Algorithmic bias is a major issue in machine learning models in educational contexts. However, it has not yet been studied thoroughly in Asian learning contexts, and only limited work has considered algorithmic bias based on regional (sub-national) background. As a step towards addressing this gap, this paper examines the population of 5,986 students at a large university in the Philippines, investigating algorithmic bias based on students' regional background. The university used the Canvas learning management system (LMS) in its online courses across a broad range of domains. Over the period of three semesters, we collected 48.7 million log records of the students' activity in Canvas. We used these logs to train binary classification models that predict student grades from the LMS activity. The best-performing model reached AUC of 0.75 and weighted F1-score of 0.79. Subsequently, we examined the data for bias based on students' region. Evaluation using three metrics: AUC, weighted F1-score, and MADD showed consistent results across all demographic groups. Thus, no unfairness was observed against a particular student group in the grade predictions.

Recent years, typhoon damages has become social problem owing to climate change. Especially, 9 September 2019, Typhoon Faxai passed on the south Chiba prefecture in Japan, whose damages included with electric and water provision stop and house roof break because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took eighteen days for recovery longer than past ones. Initial responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster, where an aerial photograph is partitioned into 4,096 grids that is 64 by 64, with each unit image of 48 meter square. Using target feature class probabilities, we can visualize disaster features map to scale the color range from blue to red or yellow. Furthermore, we can realize disaster feature mapping on each unit grid images to compute the convolutional activation map using Grad-CAM based on deep neural network layers for classification. This paper demonstrates case studies applied to aerial photographs recorded at the south Chiba prefecture in Japan after typhoon disaster.