New Pedagogical Ideas and the virtual space.

Keino Baird
4 min readJan 12, 2022

I am a learner. This is the permanent stance I take when engaging almost everything I do, especially work. I have spent all of my career in the ‘teaching and learning space’ and outside of the classroom, my work with teachers I have done and still do is as a professional learning specialist. After leaving data science boot camp, spending all of 2020 learning, I selfishly (to learn) picked up a part-time role teaching data science in June 2021 until last night (1/11/22) when all the joy from the role permanently disappeared.

I am a facilitator. When working with adults (mostly teachers), this is the hat that I wear. The vantage point of a facilitator in an era of personalized learning is critical to the creation of a collaborative learning space or community irrespective of length. Whether facilitating a one-time session or teaching a long-term class, a learning space is created and a community is established with its unique culture. Building and creating learning communities is a skill facilitators need for learning to be sticky and engaging irrespective of the learner — whether elementary school students on a virtual platform, teachers participating in professional development online, or adults learning data science via zoom.

Big Oh.

With my stance and my hat, this is the approach I take to my work, much of it in the education space, looking at issues such as data, instruction, and student engagement. In essence, it is a merging of the analytical with the pedagogical to the practical, a very interesting niche space as data science continues to evolve in the education space to gain greater insight into teaching and learning, especially on virtual platforms where data can be easily collected.

For example, the issue of student engagement on a platform such as Zoom, when cameras are off. I relied heavily on the chat feature, but going even further, conducting my own analysis using ideas of natural language processing to see emerging themes from an instructional perspective about what students understand. Additionally, easy access to the internet, allows students to research learning content that they can contribute to the chat, that other students can read and respond to. All of this is part of student learning and engagement. There is also an assessment component to this, but that’s for another time.

I am a collaborator. Data science work cannot be done in isolation. Data Science is a team thing. From the first night of the data science course, collaboration became a feature and a practice that was embedded into the overall learning structure of the class. A culture must be created that promotes risk-taking, a culture that appreciates and values alternative approaches to problem-solving, and a culture where we seek out clarity through effective communication. In addition to the technical skills, students were gaining they were also developing skills needed to communicate these technical ideas into everyday language and solutions in their future work and to be effective on cross-functional teams.

I am a designer. The instructional team I led, my teaching assistants, had strong technical backgrounds, in fact much stronger than mine and I appreciated this. This was a benefit for students, especially in areas such as debugging to a deeper knowledge of how the math behind the algorithms worked. Much of the time spent on the instructional design was taking already created content and strategically curating that content to lay the conceptual groundwork and foundational understandings. Students had the opportunity to watch a video five times, if needed, use a variety of supplemental sources to complement initial understanding, make use of the documentation, take multiple at-bats, live code-along, labs, and homework were all part of designing instructional experiences that were learner-centric. These strategies promoted the concept of learning to learn data science collaboratively but also established a concrete structure, strategies, and path for future independent learning.

There is always a straw that breaks the camel’s back but there is absolutely no need to elaborate there. We must never overstay our welcome anywhere and know when the winds of change are moving us in another direction, allowing us to find even more joy and new challenges allowing us to grow. We can be dismissive of the old, but rigid pedagogies are harmful to children and adults alike, especially in a time when greater empathy is needed as the world of work and learning and how we learn has rapidly changed and quickly evolved.

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Keino Baird

Keino is a data nerd, a data science student at Lambda School and an educational consultant.