The Influence of Teacher-Student Interaction on the Effects of Online Learning: Based on a Serial Mediating Model.

During the COVID-19 pandemic, online education has become an important approach to learning in the information era and an important research topic in the field of educational technology as well as that of education in general. Teacher-student interaction in online education is an important factor affecting students' learning performance. This study employed a questionnaire survey to explore the influence of teacher-student interaction on learning effects in online education as well as the mediating role of psychological atmosphere and learning engagement. The study involved 398 college students studying at Chinese universities as the research object. Participants filled out a self-report questionnaire. The study found that (1) the level of teacher-student interaction positively affected students' learning effects (r = 0.649, p < 0.01). (2) The psychological atmosphere mediated the positive effect of the level of teacher-student interaction on learning effects with mediating effect value of 0.1248. (3) Learning engagement mediated the positive effect of teacher-student interaction on learning effects with a mediating effect value of 0.1539. (4) The psychological atmosphere and learning engagement play a chain-mediating role in the mechanism of teacher-student interaction affecting students' learning effects; that is, teacher-student interaction promotes students' learning engagement by creating a good psychological atmosphere, which, in turn, influences learning effects. The mediating effect value was 0.0403. The results indicate that teacher-student interaction not only directly affects students' learning effects but also influences students' learning effects through the mediating effect of the psychological atmosphere and learning engagement.

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.

[Change pattern of heartwood of Larix olgensis plantation.] / 长白落叶松人工林心材变化规律.

Based on the data of 1179 discs and whorls of 49 trees from larch (Larix olgensis) plantations located in Mengjiagang forest farm in Heilongjiang Province, China, we analyzed the longitudinal variation pattern of heartwood radius. The results showed that the heartwood radius decreased with the increases of tree height, which was basically the same as the trunk shape. The relationship between the xylem radius (XR), diameter at breast height (DBH) and cambial age (CA) with the heartwood radius was significant. The stepwise regression analysis was used to develop heartwood radius (HR) and heartwood area (HA) models: HR=b1+b2XR2+b3CA+b4XR, HA=b1+b2DBH·XR+b3CA+b4DBH·XR2. We used the evaluation statistics such as AIC, BIC, Log Likelihood and Likelihood ratio test to compare the heartwood radius and heartwood area models which fitted with the plot effect and tree effect. The heartwood radius and heartwood area models with parameters b1, b2, b3 as mixed effects performed best when the tree effect was considered. The prediction accuracy of the mixed model was better than that of the basic model. In the application, the total heartwood radius and area could be predicted by the mixed model. Beta regression model was used to simulate the heartwood proportion. In this model, all parameters were significant, and the coefficients of determination were relatively high, with a good simulation effect.