ho et al, 2013), and IFFO (Lin & Tsau, 2012). In the first model, the researchers considered the imagination can be formed in four components, including initiation, development, alternatives and links.…”

Section: Imaginationmentioning

confidence: 99%

“…The key concept of the stage focuses on the association which can be briefly divided in two parts. One part is related to how to envision relationships among thoughts; that is, students should link related thoughts, extend the concepts behind thoughts, or identify the contradictions between thoughts and reorganize them accordingly (Ho et al, 2013); the other part is the quality which involved in conferring new meanings on a thought within an associative network to transform it into a novel thought. As for the virtual implementation stage, the key concept focuses on refining particular thoughts.…”

Section: Imaginationmentioning

confidence: 99%

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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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Section: Imaginationmentioning

confidence: 99%

Section: Imaginationmentioning

confidence: 99%

Using Localized Features for Analyzing College Students’ Imagination

Liu

Liao

2019

EURASIA J MATH SCI T

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“…Einstein and Infeld (1936): "Physical concepts are free creations of the human mind, and are not, however it may seem, uniquely determined by the external world". As mere anecdotes, Einstein imagined himself flying at light speed and visualizing the objects that he could see, and Kekulé literally imagined the shape of benzene prior to experimentation (Ho et al, 2013;Wang et al, 2015). But these anecdotes can only illustrate the debate that took place in the field of the theory of knowledge.…”

Section: Imagination In the Theory Of Knowledgementioning

confidence: 99%

University Students Developing Imaginative Problem Solving Skills — The Case of Food Engineering

Torrecilla

Aguado

Tijero

et al. 2018

Proceedings of the 4th International Conference on Higher Education Advances (HEAd'18)

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“…In the process of scientific imagination proposed by Ho, Wang & Cheng (2013), the final "virtual hands-on stage" specifies the results of refining the use of imagination by creating a prototype of works with novel ideas. To apply the mental operation involving conceptual, organized and structured ideas, students were guided to describe their specific ideas or make drawings and were encouraged to translate ideas into concrete actions.…”

Section: Theory About and Research Into Imaginative Educationmentioning

confidence: 99%

“…In addition, the transformation of imagination proposed by Liang, Hsu and Ling (2014) and the virtual handson stage put forward by Ho, Wang, Cheng (2013) both mentioned the meanings and actions of visualizing the abstract concept of imagination. Furthermore, the iSTEM learning model emphasizes the process of students' hands-on practice, so the researchers added the sixth stage, "presentation".…”

Section: Task Stage Of the Istem Learning Modelmentioning

confidence: 99%

Construction and Development of iSTEM Learning Model

Tsai

Chung

Lou

2017

EURASIA J MATH SCI T

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This study integrates STEM education and imaginative education with project-based learning, and develops a set of "iSTEM (imagination, science, technology, engineering, mathematics) learning" methods in order to construct iSTEM ability indexes for vocational high school students and to explore the application and effectiveness of the learning model. This study employed the fuzzy Delphi method, a questionnaire survey, content analysis and focus group interviews and included 12 experts and 39 vocational high school students as the subjects. The important results obtained in this study are as follows: 20 iSTEM imagination ability indexes and 50 STEM integrated thinking and application ability indexes are constructed; a student-centered iSTEM learning model is developed; iSTEM learning activities can enhance the learning and integration of STEM knowledge; the iSTEM learning model produces a positive effect on integrated STEM thinking and the imagination ability of vocational high school students. Finally, this study puts forward recommendations for improving STEM education practices, teaching strategy design and high-level STEM ability assessment in the future.

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Analysis of the Scientific Imagination Process

Cited by 40 publications

References 28 publications

Using Localized Features for Analyzing College Students’ Imagination

Using Localized Features for Analyzing College Students’ Imagination

University Students Developing Imaginative Problem Solving Skills — The Case of Food Engineering

Construction and Development of iSTEM Learning Model

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