近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学...近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。展开更多
Raman spectra and ultraviolet-visible（UV-Vis） absorption spectra of linear polyene molecule-canthaxan-thin in n-hexane are measured and analyzed.In addition,the optimized structure of canthaxanthin was calculated vi...Raman spectra and ultraviolet-visible（UV-Vis） absorption spectra of linear polyene molecule-canthaxan-thin in n-hexane are measured and analyzed.In addition,the optimized structure of canthaxanthin was calculated via density functional theory（DFT） functional B3LYP.With decreasing the concentration,Raman scattering cross section （RSCS） of fundamental frequency is extremely high,and the UV-Vis absorption bands become narrower.The results of coherent weakly damped electron-Lattice vibration model were analyzed.展开更多
We analyzed the properties and structures of the hydrogen-bonded complexes of tetrahydrofuran（THF） and water by means of experimental Raman spectra and ab initio calculations.The optimized geometries and vibrational...We analyzed the properties and structures of the hydrogen-bonded complexes of tetrahydrofuran（THF） and water by means of experimental Raman spectra and ab initio calculations.The optimized geometries and vibrational frequencies of the neat THF molecule and its hydrogen-bonded complexes with water（THF/H2O） were calculated at the MP2/6-311＋G（d,p） level of theory.We found that the intermolecular hydrogen bonds which are formed from the binary mixtures of the neat THF and water with different molar ratios could explain the changes in wavenumber position and linewidth very well.The combination of ab initio calculations and experimental Raman spectral data provides an insight into the hydrogen bonds leading to the concentration dependent changes in the spectral features.展开更多
文摘近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。
基金Supported by the National Natural Science Foundation of China(No.10974067)the Natural Science Foundation of Jilin Province,China(No.20101508)the Technology Development Projects of Jilin Province,China(No.20090534)
文摘Raman spectra and ultraviolet-visible（UV-Vis） absorption spectra of linear polyene molecule-canthaxan-thin in n-hexane are measured and analyzed.In addition,the optimized structure of canthaxanthin was calculated via density functional theory（DFT） functional B3LYP.With decreasing the concentration,Raman scattering cross section （RSCS） of fundamental frequency is extremely high,and the UV-Vis absorption bands become narrower.The results of coherent weakly damped electron-Lattice vibration model were analyzed.
基金Supported by the National Natural Science Foundation of China(Nos.20303007,20333050,20973077)the Program for New Century Excellent Talents in University,China and the Graduate Innovation Fund of Jilin University,China(No.20101046)
文摘We analyzed the properties and structures of the hydrogen-bonded complexes of tetrahydrofuran（THF） and water by means of experimental Raman spectra and ab initio calculations.The optimized geometries and vibrational frequencies of the neat THF molecule and its hydrogen-bonded complexes with water（THF/H2O） were calculated at the MP2/6-311＋G（d,p） level of theory.We found that the intermolecular hydrogen bonds which are formed from the binary mixtures of the neat THF and water with different molar ratios could explain the changes in wavenumber position and linewidth very well.The combination of ab initio calculations and experimental Raman spectral data provides an insight into the hydrogen bonds leading to the concentration dependent changes in the spectral features.
