Analysis of the Correlation between the Mental Stress of College Students and Their Learning Initiative during the Outbreak of COVID-19

Xi, Limin; Wen, Bangchun; Zhang, Hua

doi:10.3991/ijet.v17i23.35933

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Contemporary college students have a certain degree of autonomy in their learning activities, that's, they have the right and freedom to choose independently in terms of learning content, learning methods and learning time [6][7][8][9][10][11]. Although college students also need the guidance and management of teachers in learning, their knowledge structure has changed from the vertical accumulation in the middle school stage to the horizontal expansion in the university stage [12][13][14][15][16][17][18][19]. A college student who is active in learning has more clear goals and enthusiasm for learning in terms of class participation, independent learning, and seeking knowledge, and their academic performance and comprehensive quality are also higher.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Students' Learning Motivation Based on Combinatorial Optimization Algorithm

Deng¹,

Lin²

2023

Int. J. Emerg. Technol. Learn.

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The strength of college students' learning enthusiasm directly affects their learning effect. Studying and predicting students' learning enthusiasm has positive significance for improving college students themselves and college education. Existing methods have not involved how to select and extract features related to students' learning enthusiasm, so it is difficult to meet the needs of learning enthusiasm prediction. Therefore, this article takes English learning as an example to conduct a research on the prediction model of students' learning enthusiasm based on combinatorial optimization algorithm. Taking English learning as an example, this article expounds in detail the main manifestations of students' learning enthusiasm, and predicts the intention of students to participate in learning behaviors based on the enumerated behaviors, so as to judge students' learning enthusiasm, and gives the construction method of the prediction model. This study constructs a network model for the evaluation of students' learning autonomy, and finally outputs the grade results of students' learning autonomy evaluation. Based on the obtained predictions of students' participation in learning behavior intentions and the evaluation results of students' learning autonomy, a combined model is established through the weighting of the inverted error method to predict the regression of students' learning enthusiasm. Experimental results verify the effectiveness of the constructed single model and combined model.

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