Search

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.

From trivia games to final exams, quizzing tools have a variety of uses for learning as well as assessment. Exams and quizzes have a particularly plentiful range of possibilities in a multimodal or hybrid course, where they can be administered synchronously or asynchronously. Research suggests that the presentation of a tool influences student behavior in response to the tool. In comparing two student discussion boards, one an ungraded discussion and one a graded replacement for a final exam, Cheng et al. (2013) found that students displayed more knowledge on the graded board, but more evidence of learning on the ungraded board. The students who participated in the study were more likely to grapple with new ideas when the stakes were low, but more eager to showcase topics they were confident about when their responses would have a greater impact on their grades. When considering quizzing tools, then, we recommend allowing your course goals to guide your usage.

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 studies are an effective and powerful pedagogical tool. They present realistic narratives to students and require them to analyze possible outcomes or solve a dilemma or challenge associated with the narrative, and they are often followed by a series of questions or prompts that ask students to demonstrate their learning. Case studies can be based on real-world situations or fictional scenarios modeled on authentic occurrences. Regardless of the source and format, case studies provide students an opportunity to practice solving problems that they might encounter in the future.

Learning objectives help inform students about what they will learn and how they will be assessed. Objectives are meant to align with course expectations. Therefore, any assigned exercises should be guided by the course’s specific learning objectives. Everything in the course should work together to ensure students master the course objectives.