Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment:Learning Experience as a Mediator

Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.

Application Effect of Blended Education Strategy Based on the Learning Pass Platform in Phase III Cardiac Rehabilitation of Patients with Coronary Artery Disease

Objective:To explore the application effect of the blended education strategy based on the Learning Pass platform in the phase III cardiac rehabilitation of patients with coronary artery disease.Methods:90 patients diagnosed with coronary artery disease in the Department of Cardiology of our hospital from January 2019 to January 2021 were selected and divided into the control group and the experimental group according to the method of randomized numerical table,with 45 cases in each group.Both the experimental group and the control group received pre-discharge cardiac rehabilitation education by conventional means.The control group received education and supervision information via WeChat after discharge,while the experimental group joined the Learning Pass platform to receive online and offline hybrid education and supervision,with online as the mainstay and offline as a supplement.The disease cognitive level,self-management skills,quality of life,medication adherence,and emotional status of the two groups were compared.Results:The disease cognitive levels in the experimental group were significantly higher than those of the control group(P<0.05);the scores of the experimental group in terms of quality of life,self-management skills,and medication adherence were significantly higher than those of the control group(P<0.05);and the scores of anxiety and depression in the experimental group were significantly lower than those of the control group(P<0.05).Conclusion:The blended education strategy based on the Learning Pass platform has a significant application effect in phase III cardiac rehabilitation of patients with coronary artery disease.It can improve patients’disease cognitive level,self-management skills,and quality of life,and provide a basis for improving patients’prognosis.

A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.

Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple Machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.

A Research of the Course “Taishan Cultural Communication with the World” under Blended Learning Model and Outcome-Based Education Concept

The course “Taishan Cultural Communication with the World” has been online and offline teaching and learning for two terms based on the theoretical ideas: Blended Learning and Outcome-Based Education. This paper uses the data from one semester to state how to carry out the program and the good results. At the same time disadvantages are also the points that should be taken into consideration. From the teaching and learning practice, students have benefited from the online videos, complementary materials and discussions;they need to be guided as well, especially the guidance offline to make up. Furthermore, the balance of time online and offline is a great challenge.

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.

An Empirical Study of the Influence of Online Learning Behaviors on Learning Outcomes in Blended Teaching Mode—Taking College English Listening as an Example

Blended teaching, which integrates the advantages of online and offline teaching, has become the main direction of higher education teaching reform. In the era of education big data, research on the online learners’ behavior based on data mining has attracted more and more attention from higher education researchers. However, in the field of foreign language teaching, research on the relationship between online learning behaviors and learning outcomes in the blended teaching mode is still at an early stage. Taking the course College English Listening in Zhejiang Yuexiu University (ZYU) as an example, this study conducted a comprehensive data analysis of online learning behaviors of 152 students of ZYU to explore the influence of online learning behaviors on learning outcomes in the blended teaching mode by utilizing Microsoft Excel and SPSS.20 statistic software. The result shows that the number of course login, the quantity and the quality of forum replies, the number of note submission, the quality of the notes, the average score of vocabulary tests, the number of the times of taking listening tests and the average score of listening tests are all significantly and positively correlated with students’ learning outcomes, while the study does not find a correlation between students’ learning outcomes and the number of the times of taking vocabulary tests, the total length of online learning and the length of video viewing. Based on the study results, implications are put forward to give reference for the teaching design and the management of the foreign language blended courses.

Research on Blended Teaching Design Based on Deep Learning

In recent years,the blended teaching model in Chinese universities has developed rapidly,breaking through the time and space constraints of the teaching process and enriching teaching resources.The emergence of different teaching platforms has also actively promoted the modern development of education.However,there is still room for continuous optimization and development of blended learning,and issues such as some students’inability to effectively utilize online resources,inability to conduct in-depth learning,lack of thinking training,and insufficient ability cultivation and improvement require resolution.This article fully utilizes the basic concepts of deep learning in the existing blended teaching mode,proposes a blended teaching design based on deep learning,and enriches the existing student assessment and evaluation methods,thereby improving the teaching effectiveness of existing blended teaching,deepening the achievements of teaching reform,and improving teaching quality.

An Analysis of the Research Trends in Blended Learning Methods in Teaching College English Reading Courses

Curtis J.Bonk defined blended learning as a combination of face-to-face and computer-assisted online learning instruction since it was gradually formed after the emergence of the Internet[1,2].Breen pointed out that blended learning is a learning method that combines online and offline learning activities and resources[3].The blended learning approach is popular in school and e-learning.Moreover,it is one of the important trends in promoting higher education reform in the coming years.Therefore,it has attracted the attention of international researchers[4].In this paper,42 articles in Scopus(established by international institutions)and 30 articles in CNKI(found by Chinese mainland institutions)on the query of“college English reading blended learning”in the field of education were analyzed.Furthermore,CiteSpace software was used to analyze the research hotspots and trends with the keyword clusters and citations that occurred in the last two decades(1999 to 2022)to foresee future research prospects.

Application Effect of Blended Education Strategy Based on the Learning Pass Platform in Phase III Cardiac Rehabilitation of Patients with Coronary Artery Disease

Objective:To explore the application effect of the blended education strategy based on the Learning Pass platform in the phase III cardiac rehabilitation of patients with coronary artery disease.Methods:90 patients diagnosed with coronary artery disease in the Department of Cardiology of our hospital from January 2019 to January 2021 were selected and divided into the control group and the experimental group according to the method of randomized numerical table,with 45 cases in each group.Both the experimental group and the control group received pre-discharge cardiac rehabilitation education by conventional means.The control group received education and supervision information via WeChat after discharge,while the experimental group joined the Learning Pass platform to receive online and offline hybrid education and supervision,with online as the mainstay and offline as a supplement.The disease cognitive level,self-management skills,quality of life,medication adherence,and emotional status of the two groups were compared.Results:The disease cognitive levels in the experimental group were significantly higher than those of the control group(P<0.05);the scores of the experimental group in terms of quality of life,self-management skills,and medication adherence were significantly higher than those of the control group(P<0.05);and the scores of anxiety and depression in the experimental group were significantly lower than those of the control group(P<0.05).Conclusion:The blended education strategy based on the Learning Pass platform has a significant application effect in phase III cardiac rehabilitation of patients with coronary artery disease.It can improve patients’disease cognitive level,self-management skills,and quality of life,and provide a basis for improving patients’prognosis.

从大学英语教学透视Blended Learning

本文从大学英语教学和 Blended L earning的产生、发展等多视角透视其概念内涵 ;在计算机及网络技术已经全面且实质性地应用于我国英语教学的今天 ,正确地把握 Blended L earning的本质将有助于教育技术在英语教学改革中发挥越来越重要的作用。

Blended Learning在高校教学改革中的应用研究——以《现代远程教育》课程为例

信息技术在很大程度上改变了高校教学环境和教学条件,但教学理念和教学方法改变却很迟缓。文章以《现代远程教育》课程为例,论述了这门课程从设计、实施到评价的教学改革过程,指出Blended Learning为高校教学改革提供了一个新的思路和方法。

Blended Learning模式在烧伤科临床教学中的应用

混合式学习模式旨在提取传统课堂教学方式与E-Learning教学模式的优点并加以有效整合,随着教学改革的逐步深入,混合式学习模式受到越来越多的关注。文章将结合烧伤科临床教学实际,对依托于Blended Learning模式的教学课程设计进行阐述。

Blended Learning:数字化学习方式的新趋势

Blended Learning是教育技术关注的热点,是传统的课堂学习与E-learning由对立走向融合的产物。文章主要探讨了Blended Learning产生原因及其教学设计的步骤,并通过具体案例进行了说明。