Reflecting on Collaborative Learning (Williams, 2017) (W10)

Following my curiosity, I read the article "Assessing collaborative learning: big data, analytics, and university features." It was a tough read for me, specifically the epistemic issues section. In the end, though, I think I was able to make sense of it all.

At its core, Williams grapples with a key point: higher education has historically assessed individual performance through "tidy," controlled exercises, but the needs of the 21st-century workplace are anything but tidy. Employers increasingly seek graduates with soft skills, not just discipline-specific knowledge. So then, should universities change their focus?

The challenge is this: how do you assess learning in messy, authentic, collaborative spaces? The answer Williams expands upon is big data and learning analytics. He sees promise in social learning analytics, tools that can track and interpret students’ group interactions, contributions, and development over time. This opens the door to formative, ongoing feedback, and richer, more holistic assessments rather than GPA scores. Still, Williams cautions against using these tools for control or surveillance. Learning analytics can either serve as a liberating force for student growth or as a tool of restriction (if used only to flag risk or enforce conformity). 

Putting AI in this conversation, it becomes even more clear that universities are at a crossroads. As generative AI tools become more embedded in daily academic life, they challenge long-standing assumptions about what constitutes original work, individual effort, and valid assessment.

But Williams’ argument actually helps reframe this question. If knowledge is increasingly produced collaboratively and contextually, and if soft skills like communication, adaptability, etc. are what matter most, then perhaps AI isn’t a threat, but rather a mirror. It's reflecting back the need for institutions to prioritize the process of learning over the product. It forces us to ask: Are we assessing the right things?

As we navigate this shifting landscape where collaboration, AI, and soft skills are increasingly central what do you think universities should value most when it comes to assessing learning?

Reference: 

Williams, P. (2017). Assessing collaborative learning: Big data, analytics and university futures. Assessment & Evaluation in Higher Education, 42(6), 978-989. doi:10.1080/02602938.2016.1216084

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