An intelligent recommendation system framework for student relationship management

In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification

Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand

In order to support the academic management processes, many universities in Thailand have developed innovative information systems and services with an aim to enhance efficiency and student relationship. Some of these initiatives are in the form of a Student Recommendation System (SRM). However, the success or appropriateness of such system depends on the expertise and knowledge of the counselor. This paper describes the development of a proposed Intelligent Recommendation System (IRS) framework and experimental results. The proposed system is based on an investigation of the possible correlations between the students’ historic records and final results. Neural Network techniques have been used with an aim to find the structures and relationships within the data, and the final Grade Point Averages of freshmen in a number of courses are the subjects of interest. This information will help the counselors in recommending the appropriate courses for students thereby increasing their chances of success

Understanding student relationship management and its effects on university students

There are many problems happening with tertiary students such as enrolment and completion. Each student has faced with various problems to survive and succeed in higher education. Therefore, many universities have been promoting implementation of their information systems and services to aid their students and support academic management processes in various ways. This article explores the concept of Student Relationship Management (SRM) and how it effects on the university students. SRM is a key to provide the strategies to solve problems. Moreover, this article proposed the system architecture of intelligent recommendation system (IRS), which assists students to choose the course for their studies. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification to make the recommendation system precisely. These techniques are used to access the performance of the system and provide the recommendations for students in universities

Neural network modeling for an intelligent recommendation system supporting SRM for universities in Thailand

In order to support the academic management processes, many universities in Thailand have developed innovative information systems and services with an aim to enhance efficiency and student relationship. Some of these initiatives are in the form of a Student Recommendation System (SRM). However, the success or appropriateness of such system depends on the expertise and knowledge of the counselor. This paper describes the development of a proposed Intelligent Recommendation System (IRS) framework and experimental results. The proposed system is based on an investigation of the possible correlations between the students' historic records and final results. Neural Network techniques have been used with an aim to find the structures and relationships within the data, and the final Grade Point Averages of freshmen in a number of courses are the subjects of interest. This information will help the counselors in recommending the appropriate courses for students thereby increasing their chances of success

Developing an intelligent recommendation system for a private university in Thailand

In Thailand, choosing a program of study for tertiary students is significant due to the associated future job opportunities. Many students have enrolled in course majors without receiving counseling or advices from appropriate authorities or university services. This could have potential mismatch between students’ aptitude, personal interest and capability, and the particular course being taken up. This may lead to low retention rate and failures. In order to improve and support the academic management processes, many universities in Thailand are developing innovative information systems and services with an aim to enhance efficiency and student relationship. Some of these initiatives are in the form of a Student Recommendation System (SRM). In Thailand, this university service is normally provided by professional counselors or advisors who have many years of experience within the organization or in the higher education sector. However, the success or appropriateness of such advice is entirely depending on expertise of the counselor and it is entirely human-driven. In addition, the process is also tedious and time consuming. This paper reports a study on an investigation of possible correlation between student historic data and their final results. Clustering techniques have been used with the aim to find structures and relationship within the data. Results from two clustering methods, k-means and TwoStep methods have been compared. This paper describes the development of the experiments, and the proposed Intelligent Recommendation System framework

An investigation on the performance of four learning instruction designs in virtual experimenting learning for educating Thai youths on traffic rules

Road accident is a major cause of mortality in many countries. In Thailand, youths have a higher rate of accidents than the average population. One of the strategic action plans is to educate Thai Youths in order to improve their understanding of traffic rules and to increase their awareness of road safety. This article is an initial report aiming to investigate the performance of four learning instruction designs for Virtual Experiential Learning (VEL) based on different learning instructional materials. This study compared the learning results between pre-test and post-test scores of four groups of participants. The results support the hypotheses that all the learning instructions have the capacity to educate the Thai youths on traffic rules. This paper also discusses the next phase of this study which will investigate the effectiveness of the use of Virtual Experiential Learning (VEL) with an aim to reduce cognitive

An intelligent recommendation system framework for student relationship management

Many universities have implemented innovative information systems and services to help their students and to support academic management processes. This paper proposes a conceptual framework to support Student Relationship Management (SRM) for Thai universities in the new electronic era. This conceptual framework assists students to choose appropriate course and subjects for their studies. The objectives of this article aim to broaden an understanding of SRM and related issues focus on recruitment, enrolment and course counseling. An Intelligent Recommendation System framework (IRS) has been designed using data mining and artificial intelligent techniques such as clustering, association rule and fuzzy logic. These techniques will be used to assess the performance of the students and to provide appropriate recommendations for their choice of courses and subjects