Construct Concept Structure for Linear Algebra Based on Cognition Diagnosis and Clustering with Mahalanobis Distances

Chen, Chin Chun; Lin, Yifan; Yih, Jeng Ming; Juan, Shu Yi

doi:10.4028/www.scientific.net/amr.211-212.756

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Therefore teachers should strengthen teach and application of the concept for new students learning the concept exercises. [10][11][12]. Overall, the proposed cognitive loading components can effectively explain the difficulty of the basic mathematics achievement assessment items, also supporting teachers to implement remedial teaching for learning backward students in coming future.…”

Section: Items Cognitive Components For Difficulty Predictionmentioning

confidence: 81%

Cognitive Component Analysis of Basic Mathematics Achievement Assessment

Yang¹

2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

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To provide some useful information into teaching practice adjustment, teachers usually need some professional supports. In this study, an analysis framework on item cognitive component is proposed and implemented. The sample for this study consisted of the Nursing and Management College student from eight schools in Taiwan. A sample of 2051 students was drawn for this study. A 3 cognitive components coding schema was developed to predict the item difficulty parameters. The results suggest that the framework proposed can predict around 70.9% of the difficulty variance across seven contents.

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Section: Items Cognitive Components For Difficulty Predictionmentioning

confidence: 81%

Cognitive Component Analysis of Basic Mathematics Achievement Assessment

Yang¹

2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

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“…Unlike Euclidean distance, it is based on the overall sample set, considering the connections between the various features of the samples. By introducing the correlation and variance between samples, it can better measure the similarity between samples, so it is commonly used in clustering [29], anomaly detection [30], and pattern recognition [31]. The Mahalanobis distance D M x i , x j between the sample x i and the sample x j can be calculated by Eq.…”

Section: Related Theories 21 Silhouette Value Is Calculated Based On ...mentioning

confidence: 99%

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

Zhu,

Jing,

Qiu

et al. 2024

CMC

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When building a classification model, the scenario where the samples of one class are significantly more than those of the other class is called data imbalance. Data imbalance causes the trained classification model to be in favor of the majority class (usually defined as the negative class), which may do harm to the accuracy of the minority class (usually defined as the positive class), and then lead to poor overall performance of the model. A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article, which is based on a new hybrid resampling approach (MSHR) and a new fine cost-sensitive support vector machine (CS-SVM) classifier (FCSSVM). The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples, based on which, the so-called pseudo-negative samples are screened out to generate new positive samples (over-sampling step) through linear interpolation and are deleted finally (under-sampling step). This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline, without changing the overall scale of the dataset. The FCSSVM is an improved version of the traditional CS-SVM. It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously, and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice (RIME) algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline. To verify the effectiveness of the proposed method, a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets. The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases, and both the MSHR and the FCSSVM played significant roles.

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Construct Concept Structure for Linear Algebra Based on Cognition Diagnosis and Clustering with Mahalanobis Distances

Cited by 2 publications

References 9 publications

Cognitive Component Analysis of Basic Mathematics Achievement Assessment

Cognitive Component Analysis of Basic Mathematics Achievement Assessment

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

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