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
This paper surveys important aspects of Web Intelligence (WI). WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next genera...This paper surveys important aspects of Web Intelligence (WI). WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web - related products, systens, and activities. As a direction for scientific research and devlopment, WI can be extremely beneficial for the field of Artificial Intelligence in Education (AIED). This paper covers these issues only very briefly. It focuses more on other issues in WI, such as intelligent Web services, and semantic web, and proposes how to use them as basis for tackling new and challenging research problems in AIED.展开更多
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This paper surveys important aspects of Web Intelligence (WI). WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web - related products, systens, and activities. As a direction for scientific research and devlopment, WI can be extremely beneficial for the field of Artificial Intelligence in Education (AIED). This paper covers these issues only very briefly. It focuses more on other issues in WI, such as intelligent Web services, and semantic web, and proposes how to use them as basis for tackling new and challenging research problems in AIED.
