E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analyt...E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.展开更多
Under the macro background of rapid urbanization and social transformation in China,campus space renewal has become an important practice and carrier for the sustainable development of schools.The study on campus spac...Under the macro background of rapid urbanization and social transformation in China,campus space renewal has become an important practice and carrier for the sustainable development of schools.The study on campus space by big data and quantitative reflection of spatial distribution of applicable people in different areas of the campus can provide a certain scientific basis for campus space updating.West campus of Yangtze University is taken as research object.Based on cognitive map method,questionnaire survey method,behavior trajectory and correlation analysis method,the types and characteristics of campus space composition,campus satisfaction,usage and its relevance are analyzed.Research results show that ①the overall imageability of campus space is higher,which has a significantly positive correlation with the satisfaction of campus environment,and has no correlation with users’ behavior activities.The use frequency of non teaching areas varies greatly in different periods of time.②The correlation between the surrounding green vegetation and the image degree of campus landmarks is the most significant,and the coefficient is 0.886.③The correlation between spatial size suitability and regional image degree is the most significant,and the coefficient is 0.937.④The correlation between public activity facilities in the region and node image degree is the most significant,and the coefficient is 0.995.According to the research results,the corresponding solutions are put forward to provide scientific and quantitative reference suggestions for the renewal and transformation of the campus.展开更多
he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads t...he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads the learner to lose interest in programming or even give up.Emotion plays a crucial role in learning.Educational psychology research shows that positive emotion can promote learning performance,increase learning interest and cultivate creative thinking.Accurate recognition and interpretation of programming learners’emotions can give them feedback in time,and adjust teaching strategies accurately and individually,which is of considerable significance to improve effects of programming learning and education.The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic,mouse interaction data and interaction logs,respectively.However,none of the three studies considered the temporal characteristics of emotion,resulting in low recognition accuracy.For the first time,this paper proposes an emotion prediction model based on time series and context information.Then,we establish a Bi-recurrent neural network,obtain the time sequence characteristics of data automatically,and explore the application of deep learning in the field of Academic Emotion prediction.The results show that the classification ability of this model is much better than that of the original LSTM(Long-Short Term Memory),GRU(Gate Recurrent Unit)and RNN(Re-current Neural Network),and this model has better generalization ability.展开更多
The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observat...The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observation data and big human behavior data.A description of big geodata includes,in addition to the“5Vs”(volume,velocity,value,variety and veracity),a further five features,that is,granularity,scope,density,skewness and precision.Based on this approach,the essence of mining big geodata includes four aspects.First,flow space,where flow replaces points in traditional space,will become the new presentation form for big human behavior data.Second,the objectives for mining big geodata are the spatial patterns and the spatial relationships.Third,the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data,namely heterogeneity and homogeneity,may change with scale.Fourth,data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships.The big geodata mining methods may be categorized into two types in view of the mining objective,i.e.,classification mining and relationship mining.Future research will be faced by a number of issues,including the aggregation and connection of big geodata,the effective evaluation of the mining results and the challenge for mining to reveal“non-trivial”knowledge.展开更多
Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how...Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects： malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, prin- ciples, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the in- adequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.展开更多
基金The authors thank to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023017).
文摘E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.
文摘Under the macro background of rapid urbanization and social transformation in China,campus space renewal has become an important practice and carrier for the sustainable development of schools.The study on campus space by big data and quantitative reflection of spatial distribution of applicable people in different areas of the campus can provide a certain scientific basis for campus space updating.West campus of Yangtze University is taken as research object.Based on cognitive map method,questionnaire survey method,behavior trajectory and correlation analysis method,the types and characteristics of campus space composition,campus satisfaction,usage and its relevance are analyzed.Research results show that ①the overall imageability of campus space is higher,which has a significantly positive correlation with the satisfaction of campus environment,and has no correlation with users’ behavior activities.The use frequency of non teaching areas varies greatly in different periods of time.②The correlation between the surrounding green vegetation and the image degree of campus landmarks is the most significant,and the coefficient is 0.886.③The correlation between spatial size suitability and regional image degree is the most significant,and the coefficient is 0.937.④The correlation between public activity facilities in the region and node image degree is the most significant,and the coefficient is 0.995.According to the research results,the corresponding solutions are put forward to provide scientific and quantitative reference suggestions for the renewal and transformation of the campus.
基金supported by the 2018-2020 Higher Education Talent Training Quality and Teaching Reform Project of Sichuan Province(Grant No.JG2018-46)the Science and Technology Planning Program of Sichuan University and Luzhou(Grant No.2017CDLZG30)the Postdoctoral Science fund of Sichuan University(Grant No.2019SCU12058).
文摘he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads the learner to lose interest in programming or even give up.Emotion plays a crucial role in learning.Educational psychology research shows that positive emotion can promote learning performance,increase learning interest and cultivate creative thinking.Accurate recognition and interpretation of programming learners’emotions can give them feedback in time,and adjust teaching strategies accurately and individually,which is of considerable significance to improve effects of programming learning and education.The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic,mouse interaction data and interaction logs,respectively.However,none of the three studies considered the temporal characteristics of emotion,resulting in low recognition accuracy.For the first time,this paper proposes an emotion prediction model based on time series and context information.Then,we establish a Bi-recurrent neural network,obtain the time sequence characteristics of data automatically,and explore the application of deep learning in the field of Academic Emotion prediction.The results show that the classification ability of this model is much better than that of the original LSTM(Long-Short Term Memory),GRU(Gate Recurrent Unit)and RNN(Re-current Neural Network),and this model has better generalization ability.
基金National Natural Science Foundation of China,No.41525004,No.41421001。
文摘The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observation data and big human behavior data.A description of big geodata includes,in addition to the“5Vs”(volume,velocity,value,variety and veracity),a further five features,that is,granularity,scope,density,skewness and precision.Based on this approach,the essence of mining big geodata includes four aspects.First,flow space,where flow replaces points in traditional space,will become the new presentation form for big human behavior data.Second,the objectives for mining big geodata are the spatial patterns and the spatial relationships.Third,the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data,namely heterogeneity and homogeneity,may change with scale.Fourth,data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships.The big geodata mining methods may be categorized into two types in view of the mining objective,i.e.,classification mining and relationship mining.Future research will be faced by a number of issues,including the aggregation and connection of big geodata,the effective evaluation of the mining results and the challenge for mining to reveal“non-trivial”knowledge.
基金Project supported by the National Natural Science Foundation of China(No.61472437)
文摘Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects： malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, prin- ciples, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the in- adequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.
