A Design and Development of the Learning Contents Management based on the Personalized Online Learning

Teaching-learning methods are undergoing rapid transformation in terms of new information and communication technology and in accordance with onset of the 4th Industrial Revolution. The educational environment is being transformed into various forms, with examples being found not only in the existing traditional educational environment, but also in online education and blended learning. Existing online learning (LMS, LCMS) is offered in a limited contents transmission online educational environment, and has been limited but the level of support offered to a learner’s personalized learning. This study will overview existing flexible model of contents, suggest possible problems, and attempt to solve these problems. LCMS was designed and realized based on the open source Moodle platform, offering personalized contents to learners. LCMS is composed of the following 3 functions: contents registration of metadata inputted by administrator; search functionality for personalized learner contents; and personalized contents automatically being recommended to learners. As a result of the research, we made online learning environment that can provide customized learning recommendation and self - directed learning by increasing the continuity and efficiency of learning by automatically providing customized online contents to learners. Through this study, the learning of students promises to be effectively initiated by being based on available LCMS functions related to personalized educational contents in online education

Acupuncture Stimulation Attenuates Impaired Emotional-Like Behaviors and Activation of the Noradrenergic System during Protracted Abstinence following Chronic Morphine Exposure in Rats

The purpose of this study was to evaluate whether acupuncture stimulation attenuates withdrawal-induced behaviors in the rats during protracted abstinence following chronic morphine exposure. To do this, male rats were first exposed to morphine gradually from 20 to 100 mg/kg for 5 days, and subsequently naloxone was injected once to extend despair-related withdrawal behaviors for 4 weeks. Acupuncture stimulation was performed once at the SP6 (Sanyinjiao) acupoint on rat’s; hind leg for 5 min during protracted abstinence from morphine. The acupuncture stimulation significantly decreased despair-like behavior deficits in the forced swimming test and low sociability in the open-field test as well as increased open-arm exploration in the elevated plus maze test in the last week of 4-week withdrawal period. Also the acupuncture stimulation significantly suppressed the increase in the hypothalamic corticotropin-releasing factor (CRF) expression, the decrease in the tyrosine hydroxylase expression in the locus coeruleus, and the decrease in the hippocampal brain-derived neurotrophic factor mRNA expression, induced by repeated injection of morphine. Taken together, these findings demonstrate that the acupuncture stimulation of SP6 significantly reduces withdrawal-induced behaviors, induced by repeated administration of morphine in rats, possibly through the modulation of hypothalamic CRF and the central noradrenergic system

Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment

The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility of employing deep learning techniques to classify different workload levels, specifically normal state, low workload, and high workload. To the best of our knowledge, this study is the first attempt to classify workload levels of pilots. Our approach involves the hybrid deep neural network that consists of five convolutional blocks and one long short-term memory block to extract the significant features from electroencephalography signals. Ten pilots participated in the experiment, which was conducted within the simulated flight environment. In contrast to four conventional models, our proposed model achieved a superior grand--average accuracy of 0.8613, surpassing other conventional models by at least 0.0597 in classifying workload levels across all participants. Our model not only successfully classified workload levels but also provided valuable feedback to the participants. Hence, we anticipate that our study will make the significant contributions to the advancement of autonomous flight and driving leveraging artificial intelligence technology in the future.Comment: 5 pages, 3 figures, 1 table, 1 algorith