• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.3-35
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• 2002

Reinforcement Learning (RL) is one of machine learning methods and an RL agent autonomously learns the action selection policy by interactions with its environment. At the beginning of RL research, it was limited to problems in environments assumed to be Markovian Decision Process (MDP). However in practical problems, the agent suffers from the incomplete perception, i.e., the agent observes the state of the environments, but these observations include incomplete information of the state. This problem is formally modeled by Partially Observable MDP (POMDP). One of the possible approaches to POMDPS is to use historical nformation to estimate states. The problem of these approaches is how t..

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.151-164
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• 2006

최근 e-러닝의 확산 속도에 따른 많은 문제점도 발생되고 있으며 그중 가장 큰 이슈가 e-러닝 사이트에 대한 평가인데 즉, 제공하는 사이트의 서비스품질이 사용자들에게 얼마만큼의 성과를 통한 만족을 가져다 줄 수 있는지에 대한 문제이다. 이에 본 연구에서는 e-러닝의 사업 분류 중 일반인과 학생을 교육대상으로 하는 B2C e-러닝 사이트의 서비스품질 결정요인이 개인성과, 사용자만족, 재이용의향 등에 어떠한 영향을 미치는 지를 분석하고자 한다. 이러한 연구목적에 따른 결과를 토대로 e-러닝 사이트의 벤더가 사용자들에게 고품질의 서비스를 제공하기 위해 최우선적으로 고려해야 할 요인들과 전략적 시사점을 제시하여 실제적인 e-러닝의 활성화에 기여하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2573-2589
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• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• Information Systems Review
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• v.10 no.3
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• pp.39-64
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• 2008

As digital convergence evolves, it is expected that the market of IP-based services like VoIP and IPTV will be expanded. In particular, IPTV market is expected to attract consumers' attention through various interactive services offering a variety of experiences to consumers. Interactivity sets apart old media from new one in terms of how to mediate effects of user satisfaction. The object of this study is to investigate (1) multi-dimensional Interactivities in an interactive medium based on IP and relationship among them, and (2) significant factors affecting cognitive absorption of interactive media users. This study aims to provide implications on how to develop strategies for IP-based media including e-learning system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3307-3321
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• 2018

With the ever-increasing queries related to temporal (or time-related) information, such as the product launching time, in search engine, most web pages will be augmented with such information in the future. Meanwhile, the gradual emergence of the use of electronic textbooks (or e-Textbooks), which enrich the traditional paper-based textbooks with multimedia contents such as interactive quizzes and multimedia-based simulations, has led us to infer that e-Textbooks will be blended with temporal information to support learning. The use of temporal information helps teachers and students to understand the level of prior knowledge required to study a topic, as well as the sequence of learning activities and related sub-topics, that best attains the educational goals. This paper presents a simple yet efficient tool called TimeMap, which is based on mind mapping, to create an e-Textbook called TimeBook that takes account of time-related curriculum and the ability of students to learn via collaboration.

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.247-252
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• 2005

• Journal of Periodontal and Implant Science
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• v.52 no.3
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• pp.181-182
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• 2022

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.165-172
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• 2010

In Web-based learning systems, the techniques, as self-regulated learning, self-directed learning, are used to improve the effect of learner's study. These techniques are methods considering learner's study level but to consider the learner's study ability properly, the tailored course for learner should be applied. In this research, the learning system considering learner's study ability was proposed. To decide a learner's study ability, IRT(Item Response Theory) was applied and learning contents and question items were developed and applied by the degree of difficulty.