Association rules for data mining in item classification algorithm: Web service approach

Abstract

The student's assessment is the core of learning process, which facilitates teachers to evaluate a student's knowledge level; furthermore, the precise measurement helps the students knowledge development reaches their full potential. Usually, this assessment method is also known as computer adaptive testing (CAT). The conventional CAT systems contain its own item bank, which is stored separately in many repositories over the Internet. The collection of the items from many repositories of database together makes these items were reused, sharable, valuable, and also makes the larger item bank. Unfortunately, the combined items make the tangled data, and greater data size. The problem of data overloaded occurs, and a large number of irrelevant and redundant data should be eliminated. This paper has attempted to formulate the data mining model in manipulate the optimal item-set from the different sources of the item. The item data from many repositories were mined in order to extract the implicit, useful information and interesting patterns from the huge irrelevant and redundant data collections. Therefore, the association rules were established by applying the knowledge pattern, decision trees, adaptive testing and related theory. The result shows that the association rules and mining process are used to create the optimal item-set. This optimal item-set was delivered through Web service to the CAT applications. The result also shows that data mining works properly. Moreover, the precise items help the students improve their knowledge reach their full potential.