• Educational Technology International
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• v.6 no.2
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• pp.113-130
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• 2005

The purpose of this study was to determine the teaching styles of professors who teach adult students in selected higher institutions. It also identified whether professors' teaching styles were teacher-centered or learner-centered and examined the relationship between instructors' teaching styles and such instructor demographic variables as gender, years of teaching experience, and taught level of courses. This study used The Principles of Adult Learning Scale(PALS) (Conti,1983) to measure instructional preferences. Demographic characteristics were collected through a personal data inventory. The analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) tests were used to analyze the data. The data were examined for significance at the .05 level of confidence by means of analysis of variance. The dependent variables in this study were teaching styles of full-time professor, as represented by the seven subscores from the standardized instrument on the PALS. The seven subscores were: (1) learner-centered activities, (2) personalizing instruction, (3) relating to experience, (4) assessing student needs, (5) climate building, (6) participation in the learning process, and (7) flexibility for personal development. The study established that there was a significant difference in mean scores on the PALS between participants when examined by the number of years of teaching experiences.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.55-64
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• 2015

The present study identified the brain based learning activities on the individual learning senses by using the brain laterality and the whole brain index. Students receive the information through the visual, auditory, and kinesthetic senses by Politano and Paquin's (2000) classification. These learning senses are reflected on brain by the various combinations of senses for learning. Measuring the types of the learning senses involving in brain laterality and whole brain is required to figure out the related learning styles. Self-directed learning involved in the learning senses shows the problem-based learning associated to the brain function by emphasizing the balanced brain utilization which is known as whole brain. These research results showed the successful whole brain learning is closely associated with elevated auditory learning and elevated visual learning in sensorimotor brainwave rhythm (SMR) while it shows the close association with elevated kinesthetic and elevated visual learning in beta brainwave rhythm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Journal of The Korean Association of Information Education
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• v.21 no.4
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• pp.393-401
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• 2017

The purpose of this study is to develop a deep learning based learning system for improving learner's data analytical thinking ability. The contents of the study are as follows. First, deep learning was applied to the discovery learning model to improve data analytical thinking ability. This is a learning method that can generate a model showing the relationship of given data by using the deep learning method, then apply the model to new data to obtain the result. Second, we developed a deep learning based system for DBD learning model. Specifically, we developed a system to generate a model of data using the deep learning method and to apply this model. The research of deep learning based learning system will be a new approach to improve learner's data analytical thinking ability in future society where data becomes more important.

• English Language & Literature Teaching
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• v.11 no.4
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• pp.121-137
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• 2005

The present study aims to examine how the roles of teacher and learners affect the repair patterns of both teacher's and learner's utterances in English as a second language (ESL) classroom discourse. The study analyzed beginning ESL classroom discourse and found that the structure of repair seems to be greatly influenced by the roles of participants in a second language classroom. The teacher's repair work was mainly characterized by self-repair. In contrast, learners' repair sequences were predominantly characterized by other-repair. More specifically, self-initiation by the learner of the trouble source was cooperatively completed by the teacher and the other learners. Other-initiated and other-completed repair was the most prevalent form in the current classroom data, which was carried out by the teacher in both modulated and unmodulated manners. When the trouble sources were mostly concerned with the learners' problems with linguistic competence and information presented in the textbook, other-repair took place in a modulated manner (i.e., recasting and prompting). On the other hand, when dealing with learners' errors with factual knowledge, other-repair was conducted in an unmodulated way (i.e., 'no' plus correction).

• Journal of the Korea Computer Industry Society
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• v.9 no.4
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• pp.177-182
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• 2008

Due to the computer developments and the expansion of data communications, many changes are done to educational environments in addition to the changes of the various social areas. In this paper, a learning system is developed, which adapts itself by checking the changing factors of the learner. The educator works as a overall project manager in this project learning system and the learner benefits from the user-oriented comparative study and pattern design performed in the domains of the internet and intranet.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.57-65
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• 2008

In the web- based educational system, how to improve interaction among learners are very important by the Internet. Therefore interpersonal interaction is essential for a good educational environment. In this paper, a web-based PBL(Problem-Based Learning) system is designed and implemented to improve learner's interaction. I investigated how the method for improving interaction affect PBL activities and how students perceive the web-based PBL learning experience. The result of experiment showed that the suggested system facilitated learners' self-directed learning process and interaction.

• English Language & Literature Teaching
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• v.17 no.4
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• pp.49-74
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• 2011

The present paper aims to explore the role of non-negotiated input and output in language acquisition in the context of free Web chat. In order to examine how input and output contribute to language acquisition, with or without meaning negotiation, the present study examined a Korean EFL learner's chat data collected over 6 months. Chat texts across 43 chat sessions were analyzed, along with her comment notes and interviews. The input and output negotiated for meaning were traced throughout all sessions to find evidence that they were linked to acquisition. Other input and output in the interaction were also traced to ascertain if they contributed to acquisition. The chat text analysis, comment notes, and the interviews revealed that the opportunities of meaning negotiation in a free Web chat context was quite limited and that the learner acquired language even in the absence of meaning negotiation. The findings suggest that input and output via Web chat, whether negotiated or non-negotiated, play their respective roles, contributing to different aspects of acquisition.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.60-65
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• 2010

Language learning involves linguistic environments around the learner. So the variation in training input to which the learner is exposed has been linked to their language learning. We explore how linguistic experiences can cause differences in learning linguistic structural features, as investigate in a probabilistic graphical model. We manipulate the amounts of training input, composed of natural linguistic data from animation videos for children, from holistic (one-word expression) to compositional (two- to six-word one) gradually. The recognition and generation of sentences are a "probabilistic" constraint satisfaction process which is based on massively parallel DNA chemistry. Random sentence generation tasks succeed when networks begin with limited sentential lengths and vocabulary sizes and gradually expand with larger ones, like children's cognitive development in learning. This model supports the suggestion that variations in early linguistic environments with developmental steps may be useful for facilitating language acquisition.