Improving Performance of Decision Trees for Recommendation Systems by Features Grouping Method

Recently, recommendation systems have become an important tool to support and improve decision making for educational purposes. However, developing recommendation systems is far from trivial and there are specific issues associated with individual problems. Low-correlated input features is a problem that influences the overall accuracy of decision tree models. Weak relationship between input features can cause decision trees work inefficiently. This paper reports the use of features grouping method to improve the classification accuracy of decision trees. Such method groups related input features together based on their ontologies. The new inherited features are then used instead as new features to the decision trees. The proposed method was tested with five decision tree models. The dataset used in this study were collected from schools in Nakhonratchasima province, Thailand. The experimental results indicated that the proposed method can improve the classification accuracy of all decision tree models. Furthermore, such method can significantly decrease the computational time in the training period

Selecting feature grouping and decision tree to improve results from the Learning Object Management Model (LOMM)

Recommendation systems, also known as intelligent decision support systems, have been used to support and strengthen the decision making in various areas including education. In order to establish efficient recommendation systems for educational purposes, several specific problems have to be addressed. One such problem is the weak relationship between input features, which causes performance of decision trees to deteriorate. This paper therefore proposes two preprocessing techniques to strengthen the relationships of input features for decision trees by using ontology and Apriori algorithm. Ontology-based feature grouping is used to combine related input features and to derive a set of new inputs. Apriori-based feature adding is used to find the groups of strong input features and add them as the new derived inputs. The proposed methods have been evaluated using data collected from schools in Nakhon Ratchasima province, Thailand. The experimental results suggested that the proposed methods have improved the accuracy of decision trees and the performance of recommendation systems in this test case. Furthermore, this paper also conducted experiments to select the appropriate input features and types of the decision tree specific to the dataset for further development

An investigation on the correlation of learner styles and learning objects characteristics in a proposed Learning Objects Management Model (LOMM)

The issues of accessibility, management, storage and organization of Learning Objects (LOs) in education systems are a high priority of the Thai Government. Incorporating personalized learning or learning styles in a learning object management system to improve the accessibility of LOs has been addressed continuously in the Thai education system. A proposed Learning Object Management Model (LOMM) is discussed in this paper which aims to adapt and optimize the learning process based on characteristics of the individual learners. This study aims to find the correlation between learning styles and LOs characteristics in the LOMM. Decision Tree and Apriori algorithms were used to generate a predictive model for the classification of learners. Development of the predictive model was based on survey results from 1,586 high school students in Nakhon Ratchasima province, Thailand. The diverse LOs characteristics were analyzed in order to find the correlation with learning styles of the learners. The classification model consists of 24 sub-models used to predict a learner’s class based on 8 groups of LOs characteristics. The best accuracy obtained in the study was 80.23%. Finally, for the next phase this approach has been designed to support the proposed LOMM and it is expected that it could be readily applied to other e-learning systems and digital repositories