We explore the importance of online collaboration, cross-cultural understanding, and intercultural competence in today’s workplace, highlighting the need for Information Systems (IS) educators to integrate Collaborative Online International Learning (COIL) into their curricula. COIL offers numerous benefits by providing students with opportunities for joint projects and intercultural experiences including a global perspective, cross-cultural collaboration, exposure to innovative practices, global networking, intercultural competence development, and real-world application. Drawing on our own experiences, we present insights into recommended practices and potential pitfalls associated with COIL. Our goal is to empower IS educators to prepare students for the demands of a globalized workforce, cultivating a global mindset, cross-cultural collaboration skills, and intercultural competence in their field.

The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse of dimensionality, which significantly impacts IDS efficacy, and the limited effectiveness of singular learning classifiers in handling complex, imbalanced, and multi-categorical traffic datasets. To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. Furthermore, we introduce multi-stacking ensemble (MSE), a novel ensemble method, to enhance attack prediction capabilities. The evaluation on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrates that our IDS surpasses current models across various performance metrics.

We argue that many difficulties and frustrations that occurred during the transition to remote learning in early 2020 due to the coronavirus disease of 2019 (COVID-19) pandemic occurred due to misaligned expectations between students and professors. Many expectations that both professors and students had about the other did not account for the change in circumstances that the pandemic caused. Given the unique circumstances of learning from home, professors also needed to deal with misalignments in expectations from student families as well. We discuss these important differences and offer suggestions on how to best approach policies going forward as we inevitably have to make accommodations for students forced to make the switch in the coming semesters.