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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).

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.

Case-based learning allows students to develop higher-order critical thinking, problem-solving, synthesis, analysis, and communication skills by engaging with a realistic scenario in service of practicing course skills and concepts. Case studies are valuable tools for any class that combines asynchronous and synchronous learning. Indeed, some research (e.g., Webb, Gill, & Poe, 2005) suggests that a multimodal delivery model may be ideal for case study-based work, with the combination of synchronous and asynchronous elements enabling students to participate more fully in cases. In the first half of this piece, we outline some key considerations for using case studies in a multimodal course. In the second half, we make targeted recommendations for effectively prepping, facilitating, and reflecting on your multimodal case studies.

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.

While student populations have become increasingly diverse, many groups, including first-generation, non-native English speakers, and individuals with disabilities, still face barriers and bias that can derail their success in college (Super et al., 2020). Traditional grading practices—including penalties for late work, writing in dialects other than standard English, and even plagiarism— are prone to bias and only perpetuate disparities, the research says (Feldman, 2019; Savini, 2021).