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This study analyzed the collaborative problem solving (CPS) behavioral transition patterns of 53,859 Taiwanese students in science at age 15 by using an online Taiwanese CPS assessment that was designed according to the Programme for International Student Assessment 2015 CPS framework. Because of behavioral changes over the testing period, the CPS target skills that corresponded to the assessment items can be viewed as a CPS behavioral sequence. Hence, a lag sequential analysis was applied to explore the significance of the interactions among the CPS skills. The behavioral sequence is coded according to the level of mastery (0, 1, or 2) of items. The CPS transition patterns were analyzed in three gaps, namely the gender gap, the urban–rural gap, and the achievement gap. The findings showed that “Monitoring and repairing the shared understanding” was a crucial CPS skill in science. Moreover, the female students who would follow rules of engagement effectively exhibited higher scores than male students did in monitoring the results of their actions and evaluating their success in solving the problem. No obvious differences were observed in the urban–rural gap, whereas differences were observed in the achievement gap.
Cheng-Hsuan Li; Pei-Ling Tsai; Zhi-Yong Liu; Wen-Chieh Huang; Pei-Jyun Hsieh. Exploring Collaborative Problem Solving Behavioral Transition Patterns in Science of Taiwanese Students at Age 15 According to Mastering Levels. Sustainability 2021, 13, 8409 .
AMA StyleCheng-Hsuan Li, Pei-Ling Tsai, Zhi-Yong Liu, Wen-Chieh Huang, Pei-Jyun Hsieh. Exploring Collaborative Problem Solving Behavioral Transition Patterns in Science of Taiwanese Students at Age 15 According to Mastering Levels. Sustainability. 2021; 13 (15):8409.
Chicago/Turabian StyleCheng-Hsuan Li; Pei-Ling Tsai; Zhi-Yong Liu; Wen-Chieh Huang; Pei-Jyun Hsieh. 2021. "Exploring Collaborative Problem Solving Behavioral Transition Patterns in Science of Taiwanese Students at Age 15 According to Mastering Levels." Sustainability 13, no. 15: 8409.
Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization with procedures among personnel is difficult to measure. The US military introduced a chatbot as part of its digital training material to enhance training effectiveness and avoid equipment damage. In this study, the contribution of an artificial intelligence (AI) chatbot in training is explored. To evaluate the necessity of an AI chatbot, research samples were divided into two groups, namely an experimental and a control group, with 20 people in each group. A paired t test was employed for differentiation analysis of pretest and posttest average scores, revealing that the two groups exhibited a statistically significant improvement in their learning performance. In addition, the blend of analysis of variance results indicated significant differences between the two groups. The chatbot training was more effective than traditional instructor teaching in terms of trainee performance improvement.
Chia-Ching Yuan; Cheng-Hsuan Li; Chin-Cheng Peng. Development of mobile interactive courses based on an artificial intelligence chatbot on the communication software LINE. Interactive Learning Environments 2021, 1 -15.
AMA StyleChia-Ching Yuan, Cheng-Hsuan Li, Chin-Cheng Peng. Development of mobile interactive courses based on an artificial intelligence chatbot on the communication software LINE. Interactive Learning Environments. 2021; ():1-15.
Chicago/Turabian StyleChia-Ching Yuan; Cheng-Hsuan Li; Chin-Cheng Peng. 2021. "Development of mobile interactive courses based on an artificial intelligence chatbot on the communication software LINE." Interactive Learning Environments , no. : 1-15.
The current study explores students’ collaboration and problem solving (CPS) abilities using a human-to-agent (H-A) computer-based collaborative problem solving assessment. Five CPS assessment units with 76 conversation-based items were constructed using the PISA 2015 CPS framework. In the experiment, 53,855 ninth and tenth graders in Taiwan were recruited, and a multidimensional item response analysis was used to develop CPS scales and represent the students’ collaboration and problem solving performance. The results show that the developed H-A approach is feasible for measuring students’ CPS skills, and the CPS scales are also shown to be reliable. In addition, the students’ CPS performance scores are further explored and discussed under the PISA CPS framework.
Bor-Chen Kuo; Chen-Huei Liao; Kai-Chih Pai; Shu-Chuan Shih; Cheng-Hsuan Li; Magdalena Mo Ching Mok. Computer-based collaborative problem-solving assessment in Taiwan. Educational Psychology 2019, 40, 1164 -1185.
AMA StyleBor-Chen Kuo, Chen-Huei Liao, Kai-Chih Pai, Shu-Chuan Shih, Cheng-Hsuan Li, Magdalena Mo Ching Mok. Computer-based collaborative problem-solving assessment in Taiwan. Educational Psychology. 2019; 40 (9):1164-1185.
Chicago/Turabian StyleBor-Chen Kuo; Chen-Huei Liao; Kai-Chih Pai; Shu-Chuan Shih; Cheng-Hsuan Li; Magdalena Mo Ching Mok. 2019. "Computer-based collaborative problem-solving assessment in Taiwan." Educational Psychology 40, no. 9: 1164-1185.
The purpose of this study is to explore the validity of the assessment tool. The purposive sampling method is applied in this research on a total of 551 preschool children between 4 and 6 years old. Their ages range from 46 to 81 months, with an average age of 63.9 months (SD = 7.58). The assessment tool used in this research is the “Computerized Visual Motor Integration Assessment Tool Using Chinese Basic Strokes”. The multivariate logistic regression analysis was used to automatically determine children’s writing classification. The findings suggest that the assessment tool developed has good validity in terms of sensitivity, specificity and the accuracy. This tool is recommended for use in the diagnosis of Chinese visual motor integration issues for preschoolers, so that their potential writing difficulties may be detected early, and the goal of early intervention may be achieved.
Cheng-Hsuan Li; Huey-Min Wu; Bor-Chen Kuo; Yu-Mao Yang; Chin-Kai Lin; Wei-Hsiang Wang. The validity of computerized visual motor integration assessment using Chinese basic strokes. Interactive Learning Environments 2018, 26, 1074 -1089.
AMA StyleCheng-Hsuan Li, Huey-Min Wu, Bor-Chen Kuo, Yu-Mao Yang, Chin-Kai Lin, Wei-Hsiang Wang. The validity of computerized visual motor integration assessment using Chinese basic strokes. Interactive Learning Environments. 2018; 26 (8):1074-1089.
Chicago/Turabian StyleCheng-Hsuan Li; Huey-Min Wu; Bor-Chen Kuo; Yu-Mao Yang; Chin-Kai Lin; Wei-Hsiang Wang. 2018. "The validity of computerized visual motor integration assessment using Chinese basic strokes." Interactive Learning Environments 26, no. 8: 1074-1089.
Cheng-Hsuan Li; Zhi-Yong Liu. Collaborative Problem-Solving Behavior of 15-Year-Old Taiwanese Students in Science Education. Eurasia Journal of Mathematics, Science and Technology Education 2017, 13, 6577 -6595.
AMA StyleCheng-Hsuan Li, Zhi-Yong Liu. Collaborative Problem-Solving Behavior of 15-Year-Old Taiwanese Students in Science Education. Eurasia Journal of Mathematics, Science and Technology Education. 2017; 13 (10):6577-6595.
Chicago/Turabian StyleCheng-Hsuan Li; Zhi-Yong Liu. 2017. "Collaborative Problem-Solving Behavior of 15-Year-Old Taiwanese Students in Science Education." Eurasia Journal of Mathematics, Science and Technology Education 13, no. 10: 6577-6595.
Morphological awareness is the foundation for the important developmental skills involved with vocabulary, as well as understanding the meaning of words, orthographic knowledge, reading, and writing. Visual perception of space and radicals in two-dimensional positions of Chinese characters' morphology is very important in identifying Chinese characters. The important predictive variables of special and visual perception in Chinese characters identification were investigated in the growth model in this research. The assessment tool is the "Computerized Visual Perception Assessment Tool for Chinese Characters Structures" developed by this study. There are two constructs, basic stroke and character structure. In the basic stroke, there are three subtests of one, two, and more than three strokes. In the character structure, there are three subtests of single-component character, horizontal-compound character, and vertical-compound character. This study used purposive sampling. In the first year, 551 children 4-6 years old participated in the study and were monitored for one year. In the second year, 388 children remained in the study and the successful follow-up rate was 70.4%. This study used a two-wave cross-lagged panel design to validate the growth model of the basic stroke and the character structure. There was significant correlation of the basic stroke and the character structure at different time points. The abilities in the basic stroke and in the character structure steadily developed over time for preschool children. Children's knowledge of the basic stroke effectively predicted their knowledge of the basic stroke and the character structure.
Huey-Min Wu; Cheng-Hsaun Li; Bor-Chen Kuo; Yu-Mao Yang; Chin-Kai Lin; Wei-Hsiang Wan. Validity of the growth model of the ‘computerized visual perception assessment tool for Chinese characters structures’. Research in Developmental Disabilities 2017, 67, 71 -81.
AMA StyleHuey-Min Wu, Cheng-Hsaun Li, Bor-Chen Kuo, Yu-Mao Yang, Chin-Kai Lin, Wei-Hsiang Wan. Validity of the growth model of the ‘computerized visual perception assessment tool for Chinese characters structures’. Research in Developmental Disabilities. 2017; 67 ():71-81.
Chicago/Turabian StyleHuey-Min Wu; Cheng-Hsaun Li; Bor-Chen Kuo; Yu-Mao Yang; Chin-Kai Lin; Wei-Hsiang Wan. 2017. "Validity of the growth model of the ‘computerized visual perception assessment tool for Chinese characters structures’." Research in Developmental Disabilities 67, no. : 71-81.
Recent studies show that hyperspectral image classification techniques that use both spectral and spatial information are more suitable, effective, and robust than those that use only spectral information. Using a spatial-contextual term, this study modifies the decision function and constraints of a support vector machine (SVM) and proposes two kinds of spatial-contextual SVMs for hyperspectral image classification. One machine, which is based on the concept of Markov random fields (MRFs), uses the spatial information in the original space (SCSVM). The other machine uses the spatial information in the feature space (SCSVMF), i.e., the nearest neighbors in the feature space. The SCSVM is better able to classify pixels of different class labels with similar spectral values and deal with data that have no clear numerical interpretation. To evaluate the effectiveness of SCSVM, the experiments in this study compare the performances of other classifiers: an SVM, a context-sensitive semisupervised SVM, a maximum likelihood (ML) classifier, a Bayesian contextual classifier based on MRFs (ML_MRF), and nearest neighbor classifier. Experimental results show that the proposed method achieves good classification performance on famous hyperspectral images (the Indian Pine site (IPS) and the Washington, DC mall data sets). The overall classification accuracy of the hyperspectral image of the IPS data set with 16 classes is 95.5%. The kappa accuracy is up to 94.9%, and the average accuracy of each class is up to 94.2%.
Cheng-Hsuan Li; Bor-Chen Kuo; Chin-Teng (Ct) Lin; Chih-Sheng Huang. A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2011, 50, 784 -799.
AMA StyleCheng-Hsuan Li, Bor-Chen Kuo, Chin-Teng (Ct) Lin, Chih-Sheng Huang. A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2011; 50 (3):784-799.
Chicago/Turabian StyleCheng-Hsuan Li; Bor-Chen Kuo; Chin-Teng (Ct) Lin; Chih-Sheng Huang. 2011. "A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification." IEEE Transactions on Geoscience and Remote Sensing 50, no. 3: 784-799.
Research has shown fuzzy c-means (FCM) clustering to be a powerful tool to partition samples into different categories. However, the objective function of FCM is based only on the sum of distances of samples to their cluster centers, which is equal to the trace of the within-cluster scatter matrix. In this study, we propose a clustering algorithm based on both within- and between-cluster scatter matrices, extended from linear discriminant analysis (LDA), and its application to an unsupervised feature extraction (FE). Our proposed methods comprise between- and within-cluster scatter matrices modified from the between- and within-class scatter matrices of LDA. The scatter matrices of LDA are special cases of our proposed unsupervised scatter matrices. The results of experiments on both synthetic and real data show that the proposed clustering algorithm can generate similar or better clustering results than 11 popular clustering algorithms: K-means, K-medoid, FCM, the Gustafson-Kessel, Gath-Geva, possibilistic c-means (PCM), fuzzy PCM, possibilistic FCM, fuzzy compactness and separation, a fuzzy clustering algorithm based on a fuzzy treatment of finite mixtures of multivariate Student's t distributions algorithms, and a fuzzy mixture of the Student's t factor analyzers model. The results also show that the proposed FE outperforms principal component analysis and independent component analysis.
Cheng-Hsuan Li; Bor-Chen Kuo; Chin-Teng (Ct) Lin. LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction. IEEE Transactions on Fuzzy Systems 2010, 19, 152 -163.
AMA StyleCheng-Hsuan Li, Bor-Chen Kuo, Chin-Teng (Ct) Lin. LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction. IEEE Transactions on Fuzzy Systems. 2010; 19 (1):152-163.
Chicago/Turabian StyleCheng-Hsuan Li; Bor-Chen Kuo; Chin-Teng (Ct) Lin. 2010. "LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction." IEEE Transactions on Fuzzy Systems 19, no. 1: 152-163.
In recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data, and some results show that kernel-based classifiers have satisfying performances. Many studies about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. However, NWFE is still based on linear transformation. In this paper, the kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation, and the experimental results show that KNWFE outperforms NWFE, decision-boundary feature extraction, independent component analysis, kernel-based principal component analysis, and generalized discriminant analysis.
Bor-Chen Kuo; Cheng-Hsuan Li; Jinn-Min Yang. Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2009, 47, 1139 -1155.
AMA StyleBor-Chen Kuo, Cheng-Hsuan Li, Jinn-Min Yang. Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2009; 47 (4):1139-1155.
Chicago/Turabian StyleBor-Chen Kuo; Cheng-Hsuan Li; Jinn-Min Yang. 2009. "Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 47, no. 4: 1139-1155.
Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Many studies show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. The detection of class boundaries is an important part in NWFE and the weighted mean was defined for this purpose. In this paper, a kernel-based feature extraction is proposed based on a new class boundary detection mechanism. The soft-margin support vector machine (SVM) binary classifier and the support vector domain description (SVDD) are applied to detect the boundaries between two classes and one class, respectively. The results of real data experiments show that the proposed method outperforms original NWFE.
Cheng-Hsuan Li; Bor-Chen Kuo; Chin-Teng Lin; Chih-Cheng Hung. Dimension Reduction for Hyperspectral Image Classification via Support Vector based Feature Extraction. IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium 2008, 5, V - 389 -V - 392.
AMA StyleCheng-Hsuan Li, Bor-Chen Kuo, Chin-Teng Lin, Chih-Cheng Hung. Dimension Reduction for Hyperspectral Image Classification via Support Vector based Feature Extraction. IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. 2008; 5 ():V - 389-V - 392.
Chicago/Turabian StyleCheng-Hsuan Li; Bor-Chen Kuo; Chin-Teng Lin; Chih-Cheng Hung. 2008. "Dimension Reduction for Hyperspectral Image Classification via Support Vector based Feature Extraction." IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium 5, no. : V - 389-V - 392.
The k-nearest neighbor classifier is a simple and appealing approach to classification problems. It expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensional situation. Using a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. A technique that computes a locally flexible metric by means of the decision boundaries of support vector machines (SVMs) is proposed. Then the modified neighborhoods can be shrunk in directions orthogonal to these decision boundaries and elongated parallel to the boundaries. Thereafter, any neighborhood-based classifier can use the modified neighborhoods.
Hsin-Hua Ho; Bor-Chen Kuo; Jin-Shiuh Taur; Cheng-Hsuan Li. A Flexible Metric Nearest-Neighbor Classification based on the Decision Boundaries of SVM for Hyperspectral Image. IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium 2008, 4, IV - 212 -IV - 215.
AMA StyleHsin-Hua Ho, Bor-Chen Kuo, Jin-Shiuh Taur, Cheng-Hsuan Li. A Flexible Metric Nearest-Neighbor Classification based on the Decision Boundaries of SVM for Hyperspectral Image. IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. 2008; 4 ():IV - 212-IV - 215.
Chicago/Turabian StyleHsin-Hua Ho; Bor-Chen Kuo; Jin-Shiuh Taur; Cheng-Hsuan Li. 2008. "A Flexible Metric Nearest-Neighbor Classification based on the Decision Boundaries of SVM for Hyperspectral Image." IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium 4, no. : IV - 212-IV - 215.