• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.1-10
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• 2021

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students' academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data.

• 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 Engineering Education Research
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• v.25 no.3
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• pp.3-10
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• 2022

An educator may utilize various pedagogies such as hands-on practice, practicum, and project-based learning to enhance a student's competency. Among various pedagogies, project-based learning is one of the well-known pedagogies that may provide similar on-the-job experience. In general, an educator may divide the students into small groups and assign tasks to check students' cooperation skills and achievements during project-based learning. However, an educator may experience difficulties operating project-based learning due to the COVID-19 pandemic. Most of the team activities are done in non-face-to-face meetings, and as a consequence, the educator may not find an underachieving team easily and cannot intervene appropriately. This study introduces a rigorous analysis method to evaluate team activities to analyze individual students' participation and contributions. First, this study develops evaluation rubrics by conducting questionnaires to professors and students to find an appropriate weight value for the evaluation scale. Then, this research introduces an analysis environment to evaluate students automatically. The analysis environment collects dialog data from social network services and measures interactions among students in a team. After measuring interactions, the environment generates a report to visualize the team activities. We applied the proposed method and environment to the capstone design course to show the effectiveness of the method and environment. Based on the case study, the environment showed that the analysis method could easily indicate the teams' activities and check the level of participation.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.395-402
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• 2024

The purpose of this study is to verify the effect of the intensive support program for junior college students with low academic performance on students' learning motivation and self-learning efficacy, and to explore its meaning. This study was conducted with 46 students who participated in the JUMP-UP program supported by the D University Teaching and Learning Development Center in Busan for backers and students with low grades. The research question of this study is, first, does the JUMP-UP program affect the reinforcement of the learning motivation of junior college students? Second, does the JUMP-UP program affect the self-learning efficacy of junior college students? As a result of examining the effectiveness by conducting a survey before and after participating in the JUMP-UP program, the JUMP-UP program showed statistically significant changes in all items of participating learners' learning motivation and self-learning efficacy. Through this, it can be seen that an intensive support program such as the JUMP-UP program is valuable as a support program suitable for improving the learning motivation and self-learning efficacy of vocational college students suffering from low academic performance.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.207-214
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• 2012

This study tried to propose an optimal instruction-learning model for the cyber home learning system2.0 through grounded theory. In-depth interviews were conducted to investigate causes of underachievement and the causes were categorized according to common concepts. A total of 25 causes of underachievement could be grouped into four categories and eight sub-categories, as a result. Underachievers, then, participated in the lessons utilizing the cyber home learning system2.0 and their cognitive change process about learning was analyzed from reflectional journals and in-depth interviews with a teacher. It was found that underachievers were participated in learning by passing through 5 processes; adaptation to the cyber home learning system2.0, basic knowledge learning, task implementing, rounds of group discussions, feedbacks and evaluation. Based on analysis of these five processes, this study proposed a conditional matrix for the cyber home learning system 2.0 as the most personalized model for underachieving students.

• Journal of Gifted/Talented Education
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• v.17 no.2
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• pp.338-364
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• 2007

The purpose of this study is to investigate current trends and future directions of research in the area of gifted education through the analysis of published manuscripts on giftedness and gifted education between $2000{\sim}2006$. About 521 articles among 35 journals and 49 dissertations listed in the Korea Education and Research Information Service, including the journal of gifted/talented education and the journal of giftedness and gifted education, were mainly analyzed in the present study. The articles were examined by topics, domains, ages, and research methods both yearly and synthetically. The most widely researched topic was curriculum and program issues in gifted education, and the topic related to factors and development of giftedness was the second. Most studies have continuously focused on the mathematically and scientifically gifted students, and studies on gifted students in the areas of art, language, and other domains were scant. Issues on underachieving gifted students and underachievement were researched actively in 2005. More research has utilized elementary students as samples rather than middle or high school students. Young children under 7 have attracted much attention by researchers after 2004. Related to research methods, literature review was the most widely used, survey was the second, and experimental and correlational studies were the next. Implications related to results were discussed in depth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.819-825
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• 2020

This study explored the possibility of psychological testing as a way to proactively support underachieving students. Among the four-year college students that participated in our study, 43 students who participated in the academic warning support program for the second semester of 2019 and 30 students who had no academic warning experience used the data from the study personality type test. For data analysis, technical statistics, t-test, and correlation analysis were performed using jambi 1.1.9.0 to obtain the Pearson correlation. Studies have shown that the two classes of students differ in their learning behavior patterns. A student with a bachelor's degree warning scored high in the rest of the class, except for rebelliousness, perfection, mixed thoughts, hard work, satisfaction, single-mindedness and type satisfaction. This can be seen as a factor in the psychological conflict, such as the discrepancy between what one likes and what one studies, and lack of available support. It has been confirmed that psychology, emotional parts and economic support are needed as well as learning skills. In addition, this study is expected to provide basic data for proper preemptive support in schools, such as the prevention of dropouts and counseling programs.