Mr. Jamyangling Soinam and his friends have raised more than 13 million yuan as a donation to Tibet and other Tibetan areas in China. People who visited him in Sweden reveal that he lives in a small house of less than...Mr. Jamyangling Soinam and his friends have raised more than 13 million yuan as a donation to Tibet and other Tibetan areas in China. People who visited him in Sweden reveal that he lives in a small house of less than 10 square meters, which he leased from others. He has no car....展开更多
There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highe...There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.展开更多
Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map...Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)accreditation.In addition,ABET needs to automatically ensure that the mapping(classification)is reasonable and correct.The classification also plays a vital role in the assessment of students’learning.Since the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of dimensionality.In this work,a novel associative short text classification tech-nique is proposed to map PEOs to SOs.The datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by ABET-EAC.The datasets are processed and transformed into a representational form appropriate for association rule mining.The extracted rules are utilized as delegate classifiers to map PEOs to SOs.The proposed asso-ciative classification of the mapping of PEOs to SOs has shown promising results,which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.展开更多
BACKGROUND The causality between education and type 2 diabetes(T2DM)remains unclear.AIM To identify the causality between education and T2DM and the potential metabolic risk factors[coronary heart disease(CHD),total c...BACKGROUND The causality between education and type 2 diabetes(T2DM)remains unclear.AIM To identify the causality between education and T2DM and the potential metabolic risk factors[coronary heart disease(CHD),total cholesterol,lowdensity lipoprotein,triglycerides(TG),body mass index(BMI),waist circumference(WC),waist-to-hip ratio(WHR),fasting insulin,fasting glucose,and glycated hemoglobin]from summarized genome-wide association study(GWAS)data used a network Mendelian randomization(MR).METHODS Two-sample MR and network MR were performed to obtain the causality between education-T2DM,education-mediator,and mediator-T2DM.Summary statistics from the Social Science Genetic Association Consortium(discovery data)and Neale Lab consortium(replication data)were used for education and DIAGRAMplusMetabochip for T2DM.RESULTS The odds ratio for T2DM was 0.392(95%CI:0.263-0.583)per standard deviation increase(3.6 years)in education by the inverse variance weighted method,without heterogeneity or horizontal pleiotropy.Education was genetically associated with CHD,TG,BMI,WC,and WHR in the discovery phase,yet only the results for CHD,BMI,and WC were replicated in the replication data.Moreover,BMI was genetically associated with T2DM.CONCLUSION Short education was found to be associated with an increased T2DM risk.BMI might serve as a potential mediator between them.展开更多
Against the backdrop of growing national strength and rapid economic development, the government has placed more emphasis on education. In recent years, remarkable achievements have been registered in terms of educati...Against the backdrop of growing national strength and rapid economic development, the government has placed more emphasis on education. In recent years, remarkable achievements have been registered in terms of education in China, which lays a solid foundation for cultivating comprehensive professionally-trained personnel in the new era. However, the current education system is ridden with many setbacks and problems. This paper conducts an analysis of the specific conditions of education both at home and abroad, status quo of education in China, makes some reflections on the direction and measures of China's education reform based on the practical reality of education in China. Measures should be taken to inject personalities into the traditional, exam-oriented education system, which keeps pace with the new era. As is known to all, it's important to strike a balance between public education and non-government funded education in a scientific and reasonable manner. The overhauling of traditional education policies will pave the way for China's educational renaissance and realize the great blueprint of the Chinese dream.展开更多
文摘Mr. Jamyangling Soinam and his friends have raised more than 13 million yuan as a donation to Tibet and other Tibetan areas in China. People who visited him in Sweden reveal that he lives in a small house of less than 10 square meters, which he leased from others. He has no car....
文摘There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.
文摘Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)accreditation.In addition,ABET needs to automatically ensure that the mapping(classification)is reasonable and correct.The classification also plays a vital role in the assessment of students’learning.Since the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of dimensionality.In this work,a novel associative short text classification tech-nique is proposed to map PEOs to SOs.The datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by ABET-EAC.The datasets are processed and transformed into a representational form appropriate for association rule mining.The extracted rules are utilized as delegate classifiers to map PEOs to SOs.The proposed asso-ciative classification of the mapping of PEOs to SOs has shown promising results,which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.
基金the National Natural Science Foundation of China,No.81701378.
文摘BACKGROUND The causality between education and type 2 diabetes(T2DM)remains unclear.AIM To identify the causality between education and T2DM and the potential metabolic risk factors[coronary heart disease(CHD),total cholesterol,lowdensity lipoprotein,triglycerides(TG),body mass index(BMI),waist circumference(WC),waist-to-hip ratio(WHR),fasting insulin,fasting glucose,and glycated hemoglobin]from summarized genome-wide association study(GWAS)data used a network Mendelian randomization(MR).METHODS Two-sample MR and network MR were performed to obtain the causality between education-T2DM,education-mediator,and mediator-T2DM.Summary statistics from the Social Science Genetic Association Consortium(discovery data)and Neale Lab consortium(replication data)were used for education and DIAGRAMplusMetabochip for T2DM.RESULTS The odds ratio for T2DM was 0.392(95%CI:0.263-0.583)per standard deviation increase(3.6 years)in education by the inverse variance weighted method,without heterogeneity or horizontal pleiotropy.Education was genetically associated with CHD,TG,BMI,WC,and WHR in the discovery phase,yet only the results for CHD,BMI,and WC were replicated in the replication data.Moreover,BMI was genetically associated with T2DM.CONCLUSION Short education was found to be associated with an increased T2DM risk.BMI might serve as a potential mediator between them.
文摘Against the backdrop of growing national strength and rapid economic development, the government has placed more emphasis on education. In recent years, remarkable achievements have been registered in terms of education in China, which lays a solid foundation for cultivating comprehensive professionally-trained personnel in the new era. However, the current education system is ridden with many setbacks and problems. This paper conducts an analysis of the specific conditions of education both at home and abroad, status quo of education in China, makes some reflections on the direction and measures of China's education reform based on the practical reality of education in China. Measures should be taken to inject personalities into the traditional, exam-oriented education system, which keeps pace with the new era. As is known to all, it's important to strike a balance between public education and non-government funded education in a scientific and reasonable manner. The overhauling of traditional education policies will pave the way for China's educational renaissance and realize the great blueprint of the Chinese dream.