This case study explores possible correlation between a Taiwanese university student's choice of seating in a classroom and his/her mark performance, which could be a proxy variable of the student's learning effectiveness. While there are numerous researches regarding seating location, few focuses have been put on the influence of action zone on classroom performances. The action-zone phenomenon emerges when an instructor naturally tends to pay attention to the perceptual field before him/her. The action zone might be swayed by classroom seating position and teachers' walking route. By constructing students' learning effectiveness as a proxy variable, this study investigates the ramifications of action zone and the stability of seating selection on students' mark performances , observing class grades and grade point average. Data had been collected for over ten weeks from 34 third-year undergraduates in a statistics B.S. degree program in Taiwan during their time spent in Applied Time Series Analysis, an elective course for statistics major students. According to the research results, the action zone in a classroom impacts students' learning performances. In addition, this impact exists in terms of both seating patterns and the distance between seats and teachers. The stability of students' seating choice is of equal importance. The study also investigates the implications of the stability of seating selection for educators and students.

Prof. K. Thomas Wong earned a B.S.E. degree from UCLA (USA) and a Ph.D. from Purdue University (USA). He has/had been a regular member of the faculty at Nanyang Technological University (Singapore), Chinese University of Hong Kong, University of Waterloo (Canada), Hong Kong Polytechnic University, Beihang University (China), and now De La Salle University (the Philippines). Thomas' research interest revolves around statistical signal processing. He has authored 100+ papers in SCI journals, with 5500+ citations and an H-index of 33. He is/was on the editorial boards of 10+ SCI journals, including 5 IEEE journals. He was a Deputy Editor-in-Chief for IET Signal Processing (UK). He is a Fellow of The Acoustical Society of America (USA) and a Fellow of The Institution of Engineering and Technology (UK).

Electronic devices such as CCTVs and GPS trackers are becoming more prevalent in public utility vehicles. These electronic devices produce data that can be aggregated and processed to generate insights regarding the service quality provided by public transportation systems. We describe a methodology for processing CCTV-produced videos and GPS logs, and utilize it to perform a first order, limited assessment of the Philippines' Service Contracting Program, which ran during the COVID-19 pandemic. Our analyses uncovered vast discrepancies between data-supported figures and manually-generated estimates provided by the transport operators, especially with regards to vehicle occupancy and total number of commuters served. Our study also uncovered imperfections in the data generated by the electronic devices, which place caveats or limitations on the insights generated by the data analyses. We provide and suggest ways on how the analyses can be modified to take into account such missing data, thus enabling us to come up with educated, reasonable insights despite incomplete input datasets.

Wilson M. Tan, Ph.D., is an Associate Professor in the Department of Science, College of Engineering, University of the Philippines Diliman. He has led several research projects funded by the Department of Science and Technology, and the Commission on Higher Education. He has also collaborated in research efforts with institutions such as the Advanced Science and Technology Institute, Samsung Research Philippines, and the Department of Transportation. In addition to his post in the Department of Computer Science, Dr. Tan is also currently a Research Fellow in the National Center of Transportation Studies. His research interests include general networking technologies, as well as Internet of Things, Future Internet Technologies, and transportation analytics.