• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.65-73
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• 2014

The purpose of this study is to examine how time management, usefulness, and interest predict m-learning outcomes. For this study, 144 high school students participated in m-learning activities during science classes. After 5 week of classes, they responded the following surveys: time management, usefulness, interest, satisfaction, perceived achievement and learning persistence. Multiple regression analyses with correlation applied to this study as a data analysis method. The results showed that time management, usefulness, interest significantly predicted learning satisfaction and persistence. In addition, time management and usefulness significantly predicted perceived achievement, Therefore, these findings imply that time management, usefulness should be considered for designing m-learning activities in high school science class.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Journal of The Korean Association of Information Education
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• v.17 no.2
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• pp.115-123
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• 2013

With the rapid growth of mobile technologies, the mobile learning has been gradually considered as a efficient and effective learning form because it breaks the limitations of learning time and space occurring in the traditional classroom learning. Therefore, this research aims how the learners' m-learning efficacy, ubiquity, perceived usefulness, and ease of use predict perceived learning achievement and satisfaction Participants were 144 11th-grade students in A high school in Kyungnam area, Korea. After studying science class using mobile devices, they responded the following surveys: m-learning efficacy, ubiquity, perceived usefulness, ease of use, and satisfaction. Multiple regression analyses with correlation were applied to this study as a data analysis method. Findings of this study include: (a) m-learning efficacy and perceived usefulness predicted learning satisfaction, (b) perceived usefulness and ubiquity predicted perceived learning achievement. These findings imply that m-learning efficacy, perceived usefulness, ubiquity should be valued to enhance learning outcomes in mobile learning class.

• Research in Mathematical Education
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• v.27 no.1
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• pp.111-127
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• 2024

There is an increasing concern in the United States regarding the workforce's ability to maintain a competitive position in the global economy. This has led to an increased interest in effective science, technology, engineering, and mathematics (STEM) education. The purpose of this study was to investigate the effect of STEM project-based learning (PBL) on students' self-regulation and motivation to learn. Secondary students (n = 60) participated in a STEM summer camp in which STEM PBL was utilized. Results showed that students increased their self-regulation skills (t = 2.83, df = 59, p = .004) and motivation (t = 2.25, df = 59, p =.004), with Cohen's d effect sizes of 0.395 and 0.404, respectively. Student-centered learning and peer collaboration while solving real-world problems were likely the greatest contributing factors to the outcomes. Educators should utilize the results to provide opportunities for students to experience STEM PBL.

• Safety and Health at Work
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• v.13 no.4
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• pp.379-386
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• 2022

Emergency medical services (EMS) personnel are at high risk for adverse mental health outcomes during disease outbreaks. To support the development of evidence-informed mitigation strategies, we conducted a scoping review to identify the extent of research pertaining to EMS personnel's mental health during disease outbreaks and summarized key factors associated with mental health outcomes. We systematically searched three databases for articles containing keywords within three concepts: EMS personnel, disease outbreaks, and mental health. We screened and retained original peer-reviewed articles that discussed, in English, EMS personnel's mental health during disease outbreaks. Where inferential statistics were reported, the associations between individual and work-related factors and mental health outcomes were synthesized. Twenty-five articles were eligible for data extraction. Our findings suggest that many of the contributing factors for adverse mental health outcomes are related to inadequacies in fulfilling EMS personnel's basic safety and informational needs. In preparation for future disease outbreaks, resources should be prioritized toward ensuring adequate provisions of personal protective equipment and infection prevention and control training. This scoping review serves as a launching pad for further research and intervention development.

• Journal of Gastric Cancer
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• v.18 no.2
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• pp.172-181
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• 2018

Purpose: This study aimed to evaluate the surgical outcomes and investigate the feasibility of reduced-port laparoscopic gastrectomy using learning curve analysis in a small-volume center. Materials and Methods: We reviewed 269 patients who underwent laparoscopic distal gastrectomy (LDG) for gastric carcinoma between 2012 and 2017. Among them, 159 patients underwent reduced-port laparoscopic gastrectomy. The cumulative sum technique was used for quantitative assessment of the learning curve. Results: There were no statistically significant differences in the baseline characteristics of patients who underwent conventional and reduced-port LDG, and the operative time did not significantly differ between the groups. However, the amount of intraoperative bleeding was significantly lower in the reduced-port laparoscopic gastrectomy group (56.3 vs. 48.2 mL; P<0.001). There were no significant differences between the groups in terms of the first flatus time or length of hospital stay. Neither the incidence nor the severity of the complications significantly differed between the groups. The slope of the cumulative sum curve indicates the trend of learning performance. After 33 operations, the slope gently stabilized, which was regarded as the breakpoint of the learning curve. Conclusions: The surgical outcomes of reduced-port laparoscopic gastrectomy were comparable to those of conventional laparoscopic gastrectomy, suggesting that transition from conventional to reduced-port laparoscopic gastrectomy is feasible and safe, with a relatively short learning curve, in a small-volume center.

• Electronics and Telecommunications Trends
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• v.33 no.3
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• pp.89-99
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• 2018

Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

• Journal of Engineering Education Research
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• v.16 no.3
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• pp.3-9
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• 2013

It is very difficult for students to understand "Computer System Architecture" written by M. Morris Mano only through theoretical education. This study has developed DigCom as a computer architecture practice kit to make it easier for students to understand computer operations. The practice kit operates in the same manner as the "simple computer" described in Computer System Architecture. The purpose of this study is to examine whether using DigCom is effective in improving learning outcomes. For the purpose, t-test and two-way ANOVA were used to determine if there is any difference in learning outcomes between the experimental group of students who used DigCom and the control group who took the course in a traditional setting without using DigCom. The main conclusions of the study are as follows: Students understand computer operations better when DigCom is used in class.

• Journal of Gastric Cancer
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• v.15 no.3
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• pp.151-158
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• 2015

Purpose: Obesity is associated with morbidity following gastric cancer surgery, but whether obesity influences morbidity after laparoscopic gastrectomy (LG) remains controversial. The present study evaluated whether body mass index (BMI) and visceral fat area (VFA) predict postoperative complications. Materials and Methods: A total of 217 consecutive patients who had undergone LG for gastric cancer between May 2003 and December 2005 were included in the present study. We divided the patients into two groups ('before learning curve' and 'after learning curve') based on the learning curve effect of the surgeon. Each of these groups was sub-classified according to BMI (<$25kg/m^2$ and ${\geq}25kg/m^2$) and VFA (<$100cm^2$ and ${\geq}100cm^2$). Surgical outcomes, including operative time, quantity of blood loss, and postoperative complications, were compared between BMI and VFA subgroups. Results: The mean operative time, length of hospital stay, and complication rate were significantly higher in the before learning curve group than in the after learning curve group. In the subgroup analysis, complication rate and length of hospital stay did not differ according to BMI or VFA; however, for the before learning curve group, mean operative time and blood loss were significantly higher in the high VFA subgroup than in the low VFA subgroup (P=0.047 and P=0.028, respectively). Conclusions: VFA may be a better predictive marker than BMI for selecting candidates for LG, which may help to get a better surgical outcome for inexperienced surgeons.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.692-695
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• 2024

With the recent advancements in artificial intelligence (AI), the performance of deep learning-based audio deepfake technology has significantly improved. This technology has been exploited for criminal activities, leading to various cases of victimization. To prevent such illicit outcomes, this paper proposes a deep learning-based audio deepfake detection model. In this study, we propose CoNSIST, an improved audio deepfake detection model, which incorporates three additional components into the graph-based end-to-end model AASIST: (i) Squeeze and Excitation, (ii) Positional Encoding, and (iii) Reformulated HS-GAL, This incorporation is expected to enable more effective feature extraction, elimination of unnecessary operations, and consideration of more diverse information, thereby improving the performance of the original AASIST. The results of multiple experiments indicate that CoNSIST has enhanced the performance of audio deepfake detection compared to existing models.