This e-learning trend can be used to efficiently meet your data science goals
It’s now reasonable to compliment someone by saying they have the attention span of a goldfish.
Where humans were once thought to be the superior race with an attention span of 12 whole seconds, studies over the last 15 years have shown the average human attention span drop to 8.25 seconds.
The attention span of a goldfish is 9 seconds.
With the average human checking their inbox 30 times every hour, picking up their phone more than 1,500 times per week (with a usage of 3 hours and 16 minutes per day), and only reading an average of 28% of the words on a web page, it’s safe to say that we, as a species, are easily distracted.
This easily distracted nature translates itself poorly to the study of complex subjects, such as those associated with data science.
The traditional way of learning is no longer an option. Enter microlearning.
What is microlearning?
Microlearning is an e-learning and instructional design trend referring to an educational approach that uses small learning units to deliver just the necessary amount of information required by learners to achieve their goals.
Microlearning is an efficient shift away from the day-long courses and dry PowerPoint presentations that would have their participants dozing off in the first 2 minutes. Instead, the goal is to deliver bite-sized lessons that cover only 1–2 topics that can be consumed in less than 10 minutes.
According to a study published in the Journal of Applied Psychology, microlearning is 17% more efficient than traditional learning methods. This increased efficiency comes from learners only having to digest small chunks of information which improves topic retention and comprehension.
Microlearning also creates 50% more learning engagement, as found by a study conducted by Software Advice. This increased engagement comes from the implementation of learning sessions that match the attention span of humans.
How does the use of microlearning benefit the data science learning experience?
The study of data science is, in short, a long, arduous process. Compounded by the need to be a well-rounded individual with skills in programming, mathematics, machine learning, artificial intelligence, business-savvy, and more, the journey towards data science is not often seen through.
Data science is like golf. You could spend your life working towards mastering it and you will have only scratched the surface.
Therefore, the learning process must be as expedited and efficient as possible. No, this doesn’t mean taking shortcuts that leave foundational holes in your knowledge to get to the “good” stuff quicker. This means breaking down complex topics into bite-sized lessons where you finish the day having learned 1–2 topics that have yielded 4–5 takeaways.
Who microlearning is and isn’t for.
There’s a small caveat to the use of microlearning for the study of data science that must be addressed.
Microlearning is a beneficial learning tool for those who already have a basic foundation in data science skills. Why? Because it would take decades to learn the foundations of data science using the microlearning method. Those who are looking to transition into data science usually do so within a given window of time (usually before their safety net runs out). Therefore, only spending 10 minutes per day learning concepts will leave you knowing only the bare bones of data science after 6 months of study.
This learning method is for people who: already have a foundational knowledge of data science and are looking to improve their skills by studying more complex topics. These people can already do basic data analyses, can write code, and can apply their findings to solve business problems. This learning method is a great way for professionals to take 10 minutes out of their day to improve their skills in a given topic without disrupting their regular work.
This learning method is not for people who: are complete newcomers to data science. This learning method can be used in tandem with more traditional learning methods to supplement the more complex topics.
How to use microlearning to improve data science skills.
Setting up microlearning modules for yourself can take more time than logging into a MOOC or watching a Youtube video, but the results can yield great improvements in shorter periods of time.
How to set up your personalized microlearning modules:
- Set aside dedicated time to plan out your content: To succeed with developing a microlearning plan, you need to dedicate time to the process. A few hours is all it takes to decide what you want to learn and how you plan on learning it.
- Make a list of the concepts you want to learn: This list will set the framework for your modules. For example, if you want to learn NLP, make a list of all the concepts associated with NLP. Break them down into individual parts or group them into small logical modules.
- Make sure the microlearning process suits each of the concepts: Sometimes, microlearning isn’t sufficient for learning in-depth concepts. For example, the entirety of basic statistics could not be learned with microlearning because a more in-depth and long-term study is required. A good way to test if a concept is well-suited for microlearning is to determine how long you think it will take to learn the concept. If it takes longer than 1 hour, it’s probably not for microlearning.
- Trim the fat: Microlearning is all about learning small amounts of information in short periods of time. This means that you have to trim the content down to the bare bones. Include everything that you need and nothing that you don’t.
- Make sure that each module or lesson has a few key takeaways and only covers 1–2 objectives. It can become easy to want to add everything into a lesson, especially when it’s concerning a topic you’re excited about. However, these must remain miro-lessons to ensure that you’re getting the full effect of microlearning. If your lesson answers 1–2 questions, great! If it answers more, try breaking down the content further.
- Don’t restrict yourself to 10-minute sessions if it isn’t working: Sometimes, no matter how much fat you trim off, a concept just won’t condense itself into a 10-minute lesson. In this instance, don’t restrict yourself. Complete the lesson as is if it is less than 20 minutes in length, or consider breaking it into two lessons.
- Use multimedia to keep things interesting: Reading a textbook is boring. Watching a video is not. Pick interesting media that will keep you engaged and that add to the learning experience.
- Use micro-assessments to assess your progress: Small tests and quizzes are great ways to assess your progress in learning these topics. Examples of simple tests include but aren’t limited to completing a coding challenge on HackerRank or Kaggle, or taking 10 minutes and writing everything you know about a topic and comparing what you wrote to your notes on the topic.
After microlearning comes micro-practice.
Microlearning is nothing without micro-practice.
Adopting new skills requires practice, not simply rote memorization. Using microlearning to learn data science concepts is no exception to this rule.
The trick is to pair microlearning modules with micro-practice sessions that use the exact skills learned in a more practical situation. For example, if you just completed a microlearning module on tokenization in NLP, you should then complete a micro-practice where you take a data set of running text and segment it into sentences and words, otherwise known as tokens.
Micro-practice can be as simple as completing a coding challenge on HackerRank or Kaggle, or it can be as complex as implementing your new skills into your own personal project.
Whatever the type of practice, a 10-minute micro-practice session should follow the completion of a microlearning module to solidify your newfound knowledge.
Final thoughts.
When it comes to learning data science, every trick in the book must be used for success to be achieved. This means using every form of learning possible to ensure that you are making the most of your time and learning the concepts necessary to become more effective and impactful in this results-driven industry.
By exploring alternative learning methods, you open yourself up to having the best chance possible at mastering an un-masterable field of knowledge.
For a species with a smaller attention span than a goldfish, we need all the help we can get.
Source: medium
