What if we could redesign assessment from the ground up?
I’m writing as faculty and department coordinator in North Island College’s (NIC) Digital Design + Development program area. It’s 2035. Following a post-secondary funding crisis, NIC has reduced its physical footprint. One campus remains, while distance learning and mobile units serve dispersed communities. Most courses and grades have been replaced by competency badges (Robert et al., 2026, p.15) earned through collaborative, community-based projects.
Emma is a part-time student and mother of three living in a remote Vancouver Island community. She serves as the Knowledge Synthesizer on an app project helping kelp farms forecast changes in ocean chemistry.
“Emma, new data in your team’s workspace appears to contradict a field observation contributed last week.”
Emma reviews both artifacts and discusses the discrepancy with her teammates. Together, they identify a flawed assumption, and Emma revises their plan. The AI tentatively awards her a Critical Analysis badge.
Emma questions the attribution. The insight emerged through collaboration, yet the badge appears only on her profile because she recorded the revision. She’ll discuss this with her instructor during the badge review.
How can faculty distinguish individual learning from collective achievement? Can we make learning visible without confusing measurable activity with learning itself?
Scenario 3 in Macgilchrist et al. (2020) provides a foundation for imagining a future in which post-secondary education is reorganized around competencies understood as “a collective, embedded, embodied and political, rather than individual, practice” (p. 83). Knowledge Building (Scardamalia & Bereiter, 1994) emphasizes the collaborative development of shared ideas and solutions. Yet, as Strijbos (2011) notes, assessing individual learning within online collaborative work is challenging. Black Box Assessment (Winstone et al., 2026) advocates for assessing students’ iterative learning journeys rather than just their final products, an approach that learning analytics could support. However, Knight et al. (2014) caution that analytics without a theory of learning measure data, not learning. Meanwhile, Selwyn (2024) questions AI’s ability to interpret learning within complex social contexts.
Together, these perspectives highlight the pedagogical promises of online collaborative projects, but this future will only be optimistic if analytics inform faculty judgment rather than replace it.
Questions for classmates:
- How do you distinguish individual contributions in group assessment? Does your approach work, and what research would you recommend?
- Should individual learning be assessed separately from collective achievement at all?
- Where do you think my proposed essay is the weakest?
References
- Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2). https://doi.org/10.18608/jla.2014.12.3
- Macgilchrist, F., Allert, H., & Bruch, A. (2020). Students and society in the 2020s. Three future ‘histories’ of education and technology. Learning, Media and Technology, 45(1), 76–89. https://doi.org/10.1080/17439884.2019.1656235
- Robert, J., Muscanell, N., McCormack, M., & Arnold, K. (2026). 2026 EDUCAUSE Horizon Report, Teaching and Learning Edition [Horizon report]. EDUCAUSE. https://library.educause.edu/resources/2026/5/2026-educause-horizon-report-teaching-and-learning-edition
- Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265–283. https://doi.org/10.1207/s15327809jls0303_3
- Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk Tidsskrift for Pedagogikk Og Kritikk, 10(1). https://doi.org/10.23865/ntpk.v10.6062
- Strijbos, J.-W. (2011). Assessment of (computer-supported) collaborative learning. IEEE Transactions on Learning Technologies, 4(1), 59–73. https://doi.org/10.1109/tlt.2010.37
- Winstone, N. E., Gravett, K., & Elkington, S. (2026). Black Box Assessment: rethinking integrity and learning for a time of Generative AI. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2026.2661365
Hi Claire,
I felt like you were able to paint a fairly clear picture in this blog post. It is obvious that you have done some deep research and put a lot of thought into this so far. I will attempt to share some answers to your questions.
1. How do you distinguish individual contributions in group assessment? Does your approach work, and what research would you recommend?
As a teacher, I’ve found that a combination of a group product and an individual reflection works well. The final project shows what the group accomplished together, while the reflection helps me see each student’s thinking, contributions, and learning. One thing I’ve taken away from this program is that learning is often social, but understanding is still personal, so I think assessment needs space for both.
2. Should individual learning be assessed separately from collective achievement at all?
I think both matter. Some of the most meaningful learning I’ve experienced in this master’s program has come through discussions, group work, and hearing other perspectives. At the same time, individual assessment gives learners an opportunity to demonstrate their own growth and understanding. For me, it’s less about choosing one or the other and more about finding a balance between them.
3. Where do you think my proposed essay is the weakest?
I’m not sure that I have a good answer for this question. Overall, your post made me reflect on how often we prioritize measuring individual performance when many of the most meaningful learning experiences occur through collaboration. It will be interesting to see where your thinking takes you as you develop the essay further.
Hi Jenn,
thanks so much for reading my post and leaving feedback. Yes, I’ve followed a similar process in the past and I’ve also asked students to review each other as well. In Team-Based Learning we ask students to evaluate their teammates which is meant to keep them accountable to the group. I have found though that some groups enter a “sneaky” pact where they all agree to give each other perfect reviews, which completely undermines the process.
I like this sentence that your wrote “One thing I’ve taken away from this program is that learning is often social, but understanding is still personal, so I think assessment needs space for both.” It sums up how I feel too and answer the second question. If understanding and skills development is individual, then we should aim to separate individual learning from group achievement.
Thanks again. I look forward to reading your post.
Claire
Hi Claire,
I have found that the fictional narrative segments and case studies in the readings for Unit 3 have been extremely compelling. Your contextualization of Emma adds greatly to your message in this blog post. Will you be weaving Emma into your essay somehow? A question that has arisen for me as I’ve begun planning for this assignment is how to incorporate the case studies or fictional narratives from Unit 3 into our final assignment. This is something I am still mulling over because I find them to be gripping and powerful, but as they aren’t ‘factual’, I’m finding them challenging to make use of.
Jenna
Hi Jenna,
This is an interesting assignment, I’ve also found the speculative narratives fascinating to read. I would like to expand on Emma’s story in my assignment (I have a lot to say about her).
Yes, adding a human face to a problem helps people empathize. It’s a technique we use a lot in UX research. I’m glad you enjoyed reading about Emma. I initially thought that, in assignment 3, I might write a short story and follow it with a critique but now I’m leaning more towards interspersing bits of Emma’s story throughout my essay, wherever they serve best.
I also found it hard reconcile how to critique the speculative narratives within the context of assignment 3. I’m planning to use a couple of them as context for my story. As you said, they are fictional so we can’t use them as peer-reviewed or empirically tested research. But many of their ideas are informed by emerging trends and signals. If we can track down the signals then we can use that research to justify what we write about. Does that make sense?
Thanks for reading my post.
Claire