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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.
Teaching Styles Series: Introduction
This is the first in a series of pieces examining online instructor teaching styles. In this introductory piece, we will characterize teaching styles, summarize Anthony Grasha’s typology of teaching styles, and discuss how identifying your teaching style (or styles) can inform online course design and instruction. In subsequent pieces, we will explore each of Grasha’s teaching styles in depth.
Problem-Based Learning
Problem Based Learning is a teaching method used to facilitate student knowledge acquisition. This teaching method is often confused with Project Based Learning, which centers on students applying knowledge. The focus of Problem Based Learning is students acquiring the knowledge. Since the two methods use the same acronym, they are easily confused, but have different objectives for students.
Project-Based Learning
Project-based learning is learning that is organized around a project (Bell, 2010). It is a student-centered approach to learning, where students choose their topic of study and design an integrative project around the topic (Bell, 2010; Astawa et al., 2017). This form of study promotes self-efficacy in the learning environment. Such self-efficacy enables students to perform more difficult tasks and develop confidence in their abilities (Shin, 2018). These abilities generally help students to transfer their skills to the real world.
Beyond Traditional Grades: Alternative Assessment Methods
As educators navigate the challenges and opportunities presented by generative AI (genAI), many are reconsidering traditional assessment approaches. Alternative assessment methods offer innovative ways to evaluate student learning that go beyond conventional grading systems, focusing on authentic learning, skill development, and meaningful engagement. These approaches not only address the challenges posed by AI but also align with research on effective learning and motivation (Furze, 2023; Pitts Donahoe, 2023).