How Online Learning Shaped My Classroom
by Marcus Birkenkrahe
A recent conversation with Amy Peterson made me realize how profoundly online platforms like DataCamp have influenced my in-person teaching.
In my data science classes, I rarely stand at the whiteboard (partly because my handwriting rivals Berlin street graffiti on a rough day). Instead, I code live, with students coding along in real time. This is punctuated by brief info bursts and open-ended questions to bridge concepts. The key? Examples must be immediately practical and spark curiosity—driving students to dig deeper on their own.
This approach is straight out of DataCamp’s playbook: light-touch teaching with heavy emphasis on interactivity, bite-sized content, and relevant, hands-on exercises. I’ve honed my own skills this way, even with my non-traditional background in physics and business information systems.
Interestingly, DataCamp’s interface, which blends explanations, code editors, instructions, and consoles for interpreted languages like R, Python, or SQL, mirrors literate programming. (I’ve explored this in a 2023 paper [1]; it’s my go-to for teaching data science, weaving code and narrative for deeper understanding.)
We often romanticize the “real” classroom as the gold standard, with online learning as a mere echo, lacking the raw human energy, like spotting a student’s furrowed brow under pressure. But generative AI is flipping that narrative, highlighting how virtual and physical worlds can enrich each other.
At Lyon College, our partnership with Rize Education exemplifies this. We’re launching majors in computer and data science focused on cyber security, game development, and AI. These hybrid programs blend our foundational curriculum with high-quality asynchronous courses, giving students flexibility they crave.
A decade ago at The Berlin School of Economics and Law (HWR Berlin), I flipped classrooms [2]; 14 years back, I taught in virtual worlds likeSecond Life [3] . That seemed pretty bold then but wouldn’t turn heads now. Hybrid learning is here, and data science leads the charge, thanks to its digital roots. But its model — interactive, scalable, learner-centered — is exportable to any field.
Over the coming weeks, I’ll share insights on managing data science curricula: breaking down complex concepts, anticipating learner needs, and integrating real-world applications to improve the teaching experience (for both learners and instructors).
References
[1] Birkenkrahe: Teaching data science with literate programming tools (2023). https://www.mdpi.com/2673-6470/3/3/15
[2] Birkenkrahe, Kjellin: Improving Student Interaction and Engagement in the Flipped Classroom (2015). https://www.researchgate.net/publication/284168410_Improving_Student_Interaction_and_Engagement_in_the_Flipped_Classroom
[3] Habermann, Birkenkrahe, Quade: Ubiquitous Project Management Using Interactive Virtual 3D Worlds (2011). https://www.researchgate.net/publication/259677913_Ubiquitous_Project_Management_Using_Interactive_Virtual_3D_Worlds