Research has demonstrated numerous advantages to designing assessments in higher education as transparent learning processes. The rise of generative AI has proved to be an additional incentive to rethink product-oriented models of formative and summative assessment. This post provides general guidance on designing process-focussed assessments, but the learning outcomes will ultimately determine which design will best suit specific programmes and modules.
What is process-oriented assessment?
Process-oriented assessments aim to substantively value the development of learning throughout a module or programme. This is distinct from product-oriented assessments, which ostensibly only value the final result of learning (e.g. a report, essay, presentation, dissertation).
Why process-oriented assessment?
The benefits of designing assessments as learning processes include:
- Spreading the workload for students: students’ engagement with coursework is structured so that it takes place over a period of time, rather than trying to fit assessments and exam revision for multiple modules into short timeframes
- Spreading the marking load for staff: if designed efficiently, process-based assessments result in less of a marking bottleneck at the end of modules
- Helping students to stay on track throughout a module: breaking the assessment down into smaller tasks or milestones discourages students from waiting until the last minute, which can result in panic, poorer performance, and the temptation to use generative AI tools inappropriately (or to opt for more traditional forms of academic misconduct); it also means that learning is more likely to be embedded and so better act as a foundation for further learning
- Encouraging students to monitor and reflect on their learning: developing their learning over time in a tangible way can give students insights into the development of their understanding over the course of a module (and programme), especially if reflection is built into the assessment
- Giving lecturers a better understanding of how students are doing: staff can monitor student progress to varying degrees throughout a module, whether through marking elements of the assessment and giving feedback or simply checking to make sure students have submitted every element on time; greater familiarity with students’ progress can also help suggest when academic misconduct may have taken place (for example, if the final assessment appears to be unrealistically distinct from previous work)
- Creating more opportunities for self and peer feedback: providing feedback to peers and critically evaluating one’s own work have both been shown to develop students’ assessment literacy and subject-specific learning (see Peer Feedback and Assessment and Self Feedback); process-based assessments provide space for both
- Reflecting ‘real-world’ scenarios: process-oriented assessments tend to align more closely with work that students will be doing after they graduate, whether in post-graduate study and post-doctoral research, or other graduate jobs
- Discouraging inappropriate use of generative AI: most of the benefits listed above provide either positive motivation for students to fully engage with assessments (spread of workload, staying on track, perceiving their own learning progress, receiving additional feedback) or deterrents from taking shortcuts (requirement to submit regular progress indicators, early indicators of consequences of lack on engagement through regular feedback); see Assessment and marking in light of generative AI
Best practice tips
Consider the following as you design process-based assessments:
- Clear and close relationship among all assessment elements: ensure that students can easily understand how each task/step relates to the others and helps them to reach their goals
- Regular opportunities for expert, peer and self-feedback: build in feedback opportunities that use everyone’s time efficiently (e.g. in-class peer feedback; whole-cohort feedback on a sample of work; develop students’ skills in self-feedback)
- Work is spread evenly/appropriately across the module: take the whole module into consideration when determining the timing of assessment tasks
- Your expectations are clear: make sure that you clearly communicate what you expect of students, e.g.:
- when each element is due and how it should be submitted
- how polished each element should be
- how the elements are connected
- what constitutes academic integrity for this work
- marking criteria
- What students should expect is clear: carefully manage students’ expectations of you and of each other, e.g.:
- how the feedback processes work
- what kind of feedback will be provided and when
- how the summative mark is calculated (especially for group work)
General examples
This PowerPoint presentation provides several examples of process-based assessment designs.
Case studies
The following are examples of process-based assessments that have been implemented successfully at Durham University:
- Case study: Applying theory into practice using a strategy simulation in Business
- Case study: Authentic programming assessments
- Case study: Drafting policy on contemporary social issues
- Case study: Enhancing collaborative learning through micro-task strategies
- Case study: Peer review of formative blog posts
- Case study: Using portfolios to develop integrated skills
References and resources
Beckers, J., Dolmans, D. & van Merriënboer, J. (2016) e-Portfolios enhancing students’ self-directed learning : A systematic review of influencing factors, Australasian Journal Of Educational Technology, 32(2), pp. 32–46.
Calderon, K., Serrano, N., Blanco, C. & Gutierrez, I. (2024) Automated and continuous assessment implementation in a programming course, Computer Applications in Engineering Education, 32, e22681.
Carless, D. (2015) Excellence in University Assessment: Learning from award-winning practice. 1st edn. United Kingdom: Routledge.
Carless, D. et al. (eds.) (2017) Scaling up Assessment for Learning in Higher Education, Springer Singapore.
Guo, P. et al. (2020) A Review of Project-Based Learning in Higher Education: Student Outcomes and Measures. International Journal of Educational Research, vol. 102.
Hernández, R. (2012) Does continuous assessment in higher education support student learning?. Higher Education 64, 489–502.
Holmes, N. (2015) Student perceptions of their learning and engagement in response to the use of a continuous e-assessment in an undergraduate module, Assessment & Evaluation in Higher Education, 40:1, 1-14.
Kokotsaki, D., Menzies, V. & Wiggins, A. (2016) Project-based learning : a review of the literature, Improving schools, 19 (3). pp. 267-277.
Paloposki, T., Virtanen, V. & Clavert, M. (2024). From a final exam to continuous assessment on a large Bachelor level engineering course. European Journal of Engineering Education, 1–14.
Ramon-Muñoz, R. (2015) The Evaluation of Learning: A Case Study on Continuous Assessment and Academic Achievement, Procedia – Social and Behavioral Sciences, 196, 149-157.