T127 Reflection 2

On Observations of a Learning Designer

The halfway point of the semester forced me and my team to take a step back and look at all the ongoing processes in the data literacy project. Having worked closely with the TLL team, I realize that the project covers such a wide scope, even in its infancy. The discovery process, which often involves lots of meetings, coordinating with different departments and stakeholders, and dealing with ambiguity, taught me that the role of a learning designer encompasses a far wider scope than I originally expected. It wasn’t only the multitude of things that often seem like they are within a learning designer’s scope, but rather work that involves administrative or human relations aspects like leading focus groups or creating surveys that make it complex yet compelling.

The sheer amount of context switching involved is something that I have never been acquainted with in any of my previous HR work. One needs to be able to distill information to those Responsible and Accountable, contribute meaningfully and ask valuable questions to those Consulted, and most importantly, formulating/scaffolding questions for the Informed whilst simultaneously having the ability to listen closely to their answers. I now value the rolling agenda much more as it proves to be the single source of truth for which all work informs or is informed of.

Learning designers need to have the ability to talk to each stakeholder without confusing them, whilst also having the theory of mind about what each stakeholder is saying, or more importantly, not saying (tacit knowledge). All these skills need to work hand-in-hand with a learning designer’s metacognitive ability. A learning designer shouldn’t only think about their thinking and biases but also think about the metacognitive states of stakeholders they’re in contact with or a lack thereof.

On Capstone

I’m interested in the idea of authentic assessments and contextualization. I think it is invaluable for all Harvard staff members to have a foundational understanding of data literacy/fluency. More importantly though, is the importance of each staff member being able to put seemingly unidirectional lessons they’ll receive in future modules into their own context. I believe that for one to truly understand, they need to be able to contextualize a concept into their own job stack, or in this case, make sense of their own data. This trend will become more important in the future, and Harvard could start implementing it now through integrating solutions or components into the learning modules that force staff members to think about how they’ll improve data handling in their own unique (job) position. Data literacy, especially in the more advanced tiers, requires a set of interpretive skills so that a worker could fully make use of the learnings they’ve gone through. Data fluency is different for a HGSE career consultant and a HBS student admission officer.

What mode this authentic learning will take in the future will be my capstone focus. I aim to work closely with the TLL team to be involved in work that bridges discovery and design. Early talks have pointed towards adding a role-play element or writing case studies. I’m skeptical of the latter as it lacks the dynamic component that allows absolute contextualization of learning materials, but I also restrain myself from being a solutionist by saying that AI modules are the best way to achieve contextualization. Nevertheless, it would be great to make thinking visible, at least for the Harvard context. Who knows, these modules and user learning data could inform data practices in the future and/or tools that are used within Harvard. Though ambitious, there might be a lot of positives from centralizing learning data.