Student Classroom behavior Recognition based on OpenPose and Deep Learning

Chen, Ge; Ji, Jianqiang; Chaofeng, Huang

doi:10.1109/icsp54964.2022.9778501

Cited by 6 publications

(2 citation statements)

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“…Earlier, it was mentioned that due to the problem of local optima, the BP neural network often gets stuck, and to address this issue, an improved Whale Optimization Algorithm (WOA) called Cubic map improve whale optimization algorithm (CIWOA) was proposed. This algorithm uses the Cubic map chaotic map to initialize the positions of the whales, assuming that the current best candidate solution is the target prey or close to the optimal solution, and then continuously optimizes the initial weights and thresholds to quickly and effectively solve the problem of BP neural network getting stuck in local optima [4] . Therefore, we use this algorithm to predict the distribution of player guesses and attempts for a future date, i.e., predicting the related percentages of (1,2,3,4,5,6,X) for a future date.On March 1,2023, the word "EERIT" was used as the research object.…”

Section: Ciwoa Optimization Algorithmmentioning

confidence: 99%

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Wu,

Li,

Lin

et al. 2023

Frontiers in Artificial Intelligence and Applications

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To further improve the stickiness of puzzle game players, we developed a prediction model based on the BP neural network. Firstly, we preprocessed the existing data to remove some useless information. Then, we introduced the WOA algorithm to optimize the BP neural network model and improve its prediction accuracy. To predict the distribution percentage of attempts to guess a given word in the future, we improved the BP neural network using the multi-input multi-output CIWOA algorithm (Cubic map improvement whale optimization algorithm). The experimental results showed that the WOA-BP prediction model predicted player data for March 1, 2023, to be between 20,000 and 25,000, while the actual data was 19,655. Our prediction model had a small margin of error, indicating good prediction accuracy. Based on this model, we provided reasonable adjustment suggestions for the game company’s later operations.

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Section: Ciwoa Optimization Algorithmmentioning

confidence: 99%

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Wu,

Li,

Lin

et al. 2023

Frontiers in Artificial Intelligence and Applications

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“…The maturing deep learning technology and image processing technique have laid a theoretical foundation for developing algorithms for identifying student behavior during practice and training based on video image. Chen et al [19] pointed out in their paper that intelligent identification of students' classroom behavior is becoming increasingly important in a smart education background. To improve the accuracy of intelligent identification of student behavior, the authors collected 7 typical classroom behavior images of 300 students, preprocessed the data, then a classic deep network model VGG16, which was trained on ImageNet dataset, was applied to the student classroom behavior identification task.…”

Section: Introductionmentioning

confidence: 99%

Student Behavior Identification During Practice and Training Based on Video Image

Chen¹,

Fan²,

Dai³

et al. 2023

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Enriching and developing the connotation and value of labor education theories can help students in higher vocational colleges form correct viewpoint and attitude towards labor. Higher vocational colleges should put more efforts to education through practice based on the features of each discipline. Accurately identifying students' behavior in complex practice and training scenarios is very important for teachers to know about their status during practice and training, however, existing research results are not applicable to complex practice and training scenarios since they have neither considered how to improve the accuracy of static image identification while ensuring the model is lightweight structured, nor considered the time series information of students' behavior during practice and training in the collected video images. For this reason, this paper took the property management major as the subject to study the identification of student behavior during practice and training based on video image. In the paper, the students' practice and training content was divided into three aspects, a task of asking students to cooperate with each other to deal with an equipment failure emergency was adopted for the research, and a research idea of helping teachers figure out students' status during practice and training via identifying their actions and intentions during the said activities was determined. Then, a few pre-processing operations were performed on the captured video images of student behavior during practice and training, including removing abnormal image frames, filtering, and aligning, etc. After that, based on the collected video image data, the dynamic convolution kernel was improved and optimized, and a lightweight convolution network model was built for identifying student behavior during practice and training. At last, experimental results verified the validity of the proposed identification model.

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An Effective Teacher Behavior Recognition Method Based on Teaching-focused Regions Extraction and TeacherMask Data Augmentation

Liang,

Liu,

Zhao

2024

2024 6th International Conference on Computer Science and Technologies in Education (CSTE)

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Student Classroom behavior Recognition based on OpenPose and Deep Learning

Cited by 6 publications

References 0 publications

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Student Behavior Identification During Practice and Training Based on Video Image

An Effective Teacher Behavior Recognition Method Based on Teaching-focused Regions Extraction and TeacherMask Data Augmentation

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