Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting stud...Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.展开更多
We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand th...We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand the evolutionary mechanisms underlying these features,we categorize learners into two classes based on their different distributions of viewing durations,namely lognormal distribution and power law with exponential cutoff.Two random differential equations are provided to describe the growth patterns of viewing durations for the two classes respectively.The expected duration change rate of the learners featured by lognormal distribution is supposed to be dependent on their past duration,and that of the remainder of learners is supposed to be inversely proportional to time.Solutions to the equations predict the features of viewing duration distributions,and those of the hazard function.The equations also reveal the features of memory and memorylessness for the respective viewing behaviors of the two classes.展开更多
文摘Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.
基金supported by the National Natural Science Foundation of China (No. 61773020)the National Education Science Foundation of China (No. DIA180383)
文摘We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand the evolutionary mechanisms underlying these features,we categorize learners into two classes based on their different distributions of viewing durations,namely lognormal distribution and power law with exponential cutoff.Two random differential equations are provided to describe the growth patterns of viewing durations for the two classes respectively.The expected duration change rate of the learners featured by lognormal distribution is supposed to be dependent on their past duration,and that of the remainder of learners is supposed to be inversely proportional to time.Solutions to the equations predict the features of viewing duration distributions,and those of the hazard function.The equations also reveal the features of memory and memorylessness for the respective viewing behaviors of the two classes.
