• The Mathematical Education
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• v.57 no.2
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• pp.75-92
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• 2018

Based on Perceptions about Mathematics Teachers (PMT) perceived by high school students, measured by 2189 students from Seoul Educational Longitudinal Study 2014 (SELS 2014), latent profile analysis (LPA) identified five distinct types of student groups (positive, partial positive, middle, negative, extreme negative). These student of positive, middle, and negative groups are positive, moderate and negative perceptions about math teachers. Partial positive group generally had a positive perception about mathematics teachers, extremely negative group was very negative about mathematics teachers. Both of these groups had peculiarly inconsistent trends and several anomalies. The Multinomial logistic regression analyses also indicated that individual factors (gender, major, self-concept, resilience, self-assessment, career maturity), school factors (friendship, relationship with school teachers) and parental factors (academic-relationship, emotional-relationship) were significant predictors of PMT profile groups. The Analysis of variance also indicated that mathematics class (attitude, satisfaction and atmosphere), Mathematics achievement were significant predictors of PMT profile groups. The profiling of perceptions about mathematics teachers resulted in enhanced understanding of the complex range of processes students employed. During mathematics class, implementation of smooth interactions and communications between students and teachers added in the teaching and learning of mathematics.

• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.171-180
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• 2003

The recent paradigm of web-based teaching-learning is changing into a direction that analyzes the learning patterns of learners on the basis of learners' ability, aptitude, request, interest, learning history, activity profile, etc. and provides adaptive environment with individual learners The present study analyzed learners' learning patterns using data on learning activities and developed a personalization system that provides learning environment adapted to individual learners. This study customized in three aspects, which are recommendation of learning path, recommendation of interface and recommendation of interaction, through Web mining. The personalization system developed in this study was proved to be effective in improving individual learners' satisfaction with learning in Web-based teaching-learning environment.

• Journal of the Korean GEO-environmental Society
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• v.20 no.3
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• pp.15-20
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• 2019

A large amount of site investigation data is essential to obtain reliable design value. However, site investigations are generally insufficient due to economic problems. It is important to estimate the ground profile information in unboring region for accurate earthwork-volume prediction, and such ground profile information can be estimated by using the geo-statistical approach. Furthermore, the ground profile information in unboring region can be estimated by training a model via machine learning technique such as artificial neural network. In this paper, artificial neural network-based model estimated the ground profile information in unboring region, and this results were compared with that of ordinary kriging technique, which is referred to the geo-statistical approach. Accordingly, a total of 84 ground profile information in an actual bridge environment was split into 75 training and 9 test databases. The observed ground profile information of the test database was compared with those of the ordinary kriging technique and artificial neural network.

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

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.694-696
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• 2000

In this paper, the Direct Learning Control is applied to robot's trajectory tracking control to solve the problem that lies in the existing Iterative Learning Control(ILC) and the tracking Performance is analyzed and the better approach is searched using computer simulation and experiments. It is assumed that the Direct Learning Control(DLC) is saved onto memory basically after obtaining control input Profiles for several Periodic output trajectories using the ILC. In case the new output trajectory has special relations with the previous output trajectories, there is an advantage that the desired control input profile can be obtained without iterative executions only using the DLC. The robot's tracking control system is comprised of DSP chip. A/D converter, D/A converter and high-speed pulse counter included in the control board and the performance is examined by carrying out the tracking control for the given output trajectory.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.325-327
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• 2002

정보검색 태스크에서 사용자 모델링의 목적은 관련정보 검색을 용이하게 해주기 위하여 사용자의 관심도 또는 필요정보의 모델을 학습하는 것으로 시간적인 속성(temporal characteristics)을 가지며 관심 이동을 적절하게 반영하여야 한다. 강화학습은 정답이 주어지지 않고 사용자의 평가만이 수치적으로 주어지는 환경에서 평가를 최대화 한다는 목표를 가지므로 사용자 프로파일 학습에 적용할 수 있다. 본 논문에서는 사용자가 문서에 대해 행하는 일련의 행위를 평가값으로 하여 사용자가 선호하는 용어를 추출한 후, 사용자 프로파일을 강화학습 알고리즘으로 학습하는 방법을 제안한다. 사용자의 선호도에 적응하는 능력을 유지하기 위하여 지역 최대값들을 피할 수 있고, 가장 좋은 장기간 최적정책에 수렴하는 R-Learning을 적용한다. R-learning은 할인된 보상값의 최적화보다 평균 보상값을 최적화하기 때문에 장기적인 사용자 모델링에 적합하다는 것을 제시한다.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.31-35
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• 2021

In recent years, due to COVID-19, the process of shopping has become more restricted and difficult for customers. Based on this aspect, customers are more interested in online shopping to keep the Untact rules and stay safe, similarly ordering their product based on their need and interest with most straightforward and fastest ways. In this paper, the reinforcement learning technique is applied in the product recommendation system to improve the recommendation system quality for better and more related suggestions based on click patterns and users' profile information. The dataset used in this system was taken from an online shopping mall in Jeju island, South Korea. We have compared the proposed method with the recent state-of-the-art and research results, which show that reinforcement learning effectiveness is higher than other approaches.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.754-759
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• 2007

The information recommendation system offers selected documents according to information needs of dynamic users. User's needs are expressed as profiles consisting of one or more words and may be changed into some specifics through relevance feedback made by users during the recommendation process. In previous research, users have entered relevance information by taking part in explicit relevance feedbacks and learned user profiles using the positive relevance feedbacks. In this paper, we learn user profiles using not only positive relevance feedback but negative relevance feedback and reinforcement learning. To compare the proposed with previous method, we performed experiments to evaluate recommendation performance of the same topic. As a result, the former shows the improved performance than the latter does.