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Formative Assessments
Formative assessments encompass a broad range of low-stakes activities aimed at improving student learning outcomes. In contrast to summative assessments, which are intended to measure products of student learning, formative assessments are oriented towards the learning process itself (Black & Wiliam, 1998). They can provide students with opportunities to evaluate their developing understanding of key concepts, practice new skills, and prepare for summative assessments (McLaughlin & Yan, 2017; Orange, Agak, Okelo, & Kiprotich, 2018). They can also provide instructors with valuable data on student progress (Bell & Cowie, 2001; McLaughlin & Yan, 2017). The results of formative assessments can indicate where individual students are struggling or excelling, allowing instructors to provide targeted feedback and tailor their instructional delivery accordingly.
Peer Review Best Practices Guide
Peer review is an active learning technique in which students evaluate peer assignment submissions and provide each other feedback. There are several benefits to using peer review in a course, including increased attention to detail and quality and engagement in constructive critique (Chong, Goff & Dej, 2012). Peer review may also help students develop effective problem-solving strategies (Wagner & Rutherford, 2019). Peer reviews can impart cognitive benefits for both students who conduct reviews and students who receive peer feedback (Knight & Steinbach, 2011). When implemented effectively, the peer review process equips students with valuable feedback and promotes classroom community.
Written Assignment Best Practices Guide
Formal writing requires sustained focus on content and close attention to detail. For these reasons, written assignments can be an effective assessment tool in graduate courses when they are thoughtfully and purposefully designed. This guide provides recommendations for faculty who are looking to harness the pedagogical benefits of written assignments.
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.
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.
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.