• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.229-238
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• 2018

The purpose of this study is to investigate the mediating effect of learners' learning flow in the effect of presence on academic achievement in web-based e-learning. For this purpose, this study analyzed the influencing relationship between the each factor based on the structural model with the learning flow as a mediator variable. Based on existing theoretical studies, learning satisfaction and academic achievement, which represent learning outcomes, are set as dependent variables, and teaching presence, cognitive presence, and social presence are set as independent variables. Data collected from a total of 256 e-learning learners were used in the analysis of this study. According to the results of the analysis, teaching presence, cognitive presence, and social presence were found to have a significant effect on academic achievement when a learning flow is a mediator variable. Concretely, teaching presence, cognitive presence, and social presence have a positive effect on the learning flow, while learning flow has a positive effect on learning satisfaction. On the other hand, learning flow has a negative effect on academic achievement. As a result of verifying the mediating effect of learning flow on the relationship between presence, learning satisfaction, and academic achievement, there was meditating effect in the aggregate. This study implies that in order to increase the level of learning satisfaction and academic achievement, it is necessary to make the teaching-learning design in the provision of contents and materials for e-learning so that the learner can feel the presence. The results of this study can be used as a basic data for seeking support and promotion strategies for enhancement of future learning flow and presence.

• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.189-196
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• 2019

Artificial intelligence has begun to be used in various parts of our lives, and recently its sphere has been expanding. However, students tend to find it difficult to recognize artificial intelligence technology because education on artificial intelligence is not being conducted on elementary school students. This paper examined the teaching programming language and artificial intelligence teaching methods, and looked at the changes in students' attitudes toward artificial intelligence technology by conducting education on artificial intelligence. To this end, education on block-type programming language-based artificial intelligence technology was provided to students' level. And we looked at students' attitudes toward artificial intelligence technology through a single group pre-postmortem. As a result, it brought about significant improvements in interest in artificial intelligence, possible access to artificial intelligence technology and the need for education on artificial intelligence technology in schools.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.283-291
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• 2021

In this paper, we propose the development and application of a software education program for underachievers. The software education program for underachieving students was developed in consideration of the characteristics of learner's suffering from underachievement and the educational effects of software education, and is meaningful in that it proposes a plan to improve the learning gap in distance learning. Learners can acquire digital literacy and learning skills by solving structured tasks in the form of courseware, intelligent tutoring, debugging, and artificial intelligence learning models in educational programs. Based on the effects of software education, such as enhancing logical thinking ability and problem solving ability, this program provides opportunities to solve fusion tasks to underachievers. Based on this, it is expected that it can have a positive effect on the overall academic work.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.309-315
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• 2021

This study is about the direction of how to compose the information domain curriculum in the domestic subject matter centered curriculum system. To this end, subject-centered and competency-centered curriculum were compared and analyzed, and how the information domain was organized in two types was suggested. In spite of emphasizing competency, the domestic curriculum was judged to be inclined to the subject-centered curriculum because it emphasized the presentation of national-level educational goals, a subject learning model, and textbooks. As examples of the information domain subject-centered curriculum, the information domain of the elementary practical subject and the middle school information curriculum were presented, and the SW convergence curriculum was presented as an example of a progressive curriculum. Under such circumstances, it was emphasized that in order for the learner to lead a life in an intelligent society in the future through the information domain including SWAI content, it must be explicitly described in a subject-centered perspective with computer science as the parent study.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.83-92
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• 2022

With the digital transformation of the entire industry, software competency has become the core competency for the future talent. However, it is difficult to find researches related to the corporate education for improving employee's software capability. Therefore, this study tried to verify the relationship between factors affecting the learning performance of employees in online software education. For this purpose, a survey of 223 employees with online software education experience was analyzed using the SPSS PROCESS macro. As a result of analysis, perceived usefulness and self-regulated learning have been found to have a significant multiple mediating effect on learning performance by digital literacy and satisfaction. This suggests that not only learner factors but also the characteristics of education should be considered. The results of this study are expected to be helpful in designing effective online education programs.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.53-62
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• 2021

Distance learning (DL) has become a major issue in the educational field with the spread of COVID-19. In order to enhance the satisfaction of DL learners, efforts to cultivate learners' competencies, as well as investment to build IT infrastructure, and activities to support high-quality content provision should be comprehensively considered. Based on a survey of 221 college students, this study verified that digital literacy (knowledge, skill, and mind) and information systems success factors (system, information, and service quality) all positively affect DL satisfaction, in turn, which positively influences on DL continuance. This study is meaningful in that it comprehensively considered learner's ability and IT infrastructure and analyzed the effect on the satisfaction and intention of continuous use of DL. In the future, it is necessary to expand the target of not only college students but also elementary and secondary students and instructors, and to further consider interaction, which is a major factor in the distance learning process.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.351-359
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• 2022

In this paper, this app is an online learning tool web application that helps learners learn efficiently. It discusses how learners can improve their learning efficiency in these three aspects: retrieval practice, systematization, metacognition. Through this web service, learners can proceed with learning with a flash card-based retrieval practice. In this case, a method of managing a flash card in a form similar to a directory-file system using a composite pattern is described. Learners can systematically organize their knowledge by converting flash cards into a mind map. The color of the mind map varies according to the learner's learning progress, and learners can easily recognize what they know and what they do not know through color. In this case, it is proposed to build a deep learning model to improve the accuracy of an algorithm for determining and predicting learning progress.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.180-186
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• 2022

This study is a pilot study to confirm the effectiveness of training after applying emergency simulation training for inactive nurses and to present a new model of simulation training operation method. In this study, the control group is a group that directly participates in the simulation activity, and the experimental group is the group that observes the control group's simulation activity. Experimental group and control group were matched 1:1 to experience all the roles of the resuscitation team. The study participants were 5 inactive nurses in the experimental group and 5 inactive nurses in the control group, and the total training time was 5 hours. The emergency simulation operation composition consists of theory education, skill education, and simulation. The interview was conducted. The educational satisfaction of the participants was 4.65 points for theory education and 4.70 points for practical education based on 5 points. Participants' performance confidence improved from 3.60 points before operation to 7.20 points after operation. Emergency simulation operation consisted of pre-test, theory education, skill education, simulation implementation, debriefing, and post-test. Participants expressed that the choice of group greatly reduced the burden and anxiety about performing the role of the resuscitation team. However, difficulties and inexperience in the operation of the defibrillator were reported in the experimental group. The control group reported that the simulation activity of the experimental group was not significantly different from theirs. Through the results of this study, it was confirmed that emergency simulation education not only reduced the burden and anxiety of inactive nurses, but also had an effect of education. Based on the research results, it is proposed to expand the participants and verify the effectiveness of education through specific variables such as learning commitment, learner confidence, simulation satisfaction, and team effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.