Interaction recognition and intervention based on context feature fusion of learning behaviors in interactive learning environments

Xia, Xiaona

doi:10.1080/10494820.2021.1871632

Cited by 23 publications

(11 citation statements)

References 23 publications

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“…Learning behaviors should involve the whole learning process and learning assessment (Ruipérez-Valiente et al, 2020; Xia, 2021a). The interactive learning environment is one important data source.…”

Section: Learning Behavior Description and Standardizationmentioning

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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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: Learning Behavior Description and Standardizationmentioning

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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“…e concept of an artificial neural network (ANN) is to regard the biological simulation process as the basic feature, and the computing structure reflecting the local characteristics of the human brain is an artificial neural network. u i represents the output result of a combined input signal, v i represents the local sensing area of a neuron, x i represents the input signal of neuron i, and W ij represents the synaptic strength [11,12].…”

Section: Artificial Neuron Modelmentioning

confidence: 99%

Evaluation of Women’s Entrepreneurship Education Based on BP Neural Network

Lin

Wang

et al. 2022

Scientific Programming

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Entrepreneurship education is the key to cultivating the entrepreneurial spirit of national talents. The goal of entrepreneurship education is to cultivate a large number of pioneering talents, and women’s entrepreneurship education is particularly important. The government, enterprises, schools, and all sectors of society start from multiple channels to let students strengthen their entrepreneurial awareness and improve their entrepreneurial ability. This paper uses the most advanced BP neural network algorithm to study and evaluate women’s entrepreneurship education. This paper briefly introduces the concept and model of the artificial neural network and establishes a BP neural network model while improving the classical BP neural network. Then, we list the application process of the BP neural network model in evaluating women’s entrepreneurship education. We select college students for empirical analysis, determine the number of neurons, and select 9 items as the evaluation index of women’s entrepreneurship education. Using valid assumptions, we further determine the model learning rate and momentum factor. Finally, the results show that the actual evaluation results based on the BP neural network are basically the same as the expected results, and the maximum relative error between the actual value and the expected value is approximately 1.64 %, and the comprehensive evaluation value is 92 points. The proposed algorithm can effectively avoid the problems of instability and slow convergence of the traditional model and can comprehensively improve the accuracy of the evaluation results of women’s entrepreneurship education.

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“…Through the collaborative training of learning behaviors, the learning behavior has group tendency. It has a powerful data-driven significance to guide and construct relatively effective learning behaviors in a short time (Xia, 2021b).…”

Section: The Empirical Study Of Tendency Preference Diversionmentioning

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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Interaction recognition and intervention based on context feature fusion of learning behaviors in interactive learning environments

Cited by 23 publications

References 23 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

Evaluation of Women’s Entrepreneurship Education Based on BP Neural Network

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

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