More Than a Name: How Assignment Labels Influence Student Learning and Performance – Faculty Focus

In higher education, assignment titles often serve a functional purpose, indicating the sequence of assignments rather than their specific objectives. However, intentionally structuring assignment titles and formats can significantly influence student engagement and learning outcomes, especially in online courses. Applying adult learning theory, we redesigned assignments to emphasize process-based learning and offer scaffolded opportunities for students to engage with authentic tasks. This approach clarified expectations, enhanced performance, and fostered a more collaborative relationship between faculty and instructional designers. 

We grounded our redesign in adult learning theory (andragogy) because our student population skews older, with an average age of 25, and many are returning learners balancing coursework with careers and family responsibilities. Adult learning theory emphasizes that adults learn best when they understand why they are learning something, see clear relevance to real-world contexts, and are given opportunities to reflect and apply their knowledge authentically. With these principles in mind, we aligned assignments with professional scenarios, built feedback opportunities into the workflow, and broke tasks into logical phases to support reflection and critical thinking. 

Building on this foundation, we reconsidered how assignments were labeled and structured. Traditionally, assignments were labeled “Homework 1” or “Homework 2.” These labels communicated sequence but not purpose. To foster student understanding and engagement in a Prescriptive Analytics course that focuses on model building and model analysis, we restructured assignments around two key phases—Model Setup and Model Insights—and renamed them using a consistent structure—Model Setup and Model Insights—followed by a number (e.g., Model Setup 3, Model Insights 3). This format clarified each assignment’s stage in the modeling process without overwhelming students with overly complex titles. 

Figure 1. The revised prescriptive analytics modeling format and goals emphasize the modeling process and are split into two submissions with interim instructor feedback. 

To enhance student learning, we divided the homework assignments into two submissions with an interim instructor assessment. For instance, in one assignment where students had to match project managers to various projects, they first reviewed fictional emails presenting the problem, constraints, and necessary data. After completing their project’s initial setup, students received individualized instructor feedback that encouraged them to think critically about how they approached the problem and the assumptions they made before moving on to analyze their results. This opportunity for early intervention provided support, reduced confusion, and built students’ confidence to better prepare them for creating and running their spreadsheet models. Overall, this two-step approach fostered deeper thinking and engagement with the instructional material and interpreted the outcomes through additional questions and presentations. 

To support student learning and prepare them for the graded modeling assignments, we introduced each module with a sequence of low-stakes, formative quizzes built around simplified case-based scenarios using business-style communication such as mock emails and voicemails. These activities helped students build familiarity with core modeling concepts and decision-making logic before tackling more complex tasks.  

Graded modeling assignments were framed using business-style communications such as mock emails and memos to reinforce real-world relevance. For example, in Model 4, students received a series of emails outlining a realistic assignment problem, including objectives, constraints, and relevant data. Students submitted their Model 4 Setup individually and received instructor feedback before advancing to the Model 4 Insights phase, where they used Excel to build an optimization model, interpret the results, and respond to scenario-specific questions in a timed quiz. A short video presentation accompanied their submissions to explain their recommendations and rationale. Excel was selected as the modeling platform because it is freely accessible to our students and includes Solver functionality. This two-phase assignment format, supported by structured templates and clearly defined expectations, offered early guidance, encouraged iterative learning, and promoted deeper engagement with course concepts while supporting individual accountability. 

The results were clear when comparing course participation before and after implementing the new approach. Submission rates for all modeling assignments increased by 18 percentage points. In addition to higher completion rates, students demonstrated measurable improvements in problem formulation, critical thinking, and communication skills. These changes aligned closely with the course learning outcomes and were reinforced through targeted quiz prompts and structured instructor feedback. 

Most notably, engagement in the “new approach course” extended beyond basic requirements. While at least one sensitivity analysis was required, nearly half of the students voluntarily completed an additional, optional analysis. Given how difficult it can be to motivate students to complete even required coursework, this level of voluntary participation highlights the power of purposeful assignment design to drive deeper student investment. 

Based on this redesign experience, we recommend several strategies for instructors and instructional designers aiming to create more engaging, authentic assignments. These include using purposeful titles, scaffolding complex tasks, framing assignments in real-world contexts, and integrating tools that support communication and feedback. 

Table 1 provides an overview of these strategies and design recommendations: 

By renaming assignments, scaffolding tasks, incorporating real-world scenarios, and integrating student-centered feedback mechanisms, we helped students move from performative task completion to meaningful, process-oriented learning. Our collaboration demonstrated that intentional course design, grounded in adult learning principles, can make assignments more than a grade. It can make them matter. 

Most importantly, these strategies are highly transferable. While our work was rooted in a prescriptive analytics course, the principles of clear purpose, scaffolded structure, and authentic engagement can be applied across disciplines and modalities. Faculty and instructional designers can adapt these approaches for various disciplines in the humanities, sciences, or professional programs to support meaningful learning. 

This redesign also reinforces the value of partnership between instructors and instructional designers. Through open collaboration, we combined disciplinary expertise with design thinking to create a learning experience that was more intentional, more engaging, and ultimately, more effective. 

Salina Randall, MEd, is the Associate Director of the Center for Teaching, Learning, and Technology at the University of West Florida. She leads initiatives focused on instructional design, faculty development, and digital accessibility. With over a decade of experience in higher education, she supports faculty in designing inclusive, engaging, and data-informed learning experiences. Her work spans course design, accessibility compliance, and strategic communication, with a particular emphasis on empowering faculty through collaboration and practical solutions. 

Dr. Julie Ann Stuart Williams is a Professor of Business Administration at the University of West Florida. Her research spans operations management and business education, with recent work focusing on improving business student writing and modeling-based learning. She has co-authored papers with students in journals such as the European Journal of Operational Research and Business and Professional Communication Quarterly. A recipient of UWF’s Faculty Excellence in Teaching Award, Dr. Williams also serves on the editorial board of INFORMS Transactions on Education and as 2nd Vice Chair of the INFORMS Committee on Teaching and Learning. She holds a Ph.D. in Industrial and Systems Engineering from Georgia Tech.