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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.
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.
The Power of Retrieval Practice
Faculty aim to impart lasting knowledge and skills, but sometimes, learning doesn’t stick. One of the most powerful techniques for enhancing students’ long-term retention is retrieval practice, the process of actively recalling information to mind rather than passively reading or reviewing it. In this piece, we’ll dive into the evidence behind retrieval practice, provide strategies for how to incorporate it into online courses, suggest ways to frame its utility to students to ensure they fully reap the benefits of this learning strategy, and describe specific types of retrieval practice activities.
Best Practices for Screencast
Do you want to deliver presentations, share tutorials, or teach complex applications in your online course? If so, creating screencasts may be a great option for you. This piece defines what a screencast is, identifies important development considerations and common instructional use cases, and highlights best practices for creating screencasts for your online course.
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.
Universal Design for Learning
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).
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.