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arXiv · 2610.01147

Virtual Global Collaboration in Data Analytics and Machine Learning Education: A Mixed-Methods Study of Asynchronous Cross-Border Teamwork

Sehrish Basir Nizamani·Saad Nizamani·Khyati Goyal·Sarwat Nizamani·Zannah Zeiw
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Abstract

This innovative practice full paper examines a structured Virtual Global Collaboration (VGC) activity between undergraduate computing courses in the United States and Pakistan. The project was designed to support technical and intercultural skill development through asynchronous international teamwork in data analytics and introductory machine learning. Students worked in mixed-institution teams through a six-phase project including cultural orientation, dataset selection, data cleaning, analysis, introductory machine learning, and structured reporting. Using shared computational tools, teams coordinated across time zones to complete a joint data-driven project. Survey and qualitative reflection data were analyzed to examine collaborative experience, communication and cultural dynamics, learning outcomes, perceived value, and global readiness. Results show consistently positive student experiences across both cohorts, with collaborative processes strongly associated with learning and perceived value, and cross-cultural communication emerging as the primary driver of global readiness. These findings demonstrate how short-term, structured VGC can be effectively integrated into computing courses to support both technical learning and global competence.

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Sehrish Basir Nizamani, Saad Nizamani, Khyati Goyal, Sarwat Nizamani, Zannah Zeiw. 2026-10-01. Virtual Global Collaboration in Data Analytics and Machine Learning Education: A Mixed-Methods Study of Asynchronous Cross-Border Teamwork. https://arxiv.org/abs/2610.01147

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