김성완 교수

MOOCs have been the major issue in higher education worldwide since 2008. Frankly speaking, there are clear pros and cons of moving into MOOC-based education. This chapter deals with the pedagogical utilization and limitations of MOOCs in higher education. Through literature review and websites surfing related to MOOCs, expansion of MOOCs, instructional strategies in MOOCs, functions of platform for MOOCs, development of a MOOC, and future of MOOCs are summarized, analyzed, and discussed at the

Gravitational forces can impose physical stresses on the human body as it functions to maintain homeostasis. It has been reported that astronauts exposed to microgravity experience altered biological functions and many subsequent studies on the effects of microgravity have therefore been conducted. However, the anticancer mechanisms of simulated microgravity remain unclear. We previously showed that the proliferation of human Hodgkin's lymphoma (HL) cells was inhibited when these cells were cult

Motor imagery (MI) for target-oriented movements, which is a basis for functional activities of daily living, can be more appropriate than non-target-oriented MI as tasks to promote motor recovery or brain-computer interface (BCI) applications. This study aimed to explore different characteristics of brain activation among target-oriented kinesthetic imagery (KI) and visual imagery (VI) in the first-person (VI-1) and third-person (VI-3) perspectives. Eighteen healthy volunteers were evaluated fo

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We

A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because the non-invasive neural signal is less accurate. However the success rate could be sufficiently improved for practical applications by using additional sensors. Robot arm control based on hand trajectories predicted from EEG would allow for portabil

The purpose of this study is to develop and validate an evaluation tool of educational apps for smart education. Based on literature reviews, a potential model for evaluating educational apps was suggested. An evaluation tool consisting of 57 survey items was delivered to 156 students in middle and high schools. An exploratory factor analysis was then used to ascertain the importance of the criteria. Four exploratory constructs and eight subfactors were extracted from the data collected: Factor

In practical rehabilitation robot development, it is imperative to pre-specify the critical workspace to prevent redundant structure. This study aimed to characterize the upper extremity motion during essential activities in daily living. An IMU-based wearable motion capture system was used to access arm movements. Ten healthy subjects performed the Action Research Arm Test (ARAT) and six pre-selected essential daily activities. The Euler angles of the major joints, and acceleration from wrist a

Exposure to microgravity affects human physiology in various ways, and astronauts frequently report skin-related problems. Skin rash and irritation are frequent complaints during space missions, and skin thinning has also been reported after returning to Earth. However, spaceflight missions for studying the physiological changes in microgravity are impractical. Thus, we used a previously developed 3D clinostat to simulate a microgravity environment and investigate whether physiological changes o

Prediction of bacteremia is a clinically important but challenging task. An artificial intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding emergency department (ED) physicians in making timely decisions and reducing unnecessary medical costs. In this study, we developed and externally validated a Bayesian neural network-based AI bacteremia prediction model (AI-BPM). We also evaluated its impact on physician predictive performance considering both AI and ph