• Journal of Distribution Science
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• v.21 no.4
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• pp.11-20
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• 2023

Purpose: 1) develop a learning model involving design thinking to develop creative economy innovation and the characteristics of digital entrepreneurs. 2) evaluate the impact of design thinking on creative economy innovation 3) evaluate the impact of design thinking on digital entrepreneurial ability. Research design, data and methodology: 1) develop a learning model involving design thinking in order to develop creative economy innovation and the characteristics of digital entrepreneurs. 2) Evaluating creative economy innovation involving design thinking. 3) Assessing the characteristics of digital entrepreneurs based on design concepts. Results: 1) the development of a learning model involving design thinking to develop creative economy innovation and digital entrepreneurial competency 2) The students who studied using the learning model involving a design thinking process had the highest overall scores in terms of creative economy innovation 3) The scores for the assessment of digital entrepreneurial activity for the students who studied by using the design thinking learning model were at a high level. Conclusions: The development of the design thinking learning model can encourage students to be able to develop creative economy innovations and to empower digital entrepreneurs' ability for distribution strategy. Educational institutions that would like to succeed in developing creative economy innovative and digital entrepreneurship characteristics with the support of design thinking.

• The Journal of Korean Association of Computer Education
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• v.22 no.5
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• pp.51-65
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• 2019

This study aims to provide educational implications for more strategic online software education by the types of online learning according to learners' self-regulated learning characteristics in the online software education environment and examining the characteristics of each type. For this, variables related to self-regulated learning characteristic were extracted from the log data of 809 students participating in the online software learning program of K University, and then analyzed using hierarchical cluster analysis. Based on hierarchical cluster analysis learner clusters according to the characteristics of self-regulated learning were derived and the differences between learners' learning characteristics and learning results according to cluster types were examined. As a result, the types of self-regulated learning of online software learners were classified as 'high level self-regulated learning type (group 1)', 'medium level self-regulated learning type (group 2)', and 'low level self-regulated learning type (group 3)'. The achievement level was found to be highest in 'high-level self-regulated learning type (group 1)' and 'low-level self-regulated learning type (group 3)' was the lowest. Based on these results, the implications for effective online software education were suggested.

• The Journal of Korean Association of Computer Education
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• v.13 no.2
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• pp.15-23
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• 2010

Computational Literacy education is being required in current digital age, but the educational strategy of it is lacking. In traditional education, instructors have been teaching by considering learners' characteristics for effective learning. It is necessary to investigate their characteristics for applying this method to computational literacy education. Therefore, we taught programming that is main area on computational literacy, and analyzed learners' characteristics focused on Felder's learning style and multiple intelligence. That is, we taught 194 university students computational literacy with scratch that was one of the popular educational programming languages, and analyzed the relation among learning style, multiple intelligence and the students' programming performance. Also, we found considerations through comparing students' characteristics with experts' ones.

• East Asian mathematical journal
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• v.38 no.2
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• pp.187-214
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• 2022

This study compares the functions of the Learning Management Systems (LMS) in three widely used Edu-Tech platforms, that support students' individualized learning by using the learning characteristics of the students. The rapid advances in artificial intelligence (AI) are broadening their impacts in the education industry, and play a broad role in supporting student learning. In many countries, online classes have become a norm due to the COVID-19 crisis, and the demand for Edu-Tech in classes has increased rapidly. As a result, many countries, including South Korea, are now preparing and implementing various policy measures to adopt Edu-Tech in the class setting. Therefore, in this study, we analyze and compare the structures and characteristics of the three widely used Edu-Tech platforms that support individualized mathematics learning. In particular, we compare the LMSs of the three platforms by considering the elements such as learning design, learning management, learner analysis, learning result analysis, and student management functions. The results of this study give implications in the future directions to take on how to build Edu-Tech platform models that promote students' individualized mathematics learning in public education.

• Educational Technology International
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• v.10 no.1
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• pp.1-23
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• 2009

Learners are often posited in a paradoxical situation where they are not fully involved in decision making processes on how to learn, in designing their tools. Cognitive artifacts in e-learning are supposed to effectively support learner-centered e-learning. The purpose of the study is to analyze cases of cognitive artifacts and to inquire those design principles for facilitating the learner-centered e-learning. Four research questions are suggested: First, it will be analyzed the characteristics of learners with respect to design of cognitive artifacts for supporting the learner-centered e-learning. Second, characteristics of four cases to design cognitive artifacts in learner-centered e-learning environment are analyzed. Third, it will be suggested the appropriate design principles of cognitive artifacts to facilitating learner-centered learning in e-learning environment. Four cases of cognitive artifacts design in learner-centered e-learning was identified as follows: Wiki software as cognitive artifacts in computer-supported collaborative learning; 'Play Around Network (PAN)' as cognitive artifact to monitor learning activities in knowledge community; Knowledge Forum System (KFS) as a cognitive artifact in knowledge building; cognitive artifacts in Courses-as-seeds applied meta-design. Five design principles are concluded as follows: Promoting externalization of cognitive artifacts to private media; Helping learners to initiate their learning processes; Encouraging learners to make connections with other learners' knowledge building and their cognitive artifacts; Promoting monitoring of participants' contributions in collaborative knowledge building; Supporting learners to design their cognitive artifacts.

• Journal of the Korean Institute of Educational Facilities
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• v.16 no.1
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• pp.79-86
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• 2009

The purpose of this study is to suggest the design guidance of education facilities by analysing Alvaro Siza's education facilities of which considered having idiosyncratic spatial characteristics. Focusing on the his planning of learning and transitional space of education facilities, this study aims at eliciting the spatial characteristics of his architecture. In doing so, this study tries to figure out his basic method of reification of his basic architectural concept which is articulated in learning space and transitional space of education facilities by introducing the boundary element and penetration of light in order to support student's learning activity and foster abundant cognitive experiences. Therefore, this study presents the feasible supplementary design method for future education facilities to be appropriate not just for quantitative factors, but for qualititative aspects such as user's psychological fulfillment, and emotional satisfaction.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.3-11
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• 2023

To address the high accident rate in the construction industry, there is a growing interest in implementing virtual reality (VR)-based construction safety training. However, existing training approaches often failed to consider learners' individual characteristics, resulting in inadequate training for some individuals. This study aimed to investigate the impact of personal characteristics on learning performance in VR-based construction safety training using machine learning and SHAP (SHAPley Additional exPlanations). This study revealed that age exerted the greatest influence on learning performance, while work experience had the least impact. Furthermore, age exhibited a negative relationship with learning performance, indicating that the introduction of VR-based construction safety training can be effective for younger individuals. On the other hand, academic degree, qualifications, and work experience exhibited a positive relationship. To enhance learning performance for individuals with lower academic degree, it is necessary to provide content that is easier to understand. The lower qualifications and work experience have minimal impact on learning performance, so it is important to consider other learners' characteristics so as to provide appropriate educational content. This study confirmed that personal characteristics can significantly affect learning performance in VR-based construction safety training, highlighting the potential for leveraging these findings to provide effective safety training for construction workers.

• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.36-39
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• 2000

The adaptive learning circuit is designed the basis of modeling of MFSFET (Metal-Ferroelectric-Semiconductor FET) and the numerical results is analyzed. The output frequency of the adaptive learning circuit is inversely proportioned to the source-drain resistance of MFSFET and the capacitance of the circuit. The output frequency modulation of the adaptive learning circuit is investigated by analyzing the source-drain resistance of MFSFET as functions of imput pulse numbers in the adaptive learning circuit and the dimensionality factor of the ferroelectric thin film. From the results, the frequency modulation characteristics of the adaptive learning circuit, that is, adaptive learning characteristics which means a gradual frequency change of output pulse with the progress of input pulse are confirmed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.180-185
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• 2012

Social Learning is a new study model of future knowledge information society. In different existing study, it concentrate on relationship with others and design to connect studying with social effect as a study platform using social media such as Blog, SNS, UCC, Microblog. In my paper, social learning characteristics are described to understand social learning, that is 3 keyword such as context, connectivity, collaboration. Also we investigate social media characteristics and social media how to be used social learning. Also social learning system building method using facebook is presented.