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This paper reports on the outcomes of a Collaborative Online International Learning Activity (COIL), conducted between a Japanese and an American university. Both schools gave their students a total of eight weeks of activity. Sixty students from two institutions participated in this study. During each week, students received various cultural topics to discuss, and students from both schools exchanged opinions on Zoom. The Social Networking Approach (SNA) ("3x3+3") was used to assess students' competencies: the three content realms contain (1) linguistic; (2) cultural; and (3) global social domains, and within each of these three domains, three different skills: (i) "Can Understand"; (ii) "Can Do"; (iii) "Can Connect", that need to be developed in language education. Our findings show that Zoom Talk provided a positive experience for the students by facilitating connectivity among the participants. However, Japanese students were more confident in their "can understand" than "can do" skills whereas the American data was entirely positive, making determination difficult. [ABSTRACT FROM AUTHOR]

Understanding traditional culture is important. Various methods are used to achieve better cross-cultural understanding, and certain researchers have studied human behavior. However, behavior does not always represent a culture. Therefore, our study aims to understand Japanese greeting culture by classifying it through machine learning. Following are our study contributions. (1) The first study to analyze cultural differences in greeting gestures based on the politeness level of Japanese people by classifying them. (2) Classify Japanese greeting gestures eshaku, keirei, saikeirei, and waving hand. (3) Analyze the performance results of machine and deep learning. Our study noted that bowing and waving were the behaviors that could symbolize the culture in Japan. In conclusion, first, this is the first study to analyze the eshaku, keirei, saikeirei, and waving hand greeting gestures. Second, this study complements several human activity recognition studies that have been conducted but do not focus on behavior representing a culture. Third, according to our analysis, by using a small dataset, SVM and CNN methods provide better results than k-nearest neighbors (k-NN) with Euclidean distance, k-NN with DTW, logistic regression and LightGBM in classifying greeting gestures eshaku, keirei, saikeirei, and waving hand. In the future, we will investigate other behaviors from different perspectives using another method to understand cultural differences. [ABSTRACT FROM AUTHOR]