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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).

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.

Designed to simulate real-world experiences, branching scenarios are powerful tools for increasing student engagement. Like a choose-your-own-adventure book, a branching scenario invites users to explore a virtual world, using knowledge and skills from their coursework and information shared within the scenario to make decisions. The decisions they make lead them down different pathways (some of which may include embedded documents and videos) towards different endings. Depending on the complexity of the branching scenarios and the choices students make, they can experience a variety of different possible outcomes within a single scenario.

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.

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).

Employing a variety of modes of instruction and assessment, as recommended by Universal Design for Learning (UDL) principles, can enhance the learning experience for students in quantitative courses. Diverse elements such as visual aids, interactive features, and real-world applications can complement, extend, or replace traditional lectures and exams. Since classes consist of students with varying learning preferences and strategies, using multiple modes of representation in a course promotes deeper understanding, engagement, and skill development. This piece details design elements that can be particularly impactful in quantitative courses.

Office hours, blocks of time designated for faculty and student interaction outside of any regularly scheduled class sessions, are routinely incorporated into university courses (Briody et al., 2019; Hsu et al., 2022). Such sessions are often semi-structured and optional for students, allowing faculty to provide customized support to individual learners when needs arise. This form of faculty-student interaction can support academic achievement, retention, and engagement (Griffin et al., 2014; Guzzardo et al., 2021). That office hours attendance is often at the discretion of individual students, however, can result in underutilization of this valuable supplement to required course sessions and contents (Briody et al., 2019; Griffin et al., 2014; Smith et al., 2017). The purpose of this blog is to delineate empirically guided strategies for optimizing the inclusion of office hours in university courses. In particular, we focus on office hours hosted online, as the online modality can be advantageously employed not only for courses delivered online but also for those delivered residentially.

Multimodal courses allow for exciting opportunities in course content and activities but can be, by design, less flexible than asynchronous courses and less predictable than synchronous courses. These opportunities thus come with needs for additional logistical support and flexibility, as students need both to be able to take advantage of the opportunities of synchrony and asynchrony equitably. How can you best support students in a multimodal course, providing guidance through multiple forms of interaction? This piece gives insight into what kinds of support benefit students in multimodal courses and how to provide them. We’ll end with five quick tips for supporting students that apply to almost any multimodal course.

Faculty often express concern over how to maintain personal relationships with their students in an online course space; incorporating optional synchronous elements to an online course can help “put a face” to a name. Zoom, the video conferencing tool that allows you to create synchronous experiences for their students, has become ubiquitous in educational and businesses in the past two years.

Designing a successful multimodal course means, at each step of the process, considering what each format does well—structuring the course such that each piece of content, each activity, each interaction uses the most effective delivery method available. But what does that look like in practice? This piece describes three approaches to structuring a multimodal course. In each model, asynchronous and synchronous time complement one another and further module and course objectives. Where the models differ is in the relative importance of asynchronous activities in enabling students to complete synchronous activities and vice versa.