In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new...In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new and successful educational data mining models were analyzed and compared. These models have better performance than traditional models( such as Knowledge Tracing Model) in efficiency,comprehensiveness,ease of use,stability and so on. Then,the neural network algorithm was conducted to explore the feasibility of the application of educational data mining in student management,and the results show that it has enough predictive accuracy and reliability to be put into practice. In the end,the possibility and prospect of the application of educational data mining in teaching management system for university students was assessed.展开更多
Corporations focus on web based education to train their employees ever more than before. Unlike traditional learning environments, web based education applications store large amount of data. This growing availabilit...Corporations focus on web based education to train their employees ever more than before. Unlike traditional learning environments, web based education applications store large amount of data. This growing availability of data stimulated the emergence of a new field called educational data mining. In this study, the classification method is implemented on a data that is obtained from a company which uses web based education to train their employees. The authors＇ aim is to find out the most critical factors that influence the users＇ success. For the classification of the data, two decision tree algorithms, Classification and Regression Tree （CART） and Quick, Unbiased and Efficient Statistical Tree （QUEST） are applied. According to the results, assurance of a certificate at the end of the training is found to be the most critical factor that influences the users＇ success. Position, number of work years and the education level of the user, are also found as important factors.展开更多
With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political educat...With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political education in colleges and universities should keep pace with the times, and examine the new ways and resources of the ideological and political education in colleges and universities from the perspective of the times. The high-tech has been applied to the development of the modem education, expanding the teaching space-time, improving the teaching methods of the ideological and political course, and enriching the teaching contents of the ideological and political theoretical courses. The development mechanism of the ideological and political education resources for college students based on the data mining technology is not only in line with the trend of the times, but can also play an ideal role in optimizing the ideological and political education mechanism for college students.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
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
采用Web data mining对远程教育进行分析,根据受教育对象存在的个体差异,提出个性化远程学习系统的框架结构思想和个性化服务的理念,对相关信息进行数据挖掘并建立起一个集智能化、个性化为一体的远程教育系统,从而更好地改善远程教育...采用Web data mining对远程教育进行分析,根据受教育对象存在的个体差异,提出个性化远程学习系统的框架结构思想和个性化服务的理念,对相关信息进行数据挖掘并建立起一个集智能化、个性化为一体的远程教育系统,从而更好地改善远程教育服务的现状。展开更多
Personalized education,tailored to individual stu-dent needs,leverages educational technology and artificial intelligence(AI)in the digital age to enhance learning ef-fectiveness.The integration of AI in educational p...Personalized education,tailored to individual stu-dent needs,leverages educational technology and artificial intelligence(AI)in the digital age to enhance learning ef-fectiveness.The integration of AI in educational platforms provides insights into academic performance,learning pref-erences,and behaviors,optimizing the personal learning process.Driven by data mining techniques,it not only ben-efits students but also provides educators and institutions with tools to craft customized learning experiences.To offer a comprehensive review of recent advancements in person-alized educational data mining,this paper focuses on four primary scenarios:educational recommendation,cogni-tive diagnosis,knowledge tracing,and learning analysis.This paper presents a structured taxonomy for each area,compiles commonly used datasets,and identifies future re-search directions,emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.展开更多
Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and researches.Data mining(DM)is an in...Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and researches.Data mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the data.At the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational sector.Recently,artificial intelligence(AI)techniques can be used for mining a large amount of data.At the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms.With this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education sector.The presented IEAFSS-NFC model involves data pre-processing,feature selection,and classification.Besides,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification results.Then,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education data.The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository.The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.展开更多
基金Sponsored by the Ability Enhancement Project of Teaching Staff in Harbin Institute of Technology(Grant No.06)
文摘In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new and successful educational data mining models were analyzed and compared. These models have better performance than traditional models( such as Knowledge Tracing Model) in efficiency,comprehensiveness,ease of use,stability and so on. Then,the neural network algorithm was conducted to explore the feasibility of the application of educational data mining in student management,and the results show that it has enough predictive accuracy and reliability to be put into practice. In the end,the possibility and prospect of the application of educational data mining in teaching management system for university students was assessed.
文摘Corporations focus on web based education to train their employees ever more than before. Unlike traditional learning environments, web based education applications store large amount of data. This growing availability of data stimulated the emergence of a new field called educational data mining. In this study, the classification method is implemented on a data that is obtained from a company which uses web based education to train their employees. The authors＇ aim is to find out the most critical factors that influence the users＇ success. For the classification of the data, two decision tree algorithms, Classification and Regression Tree （CART） and Quick, Unbiased and Efficient Statistical Tree （QUEST） are applied. According to the results, assurance of a certificate at the end of the training is found to be the most critical factor that influences the users＇ success. Position, number of work years and the education level of the user, are also found as important factors.
文摘With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political education in colleges and universities should keep pace with the times, and examine the new ways and resources of the ideological and political education in colleges and universities from the perspective of the times. The high-tech has been applied to the development of the modem education, expanding the teaching space-time, improving the teaching methods of the ideological and political course, and enriching the teaching contents of the ideological and political theoretical courses. The development mechanism of the ideological and political education resources for college students based on the data mining technology is not only in line with the trend of the times, but can also play an ideal role in optimizing the ideological and political education mechanism for college students.
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.
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.
基金supported by the National Natural Science Foundation of China(No.62377002).
文摘Personalized education,tailored to individual stu-dent needs,leverages educational technology and artificial intelligence(AI)in the digital age to enhance learning ef-fectiveness.The integration of AI in educational platforms provides insights into academic performance,learning pref-erences,and behaviors,optimizing the personal learning process.Driven by data mining techniques,it not only ben-efits students but also provides educators and institutions with tools to craft customized learning experiences.To offer a comprehensive review of recent advancements in person-alized educational data mining,this paper focuses on four primary scenarios:educational recommendation,cogni-tive diagnosis,knowledge tracing,and learning analysis.This paper presents a structured taxonomy for each area,compiles commonly used datasets,and identifies future re-search directions,emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.
文摘Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and researches.Data mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the data.At the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational sector.Recently,artificial intelligence(AI)techniques can be used for mining a large amount of data.At the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms.With this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education sector.The presented IEAFSS-NFC model involves data pre-processing,feature selection,and classification.Besides,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification results.Then,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education data.The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository.The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.
