Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students’ attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.
To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students’ initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.
To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students’ satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students’ previous test scores and priorities encouraged students to concentrate on the lesson.
As information technology (IT) matures, E-learning has gradually come into its own. However, it is a challenge to develop web-based learning that is suitable for the varied needs of different students. Successful learning stems from the conformity between student needs and the learning environment (Wang, Wang, Wang, & Huang, 2006). E-Learning systems that operate on the basis of internet networks currently researched are providing varied and segmented learning content to students in order to allow students to customize the organization of their personal learning course. Learning style is a new approach to E-learning systems to provide students an optimized learning environment. An adaptive learning and a personalized learning are techniques recently studied in the field of learning style.
Adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student’s learning style (Chang, Kao, Chu, & Chiu, 2009). An advantage of E-learning is that it supports adaptive learning, which enables students to customize their learning environments and dynamically adapts learning content to students’ learning needs (Huang & Yang, 2009).
Personalized learning is a public education model that tailors learning to the students’ needs, interests and aptitudes. The model is dedicated to developing individualized learning programs for each student for the purpose of engaging each student in the learning process in the most productive and meaningful way to optimize each student’s learning potential and success (Baylari and Montazer, 2009 and Margaret, 2002).
In existing E-learning systems, a teacher organizes a learning course that consists of learning contents and sequences, according to a subject, and a student studies according to a system organized by the teacher. However, as preferences and educational levels of students are varied, and various types of content (such as text, VOD, images and sound) on a learning subject can be provided, the students must be able to choose learning content and sequences in order to study effectively. The difficulty in the existing Learning style is in selecting learning contents and sequences appropriate to the individual student, as there is no system that enables a student to organize a learning environment that is appropriate for him/her or that he/she desires by providing systematically analyzed materials reflecting the characteristic of the student.
In this study, we propose a Personalized Learning Course Planner (PLCP). PLCP allows students to easily select learning contents and sequences by analyzing user profile data in an E-Learning Decision Support System (EL-DSS). The user profile data was collected from the students’ initial priorities for learning contents and scores after their study. EL-DSS allows students to select an appropriate learning course that is organized according to calculated results based on the user profile data. We begin with a brief review of the relevant research in Section 2, and Section 3 explains the proposed systems, PLCP and EL-DSS. A sample learning system implemented to evaluate the effectiveness of PLCP and the experiment’s results are presented in Sections 4 and 5.
In this paper, we have suggested a personalized learning system, that is, a PLCP that accommodates the prioritization of learning units and the previous learning information of students and recommends an optimized learning course. The learning course is constructed according to the contents organization algorithm containing the decision matrix calculated according to the student’s priorities and previous learning information. By accessing and managing the student’s historical learning data and priorities, the proposed system can suggest the optimized learning course, considering units that require additional attention. This function makes it possible to improve the effectiveness and performance of the entire lesson.
To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students’ satisfaction by questionnaire analysis. The results demonstrate that the proposed system improved the effectiveness of student learning. Further investigation of the participants indicates that suggesting a learning course that is suitable to students’ historical learning activities and priorities encouraged students to take interest in the lesson.
These results indicate that the proposed learning method can effectively improve students’ interest and satisfaction in E-learning systems.
