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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.
Backward Design
Backward design is, as the name suggests, a process for designing curricula, courses, and lectures by working backward 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.
Creating Learning Objectives
Learning objectives help inform students about what they will learn and how they will be assessed. Objectives are meant to align with course expectations. Therefore, any assigned exercises should be guided by the course’s specific learning objectives. Everything in the course should work together to ensure students achieve the course objectives.
Two-Stage Extensions: When a Canvas Quiz Has Limited Attempts and an Availability Date
When a Canvas quiz has a limited number of attempts and an availability date, there are two sets of actions instructors usually need to take to provide a student with an additional attempt or extension on the quiz. First, the instructor will need to add a new quiz attempt for the student. Second, if the availability date has passed or is about to pass, they will need to extend the availability of the quiz. This short guide will walk you through both stages of the process.
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.
Offering Extensions in Canvas
Due dates are a useful pedagogical tool. They help students keep pace to complete the course, populate the To-Do List and Calendar with reminders for both instructors and students, and allow Canvas to work more predictably and efficiently, among other benefits. However, there inevitably come times when a student needs a different time frame than the standard allotment to complete work. This guide will help walk through the considerations needed to extend the due date on an assignment. (Note: Extending the due date of an assignment, discussion, or quiz is different than adding additional attempts at the work. For more information on adding attempts, see the Envision piece Two-Stage Extensions: When a Canvas Quiz Has Limited Attempts and an Availability Date.)
Canvas Grading and Feedback: What Students See
Did you know that some forms of assignment feedback in Canvas are more obvious to students than others? Canvas has a Student View option for instructors to get a sense of what students are seeing in most general areas of their courses, but it can be challenging to determine what your actual students are experiencing when accessing your comments on their work or the rubric you’ve filled out for their submission.
Integrating AI Into Assessments: From Policy to Practice
Conventional assessments, such as essays and multiple-choice questions, have long been the cornerstone of evaluating student performance. However, the widespread availability of generative AI (genAI) tools necessitates rethinking assessment methods. Now that genAI tools are readily accessible and rapidly improving, it is crucial to develop assessment approaches that maintain academic integrity while leveraging the benefits of AI to engage students and prepare them for the modern workforce (Yu, 2023).