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Higher education institutions are racing to keep pace with the disruption caused by artificial intelligence (AI) tools. A 2023 QuickPoll survey by Educause found that 83% of higher education stakeholders believe generative AI will "profoundly change" the sector over the next three to five years. Additionally, 65% agreed that "the use of generative AI in higher ed has more benefits than drawbacks" (McCormack, 2023, Table 1). While institutions are exploring AI's potential in areas such as admissions, enrollment, administrative duties, scheduling, and institutional data research, this piece focuses on the overarching risks and rewards AI presents in teaching and learning.

Case-based learning allows students to develop higher-order critical thinking, problem-solving, synthesis, analysis, and communication skills by engaging with a realistic scenario in service of practicing course skills and concepts. Case studies are valuable tools for any class that combines asynchronous and synchronous learning. Indeed, some research (e.g., Webb, Gill, & Poe, 2005) suggests that a multimodal delivery model may be ideal for case study-based work, with the combination of synchronous and asynchronous elements enabling students to participate more fully in cases. In the first half of this piece, we outline some key considerations for using case studies in a multimodal course. In the second half, we make targeted recommendations for effectively prepping, facilitating, and reflecting on your multimodal case studies.

The majority of an online instructor’s course facilitation will occur within the learning management system (LMS). However, there are some tasks that can be improved or streamlined by downloading comma-separated value (CSV) files from Canvas. Running an online course requires balancing a lot of names, assignments, and deadlines, so using Microsoft Excel, Google Sheets, or another spreadsheet software can assist with course management.

With the help of tools like Canvas New Analytics, faculty can leverage learning management system (LMS) data to hone their instructional techniques and improve their online students' experience. In this piece, we provide an introduction to learning analytics in online higher education and detail some analytics best practices.

At the end of 2019, Canvas rolled out New Analytics, a new version of their former analytics tool, Course Analytics. By Canvas' own description, New Analytics retains the core functionality of Course Analytics while offering a simplified user experience. In this post we share our recommendations for leveraging New Analytics to support students.