• Journal of The Korean Association For Science Education
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• v.35 no.5
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• pp.907-917
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• 2015

Response to Intervention (RTI), which is focused on the gap between pre-interventions and post-interventions, provides an effective intervention program. This study takes under-achievement factors into consideration to determine the overall characteristics of underachievers. The under-achievement factors include cognitive learning factors, affective factors, and environmental factors. This study conducted curriculum-based assessments, achievement tests, and assessments on attitudes toward science and science learning motivation to verify the effects of individually tailored teaching according to the types of under-achievement in science. The experimental group was composed of six students in fourth grade, and the comparison group had 23 students. The findings of the study were as follows. First, the performance and progress of underachievers in the first-stage showed little progress and did not reach grade-level performance. Second, the underachievers in the second-stage greatly improved. In particular, the average of eight sessions in the second-step demonstrated performance beyond that of the regular child. Third, individually tailored teaching according to the types of under-achievement in science positively affected attitudes toward science and science learning motivation. This study will contribute to the improvement of the underachiever by applying individually tailored teaching according to the types of under-achievement in science.

• Proceedings of The KACE
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• 2017.08a
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• pp.221-224
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• 2017

본 논문은 사용자 수준에 적합한 맞춤형 학습코스를 추천하여 학습효과를 향상시킬 수 있는 추천모델을 개발하고, 효과분석을 위한 방안을 제시한다. 학습자 개개인의 학습수준이나 학습내용 등에 따라 적합한 학습주제를 선정하여 제공하는 것은 중요하나, 일반적인 추천은 전문가 그룹을 활용한 사람중심의 추천으로 시간이 오래 걸리는 등 자원의 비효율적 한계점[1]을 가지고 있다. 이를 극복하기 위해, TF-IDF를 이용해 단어별 가중치를 계산하여 고빈도 단어를 추출하여 벡터 공간에 배치시키고, Cosine Similarity 기법을 이용해 벡터간의 유사도를 측정하였다. 학습자 프로파일을 분석하고, 학습스킬간의 연관성을 고려하여 맞춤형 학습코스를 추천하기 위해, 워드 임베딩 기법을 적용하였고, 이를 위해 오픈소스 Gensim[2]을 이용하였다. 맞춤형 학습코스 추천 모델의 효과를 분석하기 위한 실험을 설계하고 평가 문항지를 개발하였다.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.85-94
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• 2005

Today the web-based learning management information systems are developed as forms of many ordered learning information systems acting up to the personal characteristics, but the previous systems have difficulty in their mutual working, maintaining, and repairing between the systems, because of their one-sided "push" method or reuse of the contents. Also they were not managed together having the dissimilar learning management systems each. Therefore, I made up for the weak points of the previous systems and embodied the international standard SCORM-based ordered learning management information systems. After adapting to sequencing contents, all systems are supplied; giving lectures, solving the problems, evaluating the learners and the function of successive personalization learning based on the result of the learner evaluation systems.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.155-162
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• 2011

It is proven that students learn more effectively when they apply Empirical knowledge which they have experienced or been facing. Therefore, the customized training supporting systems for information communication ethics education for primary school is developed. It provide resources on the internet such as teaching methodology, teaching materials, real life examples and applications related to information communication ethics education to the teachers. Each resource is categorized according to the topic and it improved information communication ethics education by providing customized resources on the topic. It is also helpful for users to select necessary resources by informing the suitability between the subject and the resources and to lift the burden on searching the data.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.49-60
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• 2020

With the advent of the knowledge-based era of the fourth industrial revolution, a paradigm shifts in university education. As a complete overhaul of university educational methods is required, strengthening competence through personalized is emerging as one of the solutions to the problem. To provide appropriate education accordingly focusing on individual learners, more studies at various levels are needed about understanding the characteristics of learners and ways to support them at universities. This study aims to conduct an exploratory research for adapting personalized learning at K University and explore effective ways to support. First, through literature review, the theoretical basis of personalized learning considering the diverse characteristics of learners and domestic and overseas cases of are examined. Secondly, a pilot study is conducted with K University students as subjects. FGI, study style diagnosis, one-on-one follow-up interviews are conducted and competency-based learning performance analysis and study style diagnosis result paper are provided to selected participants. Finally, major issues and implications are suggested to support the effective personalized learning of K university students.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.35-43
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• 2023

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.89-95
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• 2020

As the e-learning market expands, interest in customized education for learners based on artificial intelligence is increasing. Customized education for learners requires essential components such as a large amount of data and learning contents for learner analysis, and it requires time and cost efforts to collect such data. In this paper, to enable efficient learner-tailored learning even in small learning groups, unstructured learner data was analyzed using python modules, and a learning algorithm was presented based on this. Through the analysis of the unstructured learning data presented in this paper, it is possible to quantify and measure the unstructured data related to learning, and the accuracy of more than 80% was confirmed when analyzing keywords for providing customized education for learners.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.67-75
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• 2020

The 'level of difficulty' is one of the major factors for learners when selecting learning contents. However, the criteria for the difficulty level is mostly defined by the contents providers. This approach does not support the personalized education which should consider the abilities and environments of various learners. In this research, the knowledge of the learners and contents were formalized and generalized to resolve the issue, and object models, including a metric for personalized difficulty level, were designed in order to be applied for experiments. And then, based on 100 contents for music education and 20 learners, we performed simulations with an implemented tool to validate our approach. The experimental results showed that our method can calculate the personalized difficulty levels considering the similarities between the knowledges from the learning state and the contents. Our approach can be effectively applied to the on-line learning management system which contains easy access to the learning state and contents data.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.165-172
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• 2010

In Web-based learning systems, the techniques, as self-regulated learning, self-directed learning, are used to improve the effect of learner's study. These techniques are methods considering learner's study level but to consider the learner's study ability properly, the tailored course for learner should be applied. In this research, the learning system considering learner's study ability was proposed. To decide a learner's study ability, IRT(Item Response Theory) was applied and learning contents and question items were developed and applied by the degree of difficulty.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.561-569
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• 2010

The research aims to provide information about the most applicable education-method by assessing learner's objective and condition, and also by searching case studies. Even though education system is introduced in various web-sites, questions about which system being most appropriate for the learner has not been answered. Therefore, instead of only providing education contents, this research paper is to create a system that contains information about academic contents, education templates, reuse of abundant educational resources and an education system specified based on the level of the learner.