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Five Ways to Combat Linguistic Bias in the Classroom
Developments such as the evolution of World Englishes (WE) and African American scholars’ use of African American Vernacular English (AAVE) have opened an important dialogue around academic writing standards, language ownership, and linguistic justice (Canagarajah, 2006; Young, 2010). Authors like Gloria Anzaldua who mix, for example, Native Indian, Spanish, and English in texts, are engaging in the literary tradition of code meshing, which has been shown to facilitate acquisition of English when used by multicultural students in the classroom, according to research (Canagarajah, 2006). By adopting inclusive practices, course designers can combat linguistic bias and promote writing achievement for all learners. This blog contains five recommendations for reducing linguistic bias in online education.
Using Hotspots
A unique way to share information, images with hotspots offer online learners the opportunity to interact with course content. Learners can click or hover on particular parts of an image and receive pop-ups giving them more information. Hotspots represent information in a particular context; thus, they fulfill the multimedia principle—use words and graphics rather than words alone—and the contiguity principle—align words to corresponding graphics (Clark & Mayer, 2016).
LMS Analytics: Supporting Your Students With Data
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.
Navigating Canvas New Analytics
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.
Improving PowerPoints
Sharing information via PowerPoint presentations is a long-established strategy in higher education. Designing PowerPoint presentations for online courses can pose unique challenges; however, best practices can help overcome these hurdles. With time and attention, faculty and instructional designers can create engaging and purposeful presentations with lasting value.
Self-Recording Best Practices
While traditional lectures are delivered in front of a classroom, allowing you to read students’ engagement and adjust in real time to both content and pacing, online lectures do not afford the same flexibility. Therefore, it is important to carefully plan your videos in accordance with best practices in online learning. There are many video types and formats to choose from (See the Envision blog: Matching Video Production Style to Learning Goals), and one decision you'll need to make is whether you want to appear on camera. This guide covers best practices for videos that will include your webcam footage.
Matching Video Production Style to Learning Goals
So, you’ve decided to record a video for your course (See first, the Envision post: Video Planning: To Record or Not to Record?). Your next concern might be technology related, as you wonder how you can match the production quality of videos you’ve seen in MOOCs (massive open online courses) such as MasterClass or Coursera. But have no fear—research shows no association between production value and learning outcomes (Hansch, et al., 2015; Sturman, Mitchell, & Mitchell, 2018). Furthermore, selecting your technology without initial consideration of your video style would be premature. “When thinking about video for learning, the choice of video production style will have a great impact on a video’s ability to effect pedagogical objectives and desired learning outcomes” (Hansch, et al., 2015, p.20). Production style refers to the holistic organization of a video, which we will discuss in terms of type (what the video aims to accomplish) and output (what the video looks like). Choosing the right style for the content centers the learner and ultimately facilitates an easier selection of technology.