This study examines the impact of education quality and innovative activities on economic growth in Shanghai through international trade and fixed asset formation. The study examines how higher education quality and innovation activities drive regional economic growth, with a focus on the mediating effects of international trade and fixed asset formation in Shanghai. The study adopts a quantitative approach utilizing panel data from 31 provinces in China covering the period from 1999 to 2022. The study incorporates variables such as education quality, innovation capacity, and GDP per capita, as well as control variables like labor, capital, and infrastructure. The methodology involves multiple regression models and robustness tests to verify the relationships between and effects of education quality and innovation with regard to economic growth. This study analyzes the direct and indirect effects of university R&D expenditure and innovation on economic growth using a regression model, based on data from 2014 to 2022 in relation to Shanghai. The model introduces variables such as international trade, capital formation, and urbanization to analyze the relationship between higher education quality and economic growth.

In this Data science research on Education, it analyses the alcohol consumption, parent’s education, study time and other factors may influence on student performance.

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.

In order to study the temperature change trend of the surrounding geotechnical soil during the operation and thermal recovery of the medium-deep geothermal buried pipe and the influence of the geotechnical soil on the operational stability of the vertical buried pipe after thermal recovery. Based on the data of geological stratum in Guanzhong area and the actual engineering application of medium-deep geothermal buried pipe heating system in Xi’an New Area, the influence law of medium-deep geothermal buried pipe heat exchanger on surrounding geotechnical soil is simulated and analyzed by FLUENT software. The results show that: after four months of heating operation, in the upper layer of the geotechnical soil, the reverse heat exchange zone appears due to the higher fluid temperature; in the lower layer of the geotechnical soil, the temperature decreases more with the increase of depth and shows a linear increase in the depth direction; without considering the groundwater seepage, after eight months of thermal recovery of the geotechnical soil after heating, the maximum temperature difference after recovery is 3.02 ℃, and the average temperature difference after recovery is 1.30 ℃ The maximum temperature difference after recovery was 3.02 ℃ and the average temperature difference after recovery was 1.30 ℃. The geotechnical thermal recovery temperature difference has no significant effect on the long-term operation of the buried pipe, and it can be operated continuously and stably for a long time. Practice shows that due to the influence of various factors such as stratigraphic structure, stratigraphic pressure, radioactive decay and stratigraphic thermal conductivity, the actual stratigraphic temperature below 2000m recovers rapidly without significant temperature decay, fully reflecting the characteristics of the Earth’s constant temperature body.