“Blended learning:” not again!
It’s a term so cringey only a consultant would love it, but “blended learning” has made waves in the education space. As you may expect from such a trendy phrase, it lacks precise definition, but here are some traits of blended learning:
- No surprise, but it’s a mix of traditional in-person and online deliveries.
- As such, it’s a mix of traditional lecture-based and activity-based class time.
So, once you start augmenting and disrupting the traditional model of preparing for class, attending class, and doing homework based on that class, you’ve got blended learning.
In particular, blended learning for data education involves a mix of concept-based instruction and project-based job tasks. This is primarily professional education, so the focus is on practical application, ideally done in a way that signals and provides value to employers.
A solid data analytics education delivery is blended and involves a mix of the following four formats. You can imagine a combination of one or more of these could power, for example, a module on learning financial modeling in Excel, or building a data cleaning pipeline in Python.
In-person sessions
Blended learning does not replace traditional classroom learning, only augments it. In-person learning can still play a central part.
Data professionals are busy. It can be wishful thinking that they’ll get through a module while at their desk during a period of downtime — the downtime never comes, and up-skilling falls entirely off their radar.
In-persion sessions provide a distraction-free zone for students. With their calendar blocked off expressly, they’re more likely to commit. It’s easier to procrastinate learning when it’s just another thing you need to do at your desk when the workday is over.
In-person sessions also provide an environment conducive for students to articulate questions and communicate their knowledge. Learning is a social process; the word “college,” for example, literally means “coming together.”
These in-person sessions could involve traditional lectures, although those might not be so effective. It’s probably better to include a mix of instructor recap of the material and interactive exercises. A flipped classroom is also an option, and pairs nicely with the next delivery method.
Online study
Learning data takes lots of study and practice, and online delivery methods are great for both.
In a traditional course design, students prepare for in-person instruction by reading on their own. This could still work for data education, but reading is supplemented with activities that move the student from passive to active learner.
A data program could use Slack, wikis or message boards to keep communication going asynchronously. Various educational technology exists to assess students as they code.
Since so much of data has to do with working through problems, data education fits nicely with a flipped classroom, where students receive the instruction between class periods, then work through the “homework” in class.
Work-based projects
Where things get really interesting with blended learning for data education is in mixing the course content with real-world applications.
Students can take data problems that have vexed them at work and see them with fresh eyes as a student. There’s also a sense of immunity that people feel as students: they are empowered to think differently and confidently about solution and alternatives.
To what extent this “student card” confidence plays out well with the organization is definitely a cultural thing. But openness to innovation is not unique to data education — it’s something that any organization needs to settle.
Hackathons
Last but not least, hackathons provide students the opportunity to work on existing work projects, or novel problems, for an extremely concentrated amount of time (usually a weekend or less).
Hackathons remove people from their routines, which can trigger innovation. With the frenzied scramble of late-night problem-solving, hackathons are also an esprit de corps-building force.
Blending methods for teaching data
There are likely other delivery methods for blended learning in data. I see these as four compatible “slices” to design a solid program.
Regardless of how you stack these slices, or if you incorporate others, the takeaway from blended learning is to think outside the traditional lecture-based analog classroom. Technology can be paired with in-person methods to dazzling effect.
Want to talk more about using blended learning to up-skill your organization’s data capacities? Drop me a line or set up a free call now.
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