Recognizing learning emotion based on convolutional neural networks and transfer learning

Hung, Jason C.; Lin, Kuan‐Cheng; Lai, Nian-Xiang

doi:10.1016/j.asoc.2019.105724

Cited by 69 publications

(32 citation statements)

References 11 publications

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“…Zhang et al proposed an optimization algorithm based on teaching and learning, which can ensure that the network algorithm has a faster convergence speed and reduce the possibility of falling into the local optimum [ 9 ]. Hung et al proposed that students' learning status can be understood through the recognition of learners' facial emotion, and convolutional neural network will automatically learn the necessary features of the whole image [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Hua

2021

Computational Intelligence and Neuroscience

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The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students’ basketball tactical basic cooperation ability.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Hua

2021

Computational Intelligence and Neuroscience

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“…Personalized learning, customized learning path and sensing the learner emotions can increase the retention rate of learners [16,17]. Understanding learner's emotions play a vital role in providing feedback about them to the E-learning system [18,19]. The term 'affect' refers to the range of affective emotional states and feelings that one can experience while engaged in the learning process.…”

Section: Methodsmentioning

confidence: 99%

I-Quiz: An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Shobana¹,

Kumar²

2022

Computer Systems Science and Engineering

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Electronic learning (e-learning) has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities. Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web. Post covid-19 pandemic, online learning has become the most essential and inevitable medium of learning in primary, secondary and higher education. In recent times, Massive Open Online Courses (MOOCs) have transformed the current education strategy by offering a technology-rich and flexible form of online learning. A key component to assess the learner's progress and effectiveness of online teaching is the Multiple Choice Question (MCQ) assessment in most of the MOOC courses. Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score. This is due to the possibility of random and smart guesses, learners can attempt, as MCQ assessments are more vulnerable than essay type assessments. This paper presents the architecture, development, evaluation of the I-Quiz system, an intelligent assessment tool, which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner's real knowledge acquisition level. The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques. The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners. A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming. The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period. The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner. The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.

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“…Fiducial Points Detection and CNN [77] 93.8% Hybrid of CNN and RNN [78] 94.91% Multi-block transfer learning [79] 94.92% 2D Gabor filter and Extreme Learning Machine [80] 95.28% Stationary Wavelet Transform and CNN [81] 98.87% three designs, and all of the three designs. For each dataset in Table 6, the first row depicts the results of the removal of all three techniques.…”

Section: Model Fusionmentioning

confidence: 99%

Facial Emotion Recognition Using Asymmetric Pyramidal Networks With Gradient Centralization

et al. 2021

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Recognizing learning emotion based on convolutional neural networks and transfer learning

Cited by 69 publications

References 11 publications

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

I-Quiz: An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Facial Emotion Recognition Using Asymmetric Pyramidal Networks With Gradient Centralization

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