The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of education.This study utilizes the historical and final lea...The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of education.This study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic year.We have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these outcomes.Our goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures promptly.Experimental results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.展开更多
The brainecomputer interface(BCI)plays an important role in neural restoration.Current BCI systems generally require complex experimental preparation to perform well,but this time-consuming process may hinder their us...The brainecomputer interface(BCI)plays an important role in neural restoration.Current BCI systems generally require complex experimental preparation to perform well,but this time-consuming process may hinder their use in clinical applications.To explore the feasibility of simplifying the BCI system setup,a wearable BCI system based on the steady-state visual evoked potential(SSVEP)was developed and evaluated.Fifteen healthy participants were recruited to test the fast-setup system using dry and wet electrodes in a real-life scenario.In this study,the average system setup time for the dry electrode was 38.40 seconds and that for the wet electrode was 103.40 seconds,which are times appreciably shorter than those in previous BCI experiments,enabling a rapid setup of the BCI system.Although the electroencephalogram(EEG)signal quality was low in this fast-setup BCI experiment,the BCI system achieved an information transfer rate of 138.89 bits/min with an eight-channel wet electrode and an information transfer rate of 70.59 bits/min with an eight-channel dry electrode,showing that the overall performance was close to that in traditional experiments.In addition,the results suggest that the solutions of a multi-channel dry electrode or few-channel wet electrode may be suitable for the fast-setup SSEVP-BCI.This fast-setup SSVEP-BCI has the advantages of simple preparation and stable performance and is thus conducive to promoting the use of the BCI in clinical practice.展开更多
文摘The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of education.This study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic year.We have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these outcomes.Our goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures promptly.Experimental results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.
基金supported by the Shenzhen Science and Technology Program(No.JCYJ20230807113007015 and GJHZ20220913143408015)Sanming Project of Medicine in Shenzhen(No.SZSM202211004)+1 种基金Shenzhen Key Medical Discipline Construction Fund(No.SZXK2020084)Zhanjiang Competitive Allocation of Special Funds for Scientific and Technological Development(No.2022A703-3).
文摘The brainecomputer interface(BCI)plays an important role in neural restoration.Current BCI systems generally require complex experimental preparation to perform well,but this time-consuming process may hinder their use in clinical applications.To explore the feasibility of simplifying the BCI system setup,a wearable BCI system based on the steady-state visual evoked potential(SSVEP)was developed and evaluated.Fifteen healthy participants were recruited to test the fast-setup system using dry and wet electrodes in a real-life scenario.In this study,the average system setup time for the dry electrode was 38.40 seconds and that for the wet electrode was 103.40 seconds,which are times appreciably shorter than those in previous BCI experiments,enabling a rapid setup of the BCI system.Although the electroencephalogram(EEG)signal quality was low in this fast-setup BCI experiment,the BCI system achieved an information transfer rate of 138.89 bits/min with an eight-channel wet electrode and an information transfer rate of 70.59 bits/min with an eight-channel dry electrode,showing that the overall performance was close to that in traditional experiments.In addition,the results suggest that the solutions of a multi-channel dry electrode or few-channel wet electrode may be suitable for the fast-setup SSEVP-BCI.This fast-setup SSVEP-BCI has the advantages of simple preparation and stable performance and is thus conducive to promoting the use of the BCI in clinical practice.
