In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes consid...In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not al...Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.展开更多
Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects the...Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects their online learning outcomes when teachers use online teaching media for communication.This sense specifically pertains to the extent to which students perceive themselves as“real persons”and establish connections with others.Therefore,this study constructs a conceptual model elucidating the impact of presence on students’online learning outcomes and empirically examines the mechanism through which three types of presence influence students’online learning.The test results of the structural equation modeling(SEM)indicate that:(a)teaching presence,social presence,and cognitive presence all exhibit significantly positive outcomes on students’online learning outcomes;(b)these three types of presence can also indirectly and positively influence students’online learning outcomes through the mediating effect of flow experience and learning satisfaction;and(c)flow experience and learning satisfaction play a sequential mediating role in the process by which presence impacts students’online learning outcomes.We hope that the relevant research findings may contribute to unveiling the“black box”of the impact of presence on students’online learning outcomes and offer valuable insights for college educators to overcome online teaching constraints and enhance online teaching quality.展开更多
Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-f...Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.展开更多
[Objectives]To explore the current situation and effects of online learning from the perspective of students,as well as the learning dynamics,and to explore the online teaching methods.It is possible to put forward re...[Objectives]To explore the current situation and effects of online learning from the perspective of students,as well as the learning dynamics,and to explore the online teaching methods.It is possible to put forward relevant suggestions for the problems presented by online teaching,find effective teaching methods,and explore appropriate online teaching methods.[Methods]The nursing students of the 2020,2021,and 2022 grades of Chengde Nursing Vocational College were selected as the research subjects.A self-made questionnaire survey method was adopted.The questionnaire had 5 items and 44 questions:3 questions on personal information,9 questions on the teacher level,12 ques-tions on the student level,7 questions on the technical level,and 13 questions on online learning satisfaction.[Results]In the process of on-line teaching,the cooperation between family and school can be carried out to give full play to the important role of family supervision in online learning and other education,which is conducive to maintaining the discipline of online courses on the Internet,increasing the online learning effect of students,and avoiding the temptation of the Internet to the greatest extent.In the process,we should increase students'self-control in online learning,help students shape realistic goals,and thus improve the effect of online learning.Technical level:increase information-based teaching,enrich teaching content,and introduce virtual simulation software to simulate clinical operations,so as to increase students’interest and enthusiasm in learning.[Conclusions]This study is expected to provide a certain reference for the smooth and efficient development of online teaching and online learning skills.展开更多
The shift towards online intelligent learning has become the norm in education and is now a fundamental part of modern educational activities.However,this new model can influence students’learning behavior and lead t...The shift towards online intelligent learning has become the norm in education and is now a fundamental part of modern educational activities.However,this new model can influence students’learning behavior and lead to changes in their approach to learning.Based on online intelligent learning,we investigated how the academic self-efficacy of nursing students affects their engagement with learning and explored the role of academic attribution as a mediator.Five hundred fifty-three nursing college students from Hebei and Hunan provinces in China participated in the online questionnaire.The results revealed that effort plays a mediating role in the relationship between academic self-efficacy and learning engagement.展开更多
To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong Uni...To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.展开更多
In the context of internationalization,China-UK Joint Education Programs are receiving increasing attention from universities.Based on the difficulties faced in China-UK Joint Education Program,this paper adopts a que...In the context of internationalization,China-UK Joint Education Programs are receiving increasing attention from universities.Based on the difficulties faced in China-UK Joint Education Program,this paper adopts a questionnaire survey method to study the learning effectiveness of students majoring in digital media technology in the China-UK Joint Education Program at Guangxi University of Finance and Economics,focusing on four aspects:learning materials,learning content,teacher conditions,and student learning outcomes.The research analysis in this paper not only provides strong support for the construction of China-UK Joint Education Program but also offers references for other China-UK Joint Education Programs.展开更多
Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficien...Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.展开更多
Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confronta...Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.展开更多
In emerging applications such as industrial control and autonomous driving,end-to-end deterministic quality of service(QoS)transmission guarantee has become an urgent problem to be solved.Internet congestion control a...In emerging applications such as industrial control and autonomous driving,end-to-end deterministic quality of service(QoS)transmission guarantee has become an urgent problem to be solved.Internet congestion control algorithms are essential to the performance of applications.However,existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements.To enable data delivery within application QoS constraints,we leverage an online learning mechanism to design Crimson,a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined QoS.Crimson can change rates adaptively that satisfy application QoS requirements as a result.Across many emulation environments and real-world experiments,our proposed scheme can efficiently balance the different trade-offs between throughput,delay and loss rate.Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.展开更多
BACKGROUND The coronavirus disease 2019(COVID-19)epidemic disrupted education systems by forcing systems to shift to emergency online leaning.Online learning satisfaction affects academic achievement.Many factors affe...BACKGROUND The coronavirus disease 2019(COVID-19)epidemic disrupted education systems by forcing systems to shift to emergency online leaning.Online learning satisfaction affects academic achievement.Many factors affect online learning satisfaction.However there is little study focused on personal characteristics,mental status,and coping style when college students participated in emergency online courses.regression analyses were performed to identify factors that affected online learning satisfaction.RESULTS Descriptive findings indicated that 62.9%(994/1580)of students were satisfied with online learning.Factors that had significant positive effects on online learning satisfaction were online learning at scheduled times,strong exercise intensity,good health,regular schedule,focusing on the epidemic less than one hour a day,and maintaining emotional stability.Positive coping styles were protective factors of online learning satisfaction.Risk factors for poor satisfaction were depression,neurasthenia,and negative coping style.CONCLUSION College students with different personal characteristics,mental status,and coping style exhibited different degrees of online learning satisfaction.Our findings provide reference for educators,psychologists,and school adminis-trators to conduct health education intervention of college students during emergency online learning.展开更多
As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalizatio...As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.展开更多
As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended lear...As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended learning outcomes, especially for practical-based subjects. In this research, students having online classes of a practical-based fabric design subject were encouraged to self-study from Open Educational Resource (OER) materials for a further and better understanding of their subject. Additionally, online materials were developed to improve students’ understanding via skill of digital literacy. Their learning progress was evaluated and compared to the face-to-face version. The majority of students found online classes combined with self-studying OER materials, potentially be a substitute for face-to-face classes. Most of the students further opined different OER videos assisted them without any face-to-face instructions in practical works, to develop new fabric samples from the inspiration. Analysis of test results, and comparison of students’ final grades with different learning modes, supported these phenomena.展开更多
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 stu...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.展开更多
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’ ...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.展开更多
With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information w...With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information with the help of the media.In the Internet era,college students,overwhelmed by complex information and a lack of information discernment,are susceptible to indulging in the curated online world presented by others.At the same time,negative information such as false,violent,and pornographic also spread rapidly in various media.These phenomena impact students’media literacy and affect their mental health,thereby leading to learning burnout.This study analyzes the current situation of learning burnout among university students,explores the effective path of improving online education of college students,and provides a theoretical basis for reducing the burnout of college students and assisting students in developing a positive mentality.展开更多
An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontol...An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.展开更多
Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi...Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.展开更多
基金supported by the National Natural Science Foundation of China(61771372,61771367,62101494)the National Outstanding Youth Science Fund Project(61525105)+1 种基金Shenzhen Science and Technology Program(KQTD20190929172704911)the Aeronautic al Science Foundation of China(2019200M1001)。
文摘In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX20_0733)Education Reform Foundation of Jiangsu Province(Grant No.2021JSJG364)+1 种基金Key Education Reform Foundation of NJUPT(Grant No.JG00220JX02,JG00218JX03,JG00215JX01,JG00214JX52)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.
基金the project“Research on the Evaluation Mechanism of College Ideological and Political Education:A Perspective on Teacher-Student Development,”funded by Zhejiang Provincial College Ideological and Political Education Research Project.
文摘Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects their online learning outcomes when teachers use online teaching media for communication.This sense specifically pertains to the extent to which students perceive themselves as“real persons”and establish connections with others.Therefore,this study constructs a conceptual model elucidating the impact of presence on students’online learning outcomes and empirically examines the mechanism through which three types of presence influence students’online learning.The test results of the structural equation modeling(SEM)indicate that:(a)teaching presence,social presence,and cognitive presence all exhibit significantly positive outcomes on students’online learning outcomes;(b)these three types of presence can also indirectly and positively influence students’online learning outcomes through the mediating effect of flow experience and learning satisfaction;and(c)flow experience and learning satisfaction play a sequential mediating role in the process by which presence impacts students’online learning outcomes.We hope that the relevant research findings may contribute to unveiling the“black box”of the impact of presence on students’online learning outcomes and offer valuable insights for college educators to overcome online teaching constraints and enhance online teaching quality.
文摘Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
基金Supported by General Project"Research on the Current Status,Effects and Satisfaction of Online Learning of Nursing Related Courses Based on Students'Perspectives under the Epidemic Situation"(Y202214).
文摘[Objectives]To explore the current situation and effects of online learning from the perspective of students,as well as the learning dynamics,and to explore the online teaching methods.It is possible to put forward relevant suggestions for the problems presented by online teaching,find effective teaching methods,and explore appropriate online teaching methods.[Methods]The nursing students of the 2020,2021,and 2022 grades of Chengde Nursing Vocational College were selected as the research subjects.A self-made questionnaire survey method was adopted.The questionnaire had 5 items and 44 questions:3 questions on personal information,9 questions on the teacher level,12 ques-tions on the student level,7 questions on the technical level,and 13 questions on online learning satisfaction.[Results]In the process of on-line teaching,the cooperation between family and school can be carried out to give full play to the important role of family supervision in online learning and other education,which is conducive to maintaining the discipline of online courses on the Internet,increasing the online learning effect of students,and avoiding the temptation of the Internet to the greatest extent.In the process,we should increase students'self-control in online learning,help students shape realistic goals,and thus improve the effect of online learning.Technical level:increase information-based teaching,enrich teaching content,and introduce virtual simulation software to simulate clinical operations,so as to increase students’interest and enthusiasm in learning.[Conclusions]This study is expected to provide a certain reference for the smooth and efficient development of online teaching and online learning skills.
基金Research Project on Education and Teaching Reform at Hebei University of Chinese Medicine(22yb-45)Hebei Province Higher Education Teaching Reform Research and Practice Project(2021GJJG278)。
文摘The shift towards online intelligent learning has become the norm in education and is now a fundamental part of modern educational activities.However,this new model can influence students’learning behavior and lead to changes in their approach to learning.Based on online intelligent learning,we investigated how the academic self-efficacy of nursing students affects their engagement with learning and explored the role of academic attribution as a mediator.Five hundred fifty-three nursing college students from Hebei and Hunan provinces in China participated in the online questionnaire.The results revealed that effort plays a mediating role in the relationship between academic self-efficacy and learning engagement.
文摘To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.
基金Guangxi Key Laboratory of Financial Big Data Fund Project(Guikejizi[2021]No.5)Research on the Innovation of Teaching Models for Foreign Professional Courses in China-UK Joint Education Under the Background of Internationalization-Taking Guangxi University of Finance and Economics as an Example(2023XJJG26)Exploration and Practice of Digital Media Technology Talent Training Models in the Context of New Productive Forces(XGK202423)。
文摘In the context of internationalization,China-UK Joint Education Programs are receiving increasing attention from universities.Based on the difficulties faced in China-UK Joint Education Program,this paper adopts a questionnaire survey method to study the learning effectiveness of students majoring in digital media technology in the China-UK Joint Education Program at Guangxi University of Finance and Economics,focusing on four aspects:learning materials,learning content,teacher conditions,and student learning outcomes.The research analysis in this paper not only provides strong support for the construction of China-UK Joint Education Program but also offers references for other China-UK Joint Education Programs.
基金Analysis and Research on Online Learning in Higher Vocational Colleges Based on Kirkpatrick Model-Taking the Course of Physiology as an Example(Project No.:D/2021/03/91)The excellent teaching team of Physiology of Suzhou Vocational College of Health Science and Technology in 2019(Project number:JXTD201912).
文摘Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.
基金the support of the Fundamental Research Funds for the Air Force Engineering University under Grant No.XZJK2019040。
文摘Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.
基金supported by the National Natural Science Foundation of China under Grant 62132009 and 61872211。
文摘In emerging applications such as industrial control and autonomous driving,end-to-end deterministic quality of service(QoS)transmission guarantee has become an urgent problem to be solved.Internet congestion control algorithms are essential to the performance of applications.However,existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements.To enable data delivery within application QoS constraints,we leverage an online learning mechanism to design Crimson,a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined QoS.Crimson can change rates adaptively that satisfy application QoS requirements as a result.Across many emulation environments and real-world experiments,our proposed scheme can efficiently balance the different trade-offs between throughput,delay and loss rate.Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.
基金The study protocol was approved by the Ethics Committee of Hebei General University and complied strictly with ethical requirements.Ethics Review No.2020 scientific ethics No.30.
文摘BACKGROUND The coronavirus disease 2019(COVID-19)epidemic disrupted education systems by forcing systems to shift to emergency online leaning.Online learning satisfaction affects academic achievement.Many factors affect online learning satisfaction.However there is little study focused on personal characteristics,mental status,and coping style when college students participated in emergency online courses.regression analyses were performed to identify factors that affected online learning satisfaction.RESULTS Descriptive findings indicated that 62.9%(994/1580)of students were satisfied with online learning.Factors that had significant positive effects on online learning satisfaction were online learning at scheduled times,strong exercise intensity,good health,regular schedule,focusing on the epidemic less than one hour a day,and maintaining emotional stability.Positive coping styles were protective factors of online learning satisfaction.Risk factors for poor satisfaction were depression,neurasthenia,and negative coping style.CONCLUSION College students with different personal characteristics,mental status,and coping style exhibited different degrees of online learning satisfaction.Our findings provide reference for educators,psychologists,and school adminis-trators to conduct health education intervention of college students during emergency online learning.
文摘As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.
文摘As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended learning outcomes, especially for practical-based subjects. In this research, students having online classes of a practical-based fabric design subject were encouraged to self-study from Open Educational Resource (OER) materials for a further and better understanding of their subject. Additionally, online materials were developed to improve students’ understanding via skill of digital literacy. Their learning progress was evaluated and compared to the face-to-face version. The majority of students found online classes combined with self-studying OER materials, potentially be a substitute for face-to-face classes. Most of the students further opined different OER videos assisted them without any face-to-face instructions in practical works, to develop new fabric samples from the inspiration. Analysis of test results, and comparison of students’ final grades with different learning modes, supported these phenomena.
文摘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.
文摘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.
文摘With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information with the help of the media.In the Internet era,college students,overwhelmed by complex information and a lack of information discernment,are susceptible to indulging in the curated online world presented by others.At the same time,negative information such as false,violent,and pornographic also spread rapidly in various media.These phenomena impact students’media literacy and affect their mental health,thereby leading to learning burnout.This study analyzes the current situation of learning burnout among university students,explores the effective path of improving online education of college students,and provides a theoretical basis for reducing the burnout of college students and assisting students in developing a positive mentality.
基金The Advanced University Action Plan of the Minis-try of Education of China (2004XD-03).
文摘An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.
基金supported by the National Science Foundation of China under Grant No.62101467.
文摘Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.
