The effects of merging student emotion recognition with learning management systems on learners’ motivation and academic achievements

Özek, Müzeyyen Bulut

doi:10.1002/cae.22000

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…Through data mining technology, students can be deeply mined for evaluation data, analyze the influence factors of student development, and then provide pathways for innovative student management in colleges and universities. Literature [24] analyzes the student learning management system, which allows colleges and universities to implement distance education activities. Through 103 engineering students as an example for the analysis, the learning management system can better understand the emotional development of students, for colleges and universities to develop targeted student management measures to provide guidance.…”

Section: ) Student Managementmentioning

confidence: 99%

Research on the synergistic nurturing mechanism of ideological work and student management in colleges and universities under the binary structure

2023

Applied Mathematics and Nonlinear Sciences

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This paper takes the synergy between ideological work and student management in colleges and universities as an entry point, analyzes the intrinsic mechanism between the two, and gives the synergistic effect of parenting after the integration of the two. The synergistic mechanism between student management and ideological work is explored by mining students’ daily behavioral data, and after analyzing students’ daily behavioral data based on density-optimized clustering algorithm, risk warning detection of students’ academic situation is carried out by combining the improved LSTM model. The GCN network model is utilized to detect students’ psychological disorders. In order to verify the effectiveness of the method given in this paper for exploring the synergistic cultivation of ideological work and student management, quantitative analysis was carried out through the data of college students’ one-card. The results show that the movement data of college students are divided into 4 classes, of which the largest proportion of students is class cluster 4 is 44.59%, the risk prediction accuracy of the improved LSTM academic warning model is 0.731, and the GCN network model has a model loss value of about 0.25% after about 50 iterations when the threshold value is 0. Colleges and universities under the binary structure can narrow the value differences between students through data mining technology, and can help students remove psychological barriers, prompting the generation of a collaborative parenting mechanism between ideological work and student management in colleges and universities.

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Section: ) Student Managementmentioning

confidence: 99%

Research on the synergistic nurturing mechanism of ideological work and student management in colleges and universities under the binary structure

2023

Applied Mathematics and Nonlinear Sciences

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“…The times we live in are always changing, and it is difficult to meet the needs of students by only relying on the traditional mode of education and management of students in the past, but a new type of student management mode can be created with the help of big data technology means [7][8]. Colleges and universities need to strengthen the integration and development of student management work and network ideological education work, cultivate more excellent talents, and promote the development of college education [9][10].…”

Section: Introductionmentioning

confidence: 99%

An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering

Sun

2023

Applied Mathematics and Nonlinear Sciences

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This paper first analyzes the path of network ideological education to boost student management and constructs a student management platform that integrates network ideological education using big data technology. Secondly, K-means clustering analysis is used to mine students’ campus behaviors, and the DPC algorithm combined with the improved AdaBoost algorithm is used to detect students’ academic anomalies so as to help schools better realize student management. Finally, performance testing and application analysis were conducted to verify the effectiveness of the student management platform that integrates online ideological education. The results show that when the number of concurrent users is 5000, the CPU occupancy rate and response time of this platform are 10.34% and 0.52 s. It is able to analyze and detect abnormalities in students’ campus behaviors and academic situations and encourage universities to analyze students’ situations through visual reports. This shows that the student management platform integrating network ideological education in the era of big data can promote the level of student management in colleges and universities.

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“…The system should be reliable enough when running on mid-range computer specs. Bulut Özek [25] provided a system that integrates student emotion recognition with a learning management system. The aim is to disclose how the integration affects learner motivation and academic performance.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Student Emotions in Classroom Learning Based on Image Processing

Wang¹,

Cuntapay²

2022

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During the learning process, students should keep positive emotions like delightfulness, cheerfulness, joy, and enthusiasm, and try to realize happy learning. This would improve learning efficiency and learning effect. However, most of the existing models cannot recognize negative discrete emotions and dimensional emotions, such as indignation and sadness, of students in classroom learning. To solve the problem, this paper deeply explores the recognition of student emotions in classroom learning based on image processing. Specifically, the authors designed a recognition model of emotional state of classroom learning, modeled the discrete emotions of students in classroom learning, and discussed the relationship between learning effect, response efficacy and stimulus. Furthermore, the generation process of student emotions in classroom learning was analyzed, and the recognition model of emotional state of classroom learning was finalized based on ResNet18. In addition, the recognition model was optimized by introducing the local importance pooling and adding a recalibration module. The experimental results verify the effectiveness of the constructed model.

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The effects of merging student emotion recognition with learning management systems on learners’ motivation and academic achievements

Cited by 13 publications

References 30 publications

Research on the synergistic nurturing mechanism of ideological work and student management in colleges and universities under the binary structure

Research on the synergistic nurturing mechanism of ideological work and student management in colleges and universities under the binary structure

An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering

Recognition of Student Emotions in Classroom Learning Based on Image Processing

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