Although MOOCs platforms offer a unique way to provide information for a large cohort of participants, only a small percentage of participants complete MOOCs. The high number of dropouts in MOOCs is a key challenge, and the literature suggests that it can be affected by participants' motivation. However, it is not known how and to what extent motivation influences participants’ dropout in MOOCs. There is a need to provide an overview of the role of motivation in MOOCs’ retention. In this study, we aimed to identify motivational factors and theories that affect participants’ retention in MOOCs and explain how does motivation supports participants to complete MOOCs. To do so, a systematic review was conducted using specific inclusion and exclusion criteria and a set of relevant keywords and databases which resulted in 50 relevant publications. Our analysis led us to identify six main motivational factors that influence participants’ MOOCs completion including academic, social, course, personal, professional, and technological motives. These factors were divided into two main categories including need-based motivation and interest-based motivation. The results showed that academic motives play the most important role in participants’ MOOCs retention compared to the other factors. It was also found that self-determination theory was used as the most dominant theory to support participants’ motivation for MOOCs completion. In addition, the results revealed that the motivational factors not only impacts participants’ MOOCs retention directly, but also this impact is mediated by participant satisfaction, self-regulation, attitude toward using MOOCs, performance, engagement, and level of participation. Based on the results, further implications for practice and future research are provided.
We investigate the noise transfer mechanism from the light source intensity fluctuations to the acoustic signal in Fourier transform photoacoustic spectroscopy (FT-PAS). This noise coupling is expected to be reduced in FT-PAS compared with conventional Fourier transform spectroscopy, as only the specific spectral components that are absorbed by the probed sample contribute to the noise level. We employ an incoherent supercontinuum (SC) light source in our experiments and observe a linear relation between the sample gas concentration and the detected noise level, which significantly reduces the influence of the SC noise on the detection limit. Based on our experimental results, we derive a model for the noise level, which establishes the foundation for practical sensitive implementation of FT-PAS.
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