The Application Of Sequential Mining Technique In The Self-adaptive Learning System In Achieving The Intelligent Tutorial System

Since the 20th century, the fast development of information and network technique has been influencing people's life, study and work. Particularly in the field of education, various network teaching systems are taking off, with its very focus on the self-adaptive learning system which itself concentrates on learners. The system can arrange different study contents and styles according to the various conditions different learners respectively hold, which means giving people what they really and specifically want.This paper, under the self-adaptive learning theory, makes research and analyses the resource database of network teaching, of which data is mined through mining sequence technique. The result is an effective tutorial system is found, students are able to learn more efficiently. An intelligent learning guidance system is thus founded to avoid students losing directions in the process of searching for information.Besides, in this paper, one of the sequential mining patterns---AprioriAll algorithms have been studied in details. For the shortcomings of AprioriAll algorithm, it takes an improved algorithm which is based on the idea of binary tree generation to reduce the appearance frequency of candidate sequences and, in a sense, reduces the number of database scannning and pruning operation. Especially for the high-volume database, it will greatly optimize its space because it has been proved by the experiment that this algorithm could save more space than the AprioriAll algorithm and operates unobviously faster. At the same time, this algorithm is fast in searching for maximum frequent patterns with less space.