Abstract:

With the advent of political pressure on universities to construct course and program learning outcomes in the 1990s, university instruction began to take a slow, arduous, even tentative turn from a content to a performance-centric approach to instruction (See Barr & Tagg, 1995). Wiggins & McTighe (2005) in their book Understanding by Design, proposed an approach where instructors design learning experiences with the end in mind; the end being higher order performance and capacity in students. This paper describes a framework for instructors and designers that partakes of a teaching method described by Eliason et al. (2018) as Authentic Purposeful Backward Design. This method, in turn, partakes of the principles laid out by Wiggins & Tighe but extends those principles in a practical way. One aspect of this approach highlighted in this paper is the creation of fully worked out practice problems that increase in complexity and scope in a way that prepares students to demonstrate complex and meaningful capacity by the end of their university course. When students have access to a large number of highly worked out problems as they engage in practice, they are able to access immediate feedback that would otherwise be impractical for instructors to provide for more than one to two dozen students on their own. A large number of fully worked out problems help student abstract patterns within the problem space that make it possible for them to transfer their knowledge to a large array of never-before-seen problems (See Chi et al., 1985).

The obstacle to this approach is the amount of content development required with limited faculty time resources to create enough simple to complex problems that make a difference in student learning. The use of generative artificial intelligence in the last year shows some promise in breaking down the time/resource barrier as just described (See Qadir, 2023). In addition to the aforementioned emphasis, this paper will demonstrate the role of using generative artificial intelligence in creating a corpus of simple to complex practice problems that extend across the length of a university course culminating in an experience where students showcase their high-level abilities in meaningful ways.

References:
[1] Barr, R. B., & Tagg, J. (1995). From teaching to learning—A new paradigm for undergraduate education. Change: The magazine of higher learning, 27(6), 12-26.
[2] Chi, M. T., & Glaser, R. (1985). Problem-Solving Ability.
[3] Eliason, S., Plummer, K., & Swan R. (2018). The Art and Discipline of Educational Consulting: Overview, Key Elements of Training, and prospects for the Future of the Field. Forum for the Center for Teaching and Learning, Teikyo University, Tokyo, Japan, Volume 5, 53-69.
[4] Qadir, J. (2023, May). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-9). IEEE.
[5] Wiggins, G. P., & McTighe, J. (2005). Understanding by design. Ascd.

Keywords:

Generative AI, backward design, performance assessment, critical thinking.