• Journal of Fisheries and Marine Sciences Education
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• v.26 no.2
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• pp.296-307
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• 2014

As an educational trial for pursuit of educational excellence in the disadvantaged gifted, this study was to explore learning styles and task preferences by student's grade and gender. Furthermore, this study sought to present the practical basis to develop programs for disadvantaged gifted students. Total 153 disadvantage gifted students responded items of the Learning Styles Inventory-III and the Task Preferences Scale, which responses were analyzed by student's grade and gender in using MANOVA. As the results, the 1st grade disadvantaged gifted students preferred Direct instruction, Technology, and Learning games to the higher grade level students. There were significant differences in task preferences by students' grade level. The 4th grade disadvantaged gifted students preferred Creative tasks and Difficult tasks more than other grade level students.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.519-525
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• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• Communications of Mathematical Education
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• v.29 no.4
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• pp.723-743
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• 2015

In this research, The objective of the present study was to develop a preliminary diagnostic worksheet for use in consultations for learning mathematics. In order to achieve this, the worksheet was constructed with questions designed to assess the students. Through standardization, diagnostic worksheets for primary school students in grades 5 and 6 and secondary school students in grades 7 and 8 were produced. The diagnostic worksheet was divided into three sections, consisting of the psychology of learning mathematics in section 1, the methodology in learning mathematics in section 2, and personal preferences in learning mathematics in section 3. The psychology of learning mathematics was composed of questions on factors such as, "confidence in math learning ability," "math anxiety," and "attitude in learning mathematics." Moreover, factors in methodology in learning mathematics were "self-management in learning mathematics" and "math learning strategies." Those for personal preferences in learning mathematics asked about "motivation" and "preferences" with questions about "math learning habits" and "management methods for learning math." This diagnostic worksheet can be used as basic material in consulting students on learning mathematics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• English Language & Literature Teaching
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• v.18 no.1
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• pp.217-235
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• 2012

The purpose of this study was to identify the perceptual learning style preferences of 97 Korean EFL students in middle school. Furthermore, it examined if students' learning styles varied in terms of gender and grade level. Data was collected by using Reid's (1987) PLSPQ and a personal background questionnaire and was analyzed by using descriptive statistics, MANOVA, ANOVA, and t-test. The results revealed that subjects had all six major learning styles but among them, auditory, group, and visual styles were the most preferred by them. The results found in this study, presented that Korean EFL middle school students favored learning English through listening, reading and working in groups and that younger students preferred learning through physical involvement and practicum. The findings of this study provide a number of useful insights for EFL and ESL educators and instructors in Korea. The current study suggests that a great number of variables such as culture, learning situation of the target country, age, and grade level can all play important roles in shaping the learning preferences and the learning styles of students. Considering these variables and promoting a curriculum that is interesting, appealing and successful may help maximize student L2 learning.

• Journal of Information Processing Systems
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• v.8 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.71-75
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• 2018

Image enhancement is a necessary end essential step after taking a picture with a digital camera. Many different photo software packages attempt to automate this process with various auto enhancement techniques. This paper provides and implements a system that can learn a user's preferences and apply the preferences into the process of image enhancement. Five major components are applied to the implemented system, which are computing a distance metric, finding a training set, finding an optimal parameter set, training and finally enhancing the input image. To estimate the validity of the method, we carried out user studies, and the fact that the implemented system was preferred over the method without learning user preferences.

• Journal of Korean Elementary Science Education
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• v.25 no.spc5
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• pp.495-506
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• 2007

This study investigated the differences in learning activity preferences according to different thinking styles between gifted and average students. A cluster analysis procedure was performed to classify students on the basis of thinking styles. Two clusters of different thinking styles were deduced: the gifted group with a high level thinking style (cluster 1), and the average group with a low level thinking style (cluster 2). The gifted group (cluster 1) preferred projects, simulations, discussions and game activities to other types of loaming activities. Gifted students and average students also were clustered into each three unique subgroups with respect to levels and patterns in thinking styles, and these subgroups also showed different learning preferences. The clusters of gifted students included the self-regulated learning type (cluster a), cooperative-learning type (cluster b), and the passive-learning type (cluster c). The clusters of average students included the independent learning type (cluster i), no-preference learning type(cluster ii), and the no-motivation & teacher-directed learning type (cluster iii). Theses clusters indicated significant differences not only in thinking styles but also in terms of preferences regarding learning activities. Theses findings are discussed in terms of their educational implications.

• Journal of the Korean Home Economics Association
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• v.36 no.2
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• pp.91-104
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• 1998

The specific objectives of the study were (1) to determine the preferences for three home economics curriculum designs(HECD) categorized by Majorie Brown(Technical HECD, Self Actualization HECD, and Practical Reasoning HECD), (2) to determine the differences between middle school HE teachers and high school HE teachers on each curriculum element, and (3) to determine the relationships between preferences for three HECD by curriculum elements and personal and professional characteristics of HE teachers. For these objectives, the subjects were randomly selected from HE teachers of secondary schools in the areas of Seoul, In-Cho, and Keungi. The 300 data collected by mailed survey were analyzed into frequency, percentage, Chi-square, and contingency coefficient using SAS program. The major findings were as follows: 1) the majority of HE teacher respeondents preferred the Practical Reasoning HECD about the following curriculum elements: Purpose of HE education, knowledge, subject matter, teaching method, society and culture, learner, learning atmosphere, and HE teacher's role. 2) No significant difference emerged when Chi-square was applied to determine difference between the two groups(middle and high school HE teachers) on three HECD according to each curriculum element. 3) The contingency coefficient between preferences of HE education purpose for three curriculum designs and age was 21, years of teaching was 23, between preferences of learning atmosphere of three curriculum designs and location of school was. 17. These mean that the younger and more beginning teachers perferred HE purpose of the Practical Reasoning HECD, and HE teachers working in urban area more preferred learning atmosphere of Practical Reasoning HECD.

• Journal of Agricultural Extension & Community Development
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• v.16 no.2
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• pp.463-486
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• 2009

The purpose of this study was to find out the differences of preferences in e-Learning contents delivery types according to information searching retrieval ability in agricultural high school students. Contents delivery types are limited three kinds which are HTML type, video type, and text type and need to know about differences. The following summarizes the results of this study. On the preference of e-Learning contents delivery type on information searching retrieval ability had differences. High level group of information searching retrieval ability showed that they mostly preferred text contents delivery type. However, low level group of information searching retrieval ability showed that they preferred video contents delivery type. The results support our belief that there could be the differences in preferences in e-Learning delivery types with students' information searching retrieval abilities. We suggest that delivery types of e-Learning should be based on the students not on designers and developers.