By employing learning analytics methodology and big data in Learning Management Systems (LMSs), this volume conducts data-driven research to identify and compare learner interaction patterns in Massive Private Online Courses (MPOCs). The uncertainties about the temporal and sequential patterns of online interaction, and the lack of specific knowledge and methods to investigate details of LMSs' dynamic interaction traces have affected the improvement of online learning effectiveness. While most research focuses on Massive Open Online Courses (MOOCs), little is investigating the learners' interaction behaviors in MPOCs. This book attempts to fill in the gaps by including research in the past decades, big data in education presenting micro-level interaction traces, analytics-based learner interaction in massive private open courses, and a case study. Aiming to bring greater efficiency and deeper engagement to individual learners, instructors, and administrators, the title provides a reference to those who need to evaluate their learning and teaching strategies in online learning. It will be particularly useful to students and researchers in the field of Education.

This issue of PET Clinics focuses on Lung Cancer, and is edited by Drs. Gang Cheng and Timothy Akhurst. Articles will include: FDG PET/CT for lung cancer staging; Lung neoplasms with low FDG avidity; FDG PET/CT evaluation of lung cancer in populations with high prevalence of granulomatous disease; Prognostic value of FDG PET/CT; Genomic characterization of lung cancer and its impact on the use and timing of PET in therapeutic response assessment; Treatment planning for radiation therapy; Future directions of PET imaging for lung cancer; PET for RT-planning in lung cancer; Genomic characterization of lung cancer and its impact on the use and timing of PET in therapeutic response assessment; and more!