• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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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.

• 한국초등과학교육학회지:초등과학교육
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• 제35권2호
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• pp.166-180
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• 2016

This paper examines the effects on academic achievement, scientific process skills and affective domain for elementary students learning the 'Chicken life cycle' through traditional science class versus a smart media based STEAM approach. Students designed and built a hatching jar and created a smart media content for chickens using time-lapse technology. This STEAM program was developed to improve their scientific concepts of animals over nine periods of classes using integrated education methods. The experimental study took place in the third grade of public schools in a province, with the STEAM approach applied in 2 classes (44 students) and the traditional discipline approach implemented in 2 classes (46 students). The STEAM education significantly influenced the improvement of academic achievements, basic scientific process skills and affective domain. The results suggest that this STEAM approach for teaching scientific concepts of animal life cycles has the performance in terms of knowledge, skills and affect gain achievements in elementary school students' learning when compared to a traditional approach. Moreover, the smart media based STEAM program is helpful to lead students to engage in integrated problem-solving designs and learning science and technology.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• 공학교육연구
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• 제4권2호
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• pp.3-10
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• 2001

최근에 인터넷과 웹의 이용이 보편화됨에 따라 이를 교육에도 활용하고자 하는 많은 노력이 이루어지고 있다. 웹을 이용한 웹 기반 강의의 장점은 언제 어디서나 저렴한 비용으로 교육받을 수 있다는 것이다. 따라서 웹 기반 강의의 전망은 매우 밝고 잠재 고객들도 매우 많다고 볼 수 있다. 웹 기반의 자바 프로그래밍 교육 수요도 늘어나고 있다. 본 논문은 자바 프로그래밍 과목을 웹 기반 강의와 면 대 면 강의로 운영한 후 학습 효과를 통계적 추론을 사용하여 비교 분석한다. 이러한 분석 결과를 통하여 웹 기반 강의가 면 대 면 강의에 비해 효과적이지 못하다고 결론짓는다. 또한 자바 프로그래밍 교육에서 웹 기반 강의의 취약점을 파악하고 웹 기반 강의를 질적으로 개선하기 위한 방안을 제안한다.

• 대한한의학원전학회지
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• 제32권3호
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• pp.47-57
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• 2019

Objectives : We aim to assist in choosing an appropriate method for word extraction when analyzing East Asian Traditional Medical texts based on unsupervised learning. Methods : In order to assign ranks to substrings, we conducted a test using one method(BE:Branching Entropy) for exterior boundary value, three methods(CS:cohesion score, TS:t-score, SL:simple-ll) for interior boundary value, and six methods(BExSL, BExTS, BExCS, CSxTS, CSxSL, TSxSL) from combining them. Results : When Miss Rate(MR) was used as the criterion, the error was minimal when the TS and SL were used together, while the error was maximum when CS was used alone. When number of segmented texts was applied as weight value, the results were the best in the case of SL, and the worst in the case of BE alone. Conclusions : Unsupervised-Learning-Based Word Extraction is a method that can be used to analyze texts without a prepared set of vocabulary data. When using this method, SL or the combination of SL and TS could be considered primarily.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• 영어어문교육
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• 제8권1호
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• pp.1-13
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• 2002

One of the benefits of the internet-assisted instruction is that it can improve L2 Learners' motivation to express themselves in English. The purpose of this paper is to investigate an effective approach to British-American fiction learning in Korean universities, which can emphasize communicative strategies drawing on video-conferencing system, a chat system(CUSeeMe), and an e-mail system. Students are passive participants who cannot assert their creativity in the traditional teaching method of British-American fiction, which mainly relies upon reading and translation far from literature lessons. In CCDL(Cross-cultural distance learning) class, students can play active roles in asserting their own ideas and assuming considerable responsibility for making a presentation in English. A professor can play a role as a coordinator in supporting the students' activities and in winding up the class. The main significance of this article lies in providing a paradigm for CCDL class beyond the limitation of the traditional teaching method of British-American fiction in Korea and futhermore in exploring the eclectic integration of the traditional one and CCDL.

• Journal of Nutrition and Health
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• 제35권8호
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• pp.886-895
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• 2002

Introduction and application of virtual education has been rapidly increased in these days. A variation of information communication technology has an effect on education in interconnect with network as internet in the world that exceed the limit of time and regional. Computer and network communication technology through the medium of internet make an entrance cyber education as a new education paradigm. It must be affective on learner who have various educational characteristics and requirements. It begins to appear quality, quantity improvements of knowledge and the development of information technology that web based cyber education. This study was conducted to develop the web based education program and to evaluate the effectiveness of learning satisfaction and accomplishment and to compare the cyber lecture system with the traditional lecture system During the second semester of 2001, this study was investigated 317 registered students in a "Food and Culture" class at Kyungwon University. The data were obtained from pre and post-study with self-administered questionnaire. The evaluation and satisfaction score of students who were registered in cyberclass was negative tendency to compare pre with post-test scores, because of insuffciency of computer-aided lecture system. The major problem was inconvenient in checking system for connecting times in cyberclass which was one of evaluation point in final score. Another problem was frequently disconnection during cyber studying and not to concentrate each time in the cyber lecture because of eye fatigue, boring due to less interesting contents than other newly developed web-site. The students was prefer to mix type of the cyber and traditional lecture type class. The result of final score an each class, the score of cyber class (71.36 $\pm$ 22.44) was significantly lower than other groups (mixed type : 76.66 $\pm$ 19.99, traditional type :79.17 $\pm$ 15.72) (p < 0.05). Cyber class was attempted to present a useful and interesting teaching and learning tool which can be applied successfully in a longer term. The result suggest that various teaching and learning strategies should be developed considering the fact that the student learn alone most in time.t in time.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.191-212
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• 2012

Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning" capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.

• 공학교육연구
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• 제26권3호
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• pp.42-48
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• 2023

We compare engineering students' learning performance in introductory Statistics classes of the two class types, traditional in-classroom classes with a few reports and real-time online classes with quizzes. Rates of missing classes and turning in homeworks are also included to explain learning attitude. Scores of quizzes, midterm test and final test are used to assess performance. Upto the midterm, the class type is not significant, but rates of missing classes and turning in homeworks are significant. Since the midterm, in-classroom class type reveals better final performance than real-time online class type, rate of turning in homeworks is significant, but rate of missing classes is not significant.