As MOOCs (Massive Open Online Courses) grow rapidly around the world, the language barrier is becoming a serious issue. Removing this obstacle by creating translated subtitles is an indispensable part of developing MOOCs and improving accessibility. Given the large quantity of MOOCs available worldwide and the considerable demand for them, machine translation (MT) appears to offer an alternative or complementary translation solution, thus providing the motivation for this research. The main goal of this research is to test the impact machine translated subtitles have on Chinese viewers’ reception of MOOC content. More specifically, the author is interested in whether there is any difference between viewers’ reception of raw machine translated subtitles as opposed to fully post-edited machine translated subtitles and human translated subtitles. Reception is operationalized by adapting Gambier's (2007) model, which divides ‘reception’ into ‘the three Rs’: (i) response, (ii) reaction and (iii) repercussion. Response refers to the initial physical response of a viewer to an audio-visual stimulus, in this case the subtitle and the rest of the image. Reaction involves the cognitive follow-on from initial response, and is linked to how much effort is involved in processing the subtitling stimulus and what is understood by the viewer. Repercussion refers to attitudinal and sociocultural dimensions of AVT consumption. The research contains a pilot study and a main experiment. Mixed methods of eye-tracking, questionnaires, translation quality assessment and frequency analysis were adopted. Over 60 native Chinese speakers were recruited as participants for this research. They were divided into three groups, those who read subtitles created by raw MT, post-edited MT (PE) and human translation (HT). Results show that most participants had a positive attitude towards the subtitles regardless of their type. Participants who were offered PE subtitles scored the best overall on the selected reception metrics. Participants who were offered HT subtitles performed the worst in some of the selected reception metrics.