• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.477-485
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• 2010

Web based learning systems are operating with various and lots of learning contents. But it is hard to construct learning contents to fit learners when they select learning contents for learning. In this paper, we proposed the recommendation method that can support the learning contents as calculate learner's preference using the learning history information of learner's profile when learner design and compose learning course. In the applying result of this method, we've selected testing learner group and was able to know it can help to learner processing learning by themselves as we've got great learning satisfaction after test.

• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.296-301
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• 2009

In the self-directed learning, learner can construct learning course. But it is very difficult for learner to construct learning course with understanding the various learning contents's characteristics. This research proposed the method to support to learner the information of learning contents type to fit the learner as calculate the learner's learning preference when learner construct the learning course. The calculating method of learning preference used preference vector value of topic map. To apply this method, we tested 20 learning sampling group and presented that this method help to learner to construct learning course as getting the high average degree of learning satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.1-8
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• 2009

Learning user's preference is a key issue in intelligent system such as personalized service. The study on user preference model has adapted simple user preference model, which determines a set of preferred keywords or topic, and weights to each target. In this paper, we recommend multi-perspective user preference model that factors sentiment information in the model. Based on the topicality and sentimental information processed using natural language processing techniques, it learns a user's preference. To handle timc-variant nature of user preference, user preference is calculated by session, short-term and long term. User evaluation is used to validate the effect of user preference teaming and it shows 86.52%, 86.28%, 87.22% of accuracy for topic interest, keyword interest, and keyword favorableness.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.439-440
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• 2019

본 연구에서는 프로그래밍 수업에 참여하는 학습자들을 대상으로 프로그래밍 학습의 예 복습과 교수자의 학습 지원 등에 대한 생각을 먼저 설문으로 조사하고 그 결과에 따라 교수자가 수업 내외에서 학습자들이 다양한 학습 활동들을 할 수 있도록 지원한다. 그리고 학기말에 학습자들을 대상으로 제공된 학습 방법들 중에 어떤 방법을 선호하는지를 설문조사하여 분석해보았다. 연구 결과를 통해 향후에는 학생들이 선호하는 학습 방법을 파악하고 제공함으로써 학생들의 학습 동기와 의욕을 향상시켜 보다 적극적인 학습 참여를 유도할 수 있을 것으로 기대한다.

• Journal of Korean Home Economics Education Association
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• v.18 no.4 s.42
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• pp.39-54
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• 2006

The main purpose of this study is to analyze the influence of gender equality consciousness of middle and high school student on the preference for Technology Home Economics and its teaming effectiveness. For this purpose, the gender equality consciousness, the preference and learning effectiveness of Technology Home Economics were measured among a number of 404 middle and high school students in Chuncheon city. The results of this study were summarized as following: 1. The gender equality consciousness of female students was higher than that of male students. 2. Middle and high school students had the higher preference and learning effectiveness for the Home Economics area in Technology Home Economics than they had for the Technology. 3. Female student's preference and learning effectiveness for the Home Economics area in Technology Home Economics were higher than male student's. Male student's preference and Loaming effectiveness for the Technology area in Technology Home Economics were higher than female student's. 4. The group which had relatively high gender equality consciousness score showed the higher preference and learning effectiveness for the Home Economics area in Technology Home Economics than the group with low gender equality consciousness score. In contrast, the group with low gender equality consciousness score had the higher preference for the Technology area in Technology Home Economics than the group with high gender equality consciousness score. In conclusion, gender equality consciousness and gender type had the most strong influence on the preference and learning effectiveness for Technology Home Economics among middle-high school students.

• (The) Korean Journal of Educational Psychology
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• v.32 no.3
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• pp.321-353
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• 2018

The purpose of this study was to investigate Korean undergraduate students' metacognitive awareness and preference of effective learning strategies. To achieve this goal, a scenario-based survey was conducted focusing on the metacognitive awareness, preference, and use of seven effective learning strategies (modality effect, static-media presentations, absence of extraneous details, testing, distributed learning, interleaved learning, generation) that were empirically supported. This study also aimed to explore the relationships between grade point average (GPA), metacognitive self-regulation, and the aforementioned variables to investigate which students know about, prefer, and use effective learning strategies. The majority of students were unknowledgeable about four of the seven strategies (modality effect, static-media presentations, absence of extraneous details, interleaved learning). Only half of the students were correctly aware of effectiveness of the two strategies (testing, generation). Moreover, students showed low preference for effective learning strategies. GPA did not show a significant correlation with metacognitive awareness and preference of effective learning strategies; however, it showed a significant positive correlation with the use of effective learning strategies. Only for a few learning strategies, metacognitive self-regulation showed a positive correlation with metacognitive awareness, preference, and/or their use. This study suggests that it is important to teach effective learning strategies to undergraduates with a specific direction of instruction. In addition, this study distinguishes metacognitive awareness from preference, suggesting that these two may reflect different constructs.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.

• The Journal of Korean Association of Computer Education
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• v.7 no.5
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• pp.1-8
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• 2004

This study investigated the effect of visual learning preference levels in computer assisted instruction (CAI) using static graphics and animations on students' conceptual understandings, application abilities and learning motivations. Fifty-nine seventh graders were selected from a middle school in seoul, and they were taught about the motion of molecule for 4 class hours. Two-way ANCOVA results revealed that the scores of the conception test of the animations group, regardless of student's visual learning preference levels, were significantly higher than those of the static graphics group. However, there were no differences between the two groups in the scores of application test and learning motivation test.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.355-363
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• 2007

One of major factors for learning achievement is the student's learning preference according to his character type. In course of learning, if a student studies e-learning contents opposed to his preference, then he would be under stress and his blood pressure and heart beat be changed. For measuring unwillingness, we used spectral components in frequency domain known as stress measure. For 13 children attending kindergarten we examined S(sensing)/ N(intuition) of MBTI and presented same learning contents during 10 minutes. During learning we gathered ECG signals, changed into HRV(heart rate variability), transformed time-varying HRV signal into spectral density in frequency domain. And then, we divided it into three areas of low(LF), middle(MF), and high-frequency(HF) and calculated stress measures by rates of those frequency area. We compared estimated stress measures of S group with them of N group whether students in different group preferred different contents or not. Experimental shows that students according to MBTI type prefer different contents.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.287-290
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• 2003

퍼지값을 크기 순서에 의해 나열하는 연구는 많이 이루어져 왔다. 그러나 기존의 방법들은 퍼지값을 제안된 기준에 의해 독자적으로 해석하여, 비교결과를 산출하는 것이 대부분이다. 본 논문에서는 사용자의 의견 또는 선호도를 반영한 학습데이터를 신경망을 이용하여 학습하는 방법을 제안한다. 이 학습이 끝난 후 얻어지는 신경망은 주어진 학습데이터를 이용하여 사용자의 퍼지값에 대한 모델을 생성하게된다.