Abstract.Many massive open online courses (MOOCs) offer mainly videobased lectures, which limits the opportunity for interactions and communications among students and instructors. Thus, the discussion forums of MOOC become indispensable in providing a platform for facilitating interactions and communications. In this research, discussion forum users who continually and actively participate in the forum discussions throughout the course are identified. We then employ different measures for evaluating whether those active users have more influence on overall forum activities. We further analyze forum votes, both positive and negative, on posts and comments to verify if active users make positive contributions to the course conversations. Based the result of analysis, users who constantly participate in forum discussions are identified as statistically more influential users, and these users also produce a positive effect on the discussions. Implications for MOOC student engagement and retention are discussed.

Population stratification is a growing concern in genetic-association studies. Averaged ancestry at the genome level (global ancestry) is insufficient for detecting the population substructures and correcting population stratifications in association studies. Local and phase stratification are needed for human genetic studies, but current technologies cannot be applied on the entire genome data due to various technical caveats. Here we developed a novel approach (aMAP, ancestry of Modern Admixed Populations) for inferring local phased ancestry. It took about 3 seconds on a desktop computer to finish a local ancestry analysis for each human genome with 1.4-million SNPs. This method also exhibits the scalability to larger datasets with respect to the number of SNPs, the number of samples, and the size of reference panels. It can detect the lack of the proxy of reference panels. The accuracy was 99.4%. The aMAP software has a capacity for analyzing 6-way admixed individuals. As the biomedical community continues to expand its efforts to increase the representation of diverse populations, and as the number of large whole-genome sequence datasets continues to grow rapidly, there is an increasing demand on rapid and accurate local ancestry analysis in genetics, pharmacogenomics, population genetics, and clinical diagnosis.