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Today’s students are diverse and include marginalized groups that have historically been excluded from mainstream education (Ladson-Billings, 2013). In 2021, students of color comprised upwards of 40% of the 15.4 million undergraduates enrolled in U.S. colleges and universities (Nam, 2023; National Center for Education Statistics, 2023). Gloria Ladson-Billings, whose work centers on culturally relevant pedagogy, argues that diverse students require inclusive learning to succeed. “[These students] do not fit neatly into the rigid categories of race, class, gender, or national origin” upon which hierarchies of the past have been built (Ladson-Billings, 2013, p. 5), so authentic representation of diversity in higher education is critical. Adrienne Keene, an assistant professor of American Studies at Brown University, writes that instructors can do their part to support underrepresented students by being honest about their own bias and blind spots, critiquing their course materials, and integrating meaningful representations of diversity into the curriculum (Fuchs et al., 2020; Keene, 2015).

Backward design is, as the name suggests, a process for designing curricula, courses, and lectures by working backwards from big-picture learning goals. The concept, introduced by Grant Wiggins and Jay McTighe (2005), suggests that instructors create assessments, activities, and course content that are explicitly aligned with the broader learning goals of the unit. This is different from the traditional content-driven approach to learning design, which focuses on course content first and only secondarily tries to align that content with learning goals.

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.

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.

Halfway through the term isn't a great time to change around a bunch of materials or assignments in your course. However, it is a useful moment to evaluate how the course is going, realign to match the goals you set out at the beginning of the term, and determine what you may be able to tweak to make your course work more effectively for you and for your students. This piece suggests actions you can take at midterm to help shape the second half of the course.

Whether designing a fully asynchronous course or a hybrid/blended learning experience, you’re likely thinking about recording a video to explain an idea, tell a story, or demonstrate a skill. And, in many situations, you should (see the Envision piece Video Planning: To Record or Not to Record? if you are wondering whether video is appropriate for your unique situation). Most research shows that online students enjoy learning from video and that it can be an effective way to deliver content. Students often appreciate the opportunity to review material at their own pace and to pause, rewind, and rewatch content as needed (Boateng et. al. 2016; Brame 2016).

For many people, “online learning” conjures images of successive videos, shot in a studio or perhaps on location, featuring high production value and expert, polished speakers, interspersed with short quizzes or activities. Indeed, strongholds such as MasterClass, LinkedIn Learning, and Coursera have mastered this formula and led many to believe that it is the recipe for success. While the economic success of these companies is undeniable, the effectiveness of an online course is measured by student success. When done well, video can have a positive impact on student learning (Clark & Mayer, 2011). Rather than designing your course according to the economic model, follow the evidence and research-based principles of visual communication, cognitive science, and online learning to decide when and how to create instructional videos. Through careful consideration of your learners, your objectives, your constraints, and the following best practices, you can spend your time and resources creating only the most essential videos. For a visual aid that accompanies this article, see the Video Planning Decision Tree.

Course announcements are an excellent way to communicate important information regarding upcoming topics and assignments. When delivered in video format, course announcements can also help create a culture of connection with your students, establishing instructor presence within the course.