The Development of an Imaginative Thinking Scale

Lin, Huei-Yung; Tsau, Saiau-Yue

doi:10.2190/ic.32.3.b

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…However, the meanings of imagination are not the same as the ones of creativity. The gyroscope theory proposed by Lin and Tsau (2012), who extended and redefined the activities related to the development of new ideas about physical objects or psychological feelings from the Vygosky theory (Lindqvist, 2003;Vygotsky, 2004), demonstrated the difference between creativity and imagination. Its concept is shown in Figure 1.…”

Section: Imaginationmentioning

confidence: 99%

“…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%

“…To avoid the effect of low In order to cultivate the imagination of college students and give the necessary training courses, we focus on measuring freshmen's and sophomores' imagination for analysis by using forecast with clustering and integration (FCI). The imaginative evaluations are performed with an imagination scale proposed by Lin and Tsau (2012). The scale has been validated with high reliability for college students, and contains four components, namely initiation, fluency, flexibility, and originality.…”

Section: Introductionmentioning

confidence: 99%

“…The scale has been validated with high reliability for college students, and contains four components, namely initiation, fluency, flexibility, and originality. Lin and Tsau (2012) considered that imagination belongs to divergent thinking and can be defined seriously as four portions derived from the theoretical basis of IERG's five understandings (Cant, 2012). Therefore, in accordance with the sequential processes of initiation, fluency, flexibility, and originality, the degrees, relationship, and thought processes can be easily revealed if students answer the questions on the scale sequentially.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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%