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Game-Based Learning Experiences
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
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.
Branching Scenario Best Practices Guide
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.
Artificial Intelligence and Online Learning
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.
Increase Engagement Now
Whether you’re halfway through the term or just getting started, this post presents actionable, simple strategies you can implement immediately to increase engagement in your course.
Implementing Social Media
Many students use social media platforms in their daily lives, and “emerging evidence indicates that students express positive attitudes toward using social media for learning in general” (Baisley-Nodine, Ritzhaupt & Antonenko, 2018). However, there are also many concerns connected with using social media in an educational setting. These include issues related to a lack of familiarity with the platform, the potential for distraction, and privacy concerns. Therefore, it is important to carefully plan the use of social media in a course to address any potential issues or concerns.
Enhancing Quantitative Courses With Varied Learning Approaches
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.
Universal Design for 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).
Representation in Course Images
How many times have you looked at an image and thought, “Have I seen this before?” Chances are, if you are browsing a stock photo site, it’s often. That feeling of déjà vu occurs because images reflect an amalgam of artistic, cultural, and ideological influences (Hall, 2015).
High-Impact Practices to Support Diversity, Equity, Inclusion, and Belonging in STEM
When you think of a scientist, who comes to mind? If it’s Albert Einstein or Charles Darwin, you’re not alone. Gender stereotypes and a lack of inclusive role models in science, technology, engineering, and math (STEM) have contributed to spaces that have not always been welcoming for African American, Indigenous, and Latino students or those from other historically underserved groups (American Association of University Women, n.d.). Kimberlé Crenshaw’s concept of intersectionality, a term she coined in 1989, provides a framework for understanding Black women’s lived and overlapping experiences of racism and sexism (Center for Excellence in Teaching and Learning, n.d.; TED, 2016). Crenshaw, a law professor and Black feminist scholar, explains that “intersectionality is a lens through which you can see where power comes and collides, where it interlocks and intersects” (Columbia Law School, 2017).