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Game-based learning (GBL) is a learning experience, or set of learning experiences, delivered through gameplay or game-like activities with defined learning outcomes. GBL is often confused with gamification, which is the application of game elements to a non-gaming experience. GBL engages students cognitively, emotionally, behaviorally, and socioculturally (Plass et al., 2015). Many factors should be considered when designing GBL, including narrative, player positioning, and interactive design (Dickey, 2005).

Providing student feedback is a key component of an instructor’s role and an important part of effective instruction. Research shows that ongoing feedback keeps students engaged and improves their morale, motivation, and learning (Best, et al, 2014). Yet, providing high quality feedback can be a time-consuming commitment, especially in courses with large class sizes or numerous written assessments. Instructors should keep in mind the tools, structure, and best practices that can help them provide feedback.

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.

While Open Educational Resources (OERs) can take on many forms, including assignments, videos, and articles, open textbooks have provided students with more affordable options for accessing and purchasing required course materials.

From time to time instructors may want to include in their courses copyrighted materials like images, print content, audio recordings, or videos. The University of Minnesota Libraries define copyright as “the area of law that deals with creation, ownership, sale, and use of creative and expressive works.”

Universal Design for Learning (UDL), which has roots in Ronald Mace’s concept of Universal Design, is a pedagogical framework that supports diverse learning needs. According to CAST, the creator of the framework, UDL seeks “to improve and optimize teaching and learning for all people based on scientific insights into how humans learn” (2018). UDL is not a step-by-step curriculum plan, but rather an approach to pedagogy and curriculum development that aims to make the learning environment as accessible as possible for as many learners as possible (Derer, 2021; CAST, 2018).

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.