| In recent years,as a result of the rapid development of information in government,military,banking,insurance and other commercial fields,large amounts of data have been generated and accumulated.All these areas have built up data warehouse by using massive data containing abundant information generated by the existing information system to provide a scientific basis for major decisions made inside,rather than traditional judgement based on experience.In the field of education,the development is relatively slow compared with the business field,but there are some universities which have used existing information systems(such as enrollment system,library system,course system,employment system,card)to build a data warehouse,to analyze the overall situation of the students,and provide the basis for education policy.However,the data building data warehouse generated by the students' learning process is not analyzed.The purpose of this study is to build a data warehouse based on students' learning behavior.Different students have different learning styles,such as time preferences,perceived preferences,preferences of learning content types,processing preferences and so on.After comparing the internationally accepted learning behavior data model,this study proposes a learning analysis data model,it's aimed to build data warehouse about the data of students' many kinds of learning behavior in the learning process.Through the OLAP technology,combined with the results of different behaviors,we can analyze the frequency distribution and duration of students' different behaviors at different time and modules on line,then speculate students' learning style.According to these speculations,teachers can make timely adjustments,provide more reasonable learning strategies,and truly have a scientific basis to teach students in accordance with their aptitude.At the same time,learning platforms can recommend personalized learning content and routes to improve the students' learning efficiency. |
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