Richard McInnes, Ngoc Nhu (Ruby) Nguyen, Sasikala Rathnappulige, Simon Marek, Daniel J. Searson, Ashlee Waterland, Aaron Honson
Teaching and Learning Innovation, Adelaide University (Tirkangkaku), South Australia, Australia
In this post, we outline the importance of accurate student workload estimates, as well as the ways that student workload is estimated. We discuss our experience in creating a student workload calculator and using it to determine whether research-based workload calculations differ from commercially advertised workloads (spoiler alert: they do!), before exploring what this means for course authors.
The importance of getting estimates right
If you’ve ever browsed a catalogue of online micro-credentials, you may have seen an estimated ‘time to complete’ on the course listing. For most of us, this is a crucial piece of information to consider before enrolling. Potential students may wonder how much time and effort they need to invest if they enrol, or whether they can fit the study load in around their other responsibilities. This advertised workload can be crucial to a student’s success. An estimate that is too low may mean that once a course commences, students are overwhelmed by an unexpectedly high workload. This, in turn, may mean increased stress, difficulty maintaining study commitments alongside work and other responsibilities, and students skimming content rather than deeply engaging. Ultimately, students may simply drop out. On the other hand, an estimate that is too high may deter potential students unnecessarily.
So, we know that having accurate student workload estimates is very important. But how do we know if an estimate is accurate? Existing literature highlights three main approaches to estimating workload.
- The experiential method involves course designers coming up with estimates based on their own experience (a practical approach, but prone to bias).
- The proxy method involves course designers timing themselves completing the learning tasks and then multiplying the result by three or four (straightforward enough, but assumes a linear relationship between workload for professionals and students).
- The survey method relies on learners’ self-reporting data after completing learning tasks (a more direct measure, but again prone to several biases).
The student workload calculator
Another approach is student workload calculators. The idea behind a workload calculator is that learning activities are categorised and entered into the calculator, and an estimated course completion time is output based on pre-defined activity durations. This provides a standardised and defensible estimate, but it’s important to note that workload calculators are not a silver bullet, as they have their own strengths and weaknesses. They may oversimplify the multifaceted nature of education, and they may not adequately reflect the specific demands of different styles of courses. However, these factors aside, the benefits of workload calculators appear strong: They provide consistency across estimates, reduce bias, and provide a research-based metric.
In our recent paper (https://doi.org/10.14742/ajet.10583), we brought together disparate literature and tools for workload estimation into a single coherent and usable framework for practitioners. We aimed to create something research-informed, yet practical. Since the publication of the paper, we have developed the calculator into a tool that you can use to help reflect on the workload in your online courses.
Student Workload Calculator – https://mediaproduction.adelaide.edu.au/au-interactives/#/workload-calculator
How should we think about calculators?
While the workload calculator gave us a structured approach to estimating study time, it is essential to recognise that it was not an objective instrument, but rather an interpretive tool. Any workload calculator is built upon underlying assumptions about student engagement, prior knowledge, and cognitive effort, all of which are inherently variable. So, rather than serving as definitive measures, these tools should be seen as prompts for critical reflection. When discrepancies arise, they should prompt educators to interrogate their assumptions about learning tasks: Are certain activities taking longer than expected? Are self-directed elements of learning being adequately accounted for? How do different learners experience the same workload? By using workload calculators as reflective instruments rather than rigid formulas, you can refine course design to better support student learning and success.
This work is based on: McInnes, R., Nguyen, N. N. (Ruby), Rathnappulige, S., Marek, S., Searson, D. J., & Waterland, A. (2026). Beyond guesstimating: Calculating student workload in fully online micro-credentials. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.10583
If you have any questions about the research or notice any glitches with the student workload calculator, please contact Richard McInnes