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
Blended PAD model is proposed and practiced in this current research.On the basis of PAD model,this research integrated blended learning into the process.An experimental design was carried out among 88 students(the co...Blended PAD model is proposed and practiced in this current research.On the basis of PAD model,this research integrated blended learning into the process.An experimental design was carried out among 88 students(the control group=45 students,the experimental group=43 students).The learning satisfaction and perceived learning questionnaire showed a significant difference between the control group and the experimental group,indicating a better learning effect of BPAD model than that of the traditional one.Moreover,the independent sample t-test showed that students from the BPAD class got higher significant final exam scores than those from the traditional one.Pedagogical implications and limitations are also discussed.展开更多
Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential rel...Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms:(1)multi-output regressor stacking;(2) multi-output extremely randomized trees; and(3) hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.展开更多
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.
基金The author is grateful to the 2021 Teaching Reform Project of Zhejiang Normal University(NO.36:A Practical Study of PFAD Model Based on Mosotech LMS in College English)for providing financial assistance to carry out this research work。
文摘Blended PAD model is proposed and practiced in this current research.On the basis of PAD model,this research integrated blended learning into the process.An experimental design was carried out among 88 students(the control group=45 students,the experimental group=43 students).The learning satisfaction and perceived learning questionnaire showed a significant difference between the control group and the experimental group,indicating a better learning effect of BPAD model than that of the traditional one.Moreover,the independent sample t-test showed that students from the BPAD class got higher significant final exam scores than those from the traditional one.Pedagogical implications and limitations are also discussed.
基金mainly supported by the National Natural Science Foundation of China(Nos.61125201,61303070,and U1435219)
文摘Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms:(1)multi-output regressor stacking;(2) multi-output extremely randomized trees; and(3) hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.