Machine translation post-editing(MTPE) service is already available in the Korean market. However, it is still challenging to have access to reliable Korean to English MTPE productivity and quality data. Therefore, it is premature to assume that Korean to English MTPE service will be more productive and of similar quality to human translation. To make matters worse, the lack of understanding about MTPE can misguide the market and future translation training. To that end, this research tries to shed light on crucial efforts required for MTPE: temporal, technical, and cognitive. First, the temporal data and expert quality assessment will provide basic insight about productivity between human translation and MTPE. Second, the keystroke data will show the difference in keystrokes between the two translation modes, if there are any. Third, the pause data will tell us if participants made different levels or patterns of cognitive efforts. Finally, based on these data sets, this research will suggest what can be a reasonable way to understand Korean to English MTPE.