The Recommendation System for Learning Activities According to Learning Styles Analyzed by Data Mining

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pensri amornsinlaphachai


      This research has four purposes including 1) to analyze the variables related to learners' learning style 2) to select a learning style classification model 3) to develop a recommendation system for learning activities according to learning style analyzed by data mining and 4) to compare learners' study achievement between the study whose learning activities are organized by the recommendation system for learning activities and the study whose learning activities are organized by an instructor. For variables related to learning style, 12 experts’ opinions are analyzed by using a questionnaire for experts' opinions on variables related to learning style. Learning style questionnaires were developed based on the principle of Honey and Mumford, which are collected from 1,328 undergraduate students studying at Nakhon Ratchasima Rajabhat University. These samplings are applied to create 14 learning classification models. In order to evaluate the recommendation system for learning activities, the satisfactions of the system are collected from 5 experts. For comparing study achievement, the experiment is conducted with 2 groups of students. The first group consists of 28 students studying by the system-based organizing the learning activities whereas the second group consists of 28 students studying by an instructor-based organizing’s learning activities. The research results are as follows. Firstly, there are 7 variables related to the learning style : gender, faculty, year, GPA, highest score subject, previous qualification and previous study plans. Secondly, the most efficient model is the one created by J48graft algorithm, which has an accuracy of 82.23%. This results are obtained by testing the performance of learning style classification models created by 14 data mining algorithms. The inputs for data mining are the 7 variables related to learning style and the results of the learning style questionnaires collected from 1,328 students. Thirdly, the recommendation system for learning activities consists of 4 main modules: student data management module, learning activity module, prediction and recommendation module and study achievement module. The model created by J48graft is used as a basis for building the prediction and recommendation module. For assessing experts’ satisfaction with the system, overall average score is 4.60, which satisfaction is at the most level. Fourthly, the students from the system-based organizing’s learning activities reach higher study achievement than the students from the instructor-based organizing’s  learning activities at the statistical significance level of .05.


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