Research and Practice of the Blended Teaching Model of BOPPPS Teaching Method Under the Background of Digital Education: Taking Operations Research Course as an Example

The rapid development of digital education provides new opportunities and challenges for teaching model innovation.This study aims to explore the application of the BOPPPS(Bridge-in,Objective,Pre-assessment,Participatory learning,Post-assessment,Summary)teaching method in the development of a blended teaching model for the Operations Research course under the background of digital education.In response to the characteristics of the course and the needs of the student group,the teaching design is reconstructed with a student-centered approach,increasing practical teaching links,improving the assessment and evaluation system,and effectively implementing it in conjunction with digital educational technology.This teaching model has shown significant effectiveness in the context of digital education,providing valuable experience and insights for the innovation of the Operations Research course.

Influence of Online Learning Attitude in the Blended Teaching Model on the Final Grades

The combination of online teaching and traditional offline teaching can maximize the advantages of both.Based on the blended teaching of English Reading course,39 students were selected as the research subjects to study the relationship between their online learning attitudes and their grades in the final examination.Judged from the number of times for each student to download teaching resources,the number of assignments submitted online,and the quality of the submitted assignments,each student’s attitude toward online learning was examined comprehensively,and a correlation analysis was conducted through SPSS Statistics 21.0 to explore the influence of online learning attitude on English reading performance.Through data collection and analysis of the online learning attitudes over a 16-week period,a significant positive correlation was found between the online learning attitudes and the English reading grades,indicating that the online learning attitude in the blended learning model plays a crucial role in improving the English reading skill,and students should maintain a positive attitude toward online teaching in blended learning.

A Brief Study on the Design of Higher Vocational English Classroom Based on Blended Teaching Model

This article takes an English classroom teaching design about healthy living as an example to demonstrate a blended learning mode,which based on learning new words and practicing oral English.The purpose of this teaching design is to let stu⁃dents memorize new vocabulary and practice spoken English in the classroom.We will use"Wenjuanxing"platform to design an English questionnaire about healthy life as the homework of this class,and finally hope that the students can learn English in the relaxed atmosphere and fall in love with English.

Diesel molecular composition and blending modeling based on SUBEM framework

Diesel molecular compositional model has important application for diesel quality prediction,blending,and molecular-level process model development.In this paper,different types of diesel molecular compositional and blending models were constructed based on the SU-BEM framework.More than 1500 representative molecules were selected to form the molecular structure library.The probability density functions(PDFs)combination was determined by experimental data and experience.A quadratic optimization strategy combining genetic algorithm with local optimization algorithm was adopted to improve the accuracy of the compositional model.The model results show good agreement with the experimental data.The diesel blending model was constructed at the molecular-level based on the above diesel compositional models.The properties of the blending model accord with the experimental regulations.It is proved that the compositional models and blending model constructed have high accuracy and strong prediction capability,and are applicable to the industrial process.

REVERSE MODELING FOR CONIC BLENDING FEATURE

A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.

Research on The Blended Teaching Mode of College English Based on Cloud Platform

With the development of domestic higher education towards the popularization of education and the integration of global economy,as a basic course in colleges and universities,college English has been playing an important role in the process of higher education.Therefore,under the guidance of scientific and technological innovation and information technology,it is necessary and worthy trying to explore and establish a practical English blended teaching mode to improve college students’English comprehen⁃sive ability.This essay attempts to discuss the problems and reflections of blended teaching mode,explore the college English teaching strategy under the blended teaching mode,which establishes the practical English blended teaching mode,promote teach⁃ing equity and benefit teachers and students.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

“金课”视域下高校思想政治理论课混合式教学模式研究

混合式教学模式是实现思想政治理论课“金课”建设的有效模式。基于“金课”视域的高校思想政治理论课混合式教学模式立足于创建、实施、评价等三个维度。通过构建分工明确的教师共同体、建设线上课程教学资源、打造互利共赢的思政教学平台等,协同推进线上、线下、实践三维课堂的“金课”建设。依托混合式教学模式对教师、学生、课程设置考核评价体系适应了新时代课程改革要求。

Spatiotemporal reflectance blending in a wetland environment

To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency,this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area,China,using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005.The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model(ESTARFM).Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM.Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance.It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is<2.68 m to achieve blending accuracy with a mean average absolute difference of 4%between the observed and blended reflectance in all spectral bands.