Decision application mechanism of regression analysis of multi-category learning behaviors in interactive learning environment

Xia, Xiaona

doi:10.1080/10494820.2021.1916767

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…In order to realize the reliability, instantaneity and integrity of forumng, it is necessary to continuously improve the business mode of forumng, according to the development trend of interactive learning environment and learning needs (Xia, 2021b). Through appropriate technical means and business patterns, we might effectively improve the interactive cooperation friendliness and convenience of forumng…”

Section: Analysis Resultsmentioning

confidence: 99%

“…To construct the feature clusters of learning behaviors, two aspects need to be considered: one is to design the process and related nodes, so as to find the features; The other is that learning behaviors involve many platforms, we should infer the features and possible relationships based on the comprehensive analysis. In this aspect, we have high requirements for data analysis and decision prediction (Aguilar et al, 2021; Xia, 2021b). This study will analyze and demonstrate the feature clusters in the second aspect.…”

Section: Related Workmentioning

confidence: 99%

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The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

Wang

Xia

2023

Sage Open

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The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

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Section: Analysis Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

Wang

Xia

2023

Sage Open

Self Cite

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“…Online learning process in a certain period forms relatively complete learning behavior. Therefore, learning behavior mainly focuses on the continuous flow of interactive learning activities, that needs friendly collaborative training to form effective learner behavior (Xia, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Application Technology on Collaborative Training of Interactive Learning Activities and Tendency Preference Diversion

Xia

2022

Sage Open

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Mining problems and exploring rules are the key problems in the learning process, and also the difficulties in education big data. Therefore, taking learning behavior as the research objective, this study demonstrates the collaborative training method of multi view learning interaction process driven by big data, so as to realize the tendency preference diversion of learning behavior. Based on collaborative training method of learning behavior. Through adequate experiments, we get the suitable and valuable rules, constructs the directed topological relationship of tendency preference division, and mines the feasible improvement measures and intervention mechanism. Experiments and practices show that the potential topological relationship can effectively improve and enable learning, and improve the assessment results.

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“…Clustering is an unsupervised in data mining field (according to the category of the unknown training sample solve various problems in pattern recognition [1][2][3]) clustering method [4].The reference [5] proposed a peak density clustering (Density Peaks Clustering, DPC) algorithm. This method could recognize arbitrary shapes of data, intuitively find the number of clusters, and easily find noise points.…”

Section: Introductionmentioning

confidence: 99%

A Density Peak Clustering Method based on Improved Siphon Effect

2022

Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering

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To solve the problem that the density peak clustering algorithm needs to manually select the clustering center, a density peak clustering algorithm based on improved siphon effect (IDPC) is designed. IDPC completes the clustering of research objects with various distribution shapes by removing the maximum cluster center weight in the descending sequence, calculating and iterating the potential difference, and selecting the relative equilibrium point. A comprehensive comparative experiment is carried out on UCI data set. The experimental results show that IDPC can accurately and automatically select the cluster center and cluster number, improve the performance indexes such as Fowles mallows score, Rand index and adjusted mutual information, and can adaptively process data sets with low dimension and various distribution shapes.

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Decision application mechanism of regression analysis of multi-category learning behaviors in interactive learning environment

Cited by 12 publications

References 11 publications

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

Application Technology on Collaborative Training of Interactive Learning Activities and Tendency Preference Diversion

A Density Peak Clustering Method based on Improved Siphon Effect

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