Designing for Critical Thinking in Higher Education


This design case features an iterative design and implementation process that uses social constructivist teaching methodologies (Woo & Reeves, 2007; Ruey, 2010) to support challenge-based learning (Pérez-Sánchez et al., 2020) in an on-campus environment. At a Western Canadian Polytechnic University, the Business Analysis and Decision-Making course (3100) is designed to prepare third-year business students to apply prerequisite analytical tools to solve real-world, ill-defined organizational problems. From its infancy in 2009, the course has undergone several revisions to address poor DFW ratings (grades with D, F, and Withdrawals) and student engagement issues. This design case offers insight for instructional designers looking to instantiate ambiguity into on-ground learning environments that support the development of critical thinking, problem-solving, and innovation in higher education business students.


All Bachelor of Business Administration students in the University’s School of Business are required to successfully complete 3100, thereby acting as a gateway for students to enter fourth-year studies. During first- and second-year studies, students acquire prerequisite knowledge, including a general understanding of analytical tools and principles, through the completion of several prerequisite courses ranging from Microeconomics to Marketing. Students then utilize their acquired knowledge and analytical tools to demonstrate the 3100 course learning objectives in third-year studies.

3100’s course design is underpinned by the backward design model (BDM), a logic that uses the desired results to inform curriculum design (Wiggins & McTighe, 1998). Crocker (2020) depicts BDM as a useful strategy for determining links between content and essential learning questions or challenges and views it as an essential step to good course design. In consideration of the learning objectives, content categories were established to include critical thinking, decision making, application of analytical tools, and learning with cases. The decision to utilize challenge-based learning through a case-based learning approach (Angelo & Boehrer, 2002; Boehrer & Linsky, 1990) can be summarized by Perez et al.’s (2020) findings on the effectiveness of the methodology itself: “Providing relevant and practical solutions [through case study assessment] for authentic organizational challenges increase internal motivational levels in management students allowing for the increase of deep learning and internalization of knowledge” (p.6).

Design Process

The 3100 design process closely resembles that of Merrill’s first principles of instructional design: activation, demonstration, application, and integration (Merrill, 2020). The activation phase of the course features a review section that serves to re-orientate students with prerequisite knowledge and analytical tools. Etmer and Newby (2013) emphasize the value of mastering early steps prior to progressing to more “complex levels of performance” (p.49), and that the forgetting of newly acquired knowledge is largely a factor of “non-use” (p.48), a common trend among 3100 students. Through formative assessment, including quizzes, assignments, and access to key resources, the review section acts as a retrieval cue for students to classify the various analytical tools available to them.

The demonstration phase incorporates a non-weighted case study where students can learn and understand exactly how to apply prerequisite knowledge and tools to ill-defined situations. The decision to include the demonstration phase is centered around offering students additional support in preparation for heavily weighted challenges (Caruana, 2012). Merrill (2020) explains that instruction is more effective when specific information is demonstrated in a practical manner and that learners “remember and can apply information far more readily” when demonstration occurs (p.48). Considering learners enter 3100 with no prior experience with case studies, the demonstration phase is essential to their success. During their practice case, students try to identify appropriate tools for the given scenario, attempt to apply them, and reflect and discuss their findings with classmates and the instructor in a collaborative environment.

The design of the application phase harnesses the social constructivist (SC) approach to teaching and learning, which in this case, combines collaboration with project-based learning activities (Woo & Reeves, 2007). Stage et al. (1998) state that many educators see SC as a foundation for the design of effective learning environments. Woo and Reeves (2007) explain that SC adds additional meaning to the learning process through communication with peers and experts (e.g., instructors) in the context of real-life tasks, which aligns with the case-based orientation of 3100. Four instances of weighted case studies are instantiated throughout the course. Students study both independently and in collaboration with classmates and instructors in weekly workshops to work through their cases. The combination of self-direction and social interaction encourages student engagement, skill development, motivation, and most importantly, project completion (Ruey, 2010; Woo & Reeves, 2007).

The final phase of the course design is integration, an aspect of experiential learning (Crocker, 2020) that combines formative assessment, student reflection, and case defense to enhance learning. Lewis and Williams (1994) credit the reflection of learning experiences for encouraging students’ development of new skills, attitudes, and new ways of thinking (p.5). Merrill (2020) explains how learners need a chance to showcase, defend, and reflect on what they have learned, “if it is to become part of their available repertoire” (p.51). Learners first reflect upon the review process, contemplating the relevance of each analytical tool and principle, and how to apply them to practical scenarios; they later defend their critical analysis of an individual-based weighted case study. Upon completion of the course, the design team evaluates the formative assessment data and all aspects regarding student engagement and performance to inform design decisions for future iterations of the course.


No formal instructional design model was used to develop 3100. In fact, much of the development relied upon trial and error, which arguably accounts for poor student performance and attrition rates in earlier versions of the course. However, when you evaluate the design process with a critical lens, there are similarities to the ADDIE model: analyze, design, develop, implement, and evaluate (Bates, 2015; Molenda, 2015). For example, assessment of the course objectives informs design, development, and implementation decisions, which align with ADDIE’s analysis phase; similarly, evaluation of student performance through formative and summative assessment analyses informs revision of future course iterations, which aligns with ADDIE’s evaluation phase. The phases in-between, namely design, development, and implementation, all occur out of necessity; therefore, ADDIE may, unknowingly at the time of conception, act as a foundation for the 3100 course design process.

Although the integration phase of the course provides students with the opportunity to reflect on learning, more can be done to ensure long-term impressions on the learner (Crocker, 2020). For example, utilizing a summative reflection assessment at the end of the course would afford students the opportunity to re-connect with the teachings, while encouraging them to make personal connections with the curriculum in relation to their everyday lives (Crocker, 2020).

While the social constructivist approach accommodates challenge-based learning, it does come with its own challenges. Most specifically, 3100 relies heavily on the interactive affordances of in-person workshops, but with higher education continually moving to online learning (Anderson, 2008), an effective online equivalent has to be realized. In fact, due to the COVID-19 pandemic of 2020, the course DFW ratings nearly doubled after two terms of running online. This challenge presents great implications for ID: how can we best create collaborative online learning environments that truly foster learner engagement and motivation, while accommodating the collaborative characteristics of on-campus workshops that students have come to enjoy? Perhaps the answer lays in the usage of ID models like ARCS-V (Keller, 1983) to innovate online student motivation strategies that promote sustained learner engagement. With online learning in mind, hybrid modeling solutions are likely at the forefront of the ID industry (Bates, 2015).




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