• Journal of Information Technology Services
• /
• v.7 no.4
• /
• pp.231-246
• /
• 2008

This paper suggests an e-learning system model, a goal-driven personalized e-learning system, which increase the effectiveness of learning. An e-learning system following this model makes the learner choose the learning goal. The learner's choice would lead learning. Therefore, the system enables a personalized adaptive learning, which will raise the effectiveness of learning. Moreover, this paper proposes a SCORM standard, which modifies SCORM 2004 that has been insufficient to implement the "goal driven personalized e-learning system." We add a data model representing the goal that motivates learning, and propose a standard for statistics on learning objects usage. We propose each standard for contents model and sequencing information model which are parts of "goal driven personalized e-learning system." We also propose that manifest file should be added for the standard for contents model, and the file which represents the information of hierarchical structure and general learning paths should be added for the standard for sequencing information model. As a result, the system could sequence and search learning objects. We proposed an e-learning system and modified SCORM standards by considering the many factors of adaptive learning. We expect that the system enables us to optimally design personalized e-learning system.

• The Mathematical Education
• /
• v.56 no.2
• /
• pp.161-175
• /
• 2017

The purpose of this study is to compare and analyze the impact on low-achievers in mathematics who studied mathematics using Blended e-learning and Personalized system of instruction after school. Blended e-learning is defined as the management of e-learning using the e-study run by the education office in local. Personalized system of instruction was proceeded as follows; (1) all students are given a syllabicated learning task and a study guide, (2) students study the material autonomously according to their own pace for a certain period of time, (3) the teacher strengthens the students' motivation through grading and feedback after students study a subject and solve the evaluation problem. The learning materials for Personalized system of instruction are re-edited the offline education contents provided by the blended e-learning to the level of students. The 118 $7^{th}$ grade students from the D middle school participated in this study. The results were verified by achievement tests before and after the study, as well as survey regarding their attitude toward mathematics. The results are as follows. First, Blended e-learning has more positive impacts than Personalized system of instruction in mathematics achievement. Second, there was no difference in mathematics achievement according to their self-directed learning between Blended e-learning and Personalized system of instruction. Third, both types utilizing Blended e-learning and Personalized system of instruction have positive effect on attitude toward mathematics, and there is not their difference between two methods of teaching and learning mathematics.

• International Journal of Contents
• /
• v.5 no.1
• /
• pp.51-55
• /
• 2009

In this paper, we present a structure of personalized e-Learning system to study for a test formalized by uniform multiple-choice using on/off line mixed estimations as is the case of Driver :s License Test in Korea. Using the system a candidate can study toward the license through the Internet (and/or mobile instruments) within the personalized concept based on IRT(item response theory). The system accurately estimates user's ability parameter and dynamically offers optimal evaluation problems and learning contents according to the estimated ability so that the user can take possession of the license in shorter time. In order to establish the personalized e-Learning concepts, we build up 3 databases and 2 agents in this system. Content DB maintains learning contents for studying toward the license as the shape of objects separated by concept-unit. Item-bank DB manages items with their parameters such as difficulties, discriminations, and guessing factors, which are firmly related to the learning contents in Content DB through the concept of object parameters. User profile DB maintains users' status information, item responses, and ability parameters. With these DB formations, Interface agent processes user ID, password, status information, and various queries generated by learners. In addition, it hooks up user's item response with Selection & Feedback agent. On the other hand, Selection & Feedback agent offers problems and content objects according to the corresponding user's ability parameter, and re-estimates the ability parameter to activate dynamic personalized learning situation and so forth.

• Fourth Industrial Review
• /
• v.3 no.1
• /
• pp.13-20
• /
• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.446-465
• /
• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• International Journal of Contents
• /
• v.4 no.2
• /
• pp.19-23
• /
• 2008

This paper presents a novel method to construct a personalized e-Learning system based on dynamic estimations of item parameters and learners' abilities, where the learning content objects are of the same intrinsic quality or homogeneously distributed and the estimations are carried out using IRT(Item Response Theory). The system dynamically connects the test and the corresponding learning procedures. Test results are directly applied to estimate examinee's ability and are used to modify the item parameters and the difficulties of learning content objects during the learning procedure is being operated. We define the learning unit 'Node' as an amount of learning objects operated so that new parameters can be re-estimated. There are various content objects in a Node and the parameters estimated at the end of current Node are directly applied to the next Node. We offer the most appropriate learning Node for a person's ability throughout the estimation processes of IRT. As a result, this scheme improves learning efficiency in web-base e-Learning environments offering the most appropriate learning objects and items to the individual students according to their estimated abilities. This scheme can be applied to any e-Learning subject having homogeneous learning objects and unidimensional test items. In order to construct the system, we present an operation scenario using the proposed system architecture with the essential databases and agents.

• The Journal of Korean Association of Computer Education
• /
• v.8 no.4
• /
• pp.49-56
• /
• 2005

Because e-learning system does not have any dynamic contents-delivery mechanism, all students in the same class get identical contents. In this paper, we introduce a personalized learning system, which is carefully designed and implemented based on data stream technology. The proposed system have a mechanism and interface changing lecture contents based on learner's level and ability. The system consists of a dynamic contents-delivery mechanism and learner level-test system. In this paper, we describe what are points to be considered when design and implementing a personalized learning system.

• Information Systems Review
• /
• v.6 no.2
• /
• pp.161-179
• /
• 2004

The purpose of this study is to suggest practical experience to develop small sized e-Learning system. The system is designed to help lecturers can arrange interface, organize contents, submit examinations and assess learner's score with no professional computing skills. The system has three advantages. First, it reduced implementation period through the use of GUI. Second, it is ordered to be personalized to construct format of the whole interface. Third, it has operational convenience which can be implemented on PC based system. These personalized features are enabling Learning on Demand. Also, there is comparatively low cost and high effectiveness on e-Learning implementation which facilitating quick adoption of e-Learning in its lectures.

• The Journal of Korean Association of Computer Education
• /
• v.12 no.3
• /
• pp.41-51
• /
• 2009

In this thesis, we suggested an e-learning model, which is named 'goal driven personalized e-learning system' to improve educational effects, and implemented it. The system makes the learner choose the learning goal which could be a motivational power for learning, so it enabled self-directed learning. In order to implement the system, we proposed new standards related to personalization by modifying SCORM 2004 standard. New standards stand for the statistics on learning objects usage, a goal for driving learning. and information of the contents model and the sequencing information model, which are parts of the system previously suggested. We implemented the system, and then proved that personalize e-learning is possible by showing that the system could offer a learning path individually to learners who have different characteristics.

• Proceedings of the Korea Contents Association Conference
• /
• 2008.05a
• /
• pp.725-731
• /
• 2008

In this paper, we propose a novel construction scheme for the personalized e-Learning system based on IRT(item response theory), which can be applied to the content including non-hierarchical and horizontal learning objects in its learning nodes. Especially the proposed system performs tests and re-estimates examinee ability during the learning nodes are operating so that the results are directly applied to the next node. This scheme can be called a dynamic relationship between test and learning which is totally different from conventional customization based on learning procedures separated from test steps. Moreover, we should periodically modify the averages of node difficulties, item parameters, and ability parameters of students so that the system have more accurate personalized learning capability. As a result, this scheme maximizes learning efficiency offering the most appropriate learning objects and items to the individual students according to their estimated abilities and the system itself should obtain continuous improvements by modifying the parameters and fulfilling periodical feedbacks.