Have you ever taken a course or attended a learning session where you felt disengaged and disinterested from the start? Maybe the instructor jumped right into the material without any introduction or context. One way to make learning more fun and interactive is by using warm-up questions. They can help introduce new topics in an engaging and interesting way.
What are Warm-Up Questions?
Warm-Up questions are introductory questions you can pose at the beginning of a course or a learning session. You can use a warm-up question to review prior knowledge or to build interest in your topic. These questions can take the form of open-ended questions, hypothetical scenarios, or even trivia. The main goal of warm-up questions is to encourage learners to think critically and begin connecting with the new information they will be learning.
Warm-Up Question Example
Let's create an example to illustrate how warm-up questions can be used to engage learners.
The scenario is a zombie apocalypse. Despite the societal breakdown caused by zombies running amok, you possess a machine-learning system capable of identifying the most hazardous areas overrun by zombies. Your zombie dataset includes a wide array of information. You need to determine which features are essential to have in your machine learning model to create the most effective system.
Which of these features of zombies do you think will create the most useful machine-learning model?
a. Their favorite food and drink
b. Their preferred mode of transportation
c. Their speed, strength, and intelligence
d. Their favorite color and clothing style
Feature selection is a crucial step in machine learning as it involves choosing the most relevant features of a dataset to build an accurate module. In this scenario, the correct answer is C. To accurately identify zombie danger zones, their speed, strength, and intelligence are the most important features to consider. These features are the most relevant for understanding how zombies move, how dangerous they are, and how difficult they may be to overcome. The other factors listed, such as their favorite foods, are irrelevant to our goals for the model and will only add noise to the data.
In machine learning, feature selection is a crucial step because it helps simplify the data and improve the model's performance by focusing on the most relevant features. As a result, we can more accurately classify and predict zombie behavior. Now, let's try another question.
Which of the following is true about feature selection in machine learning?
a. It is not important to select relevant features, as all features contribute to the model
b. Including irrelevant features in the model can improve its accuracy
c. Feature selection helps to simplify the data and improve model performance
d. The more features included in the model, the better the performance will be.
The answer is C: feature selection helps to simplify the data and improve model performance in machine learning. Did you get the correct answer?
Not all your warm-up questions have to include zombies, but we've used this unlikely scenario to demonstrate how selecting the most informative features is essential to creating accurate and reliable models in machine learning.
Guided Discovery through Warm-Up Questions
The most interesting thing about warm-up questions is their use in guided discovery. Guided discovery is a teaching principle that involves guiding learners to explore and solve problems to discover new knowledge rather than just presenting the information. Warm-up questions are a great way to guide learners to actively engage with the material and discover information through critical thinking. Learners using warm-up questions are prompted to think about and explore the topic using their knowledge and experience, leading to better retention and deeper understanding of the material.
Putting It All Together
Warm-up questions are a powerful tool to engage learners and build interest in a topic. These questions encourage critical thinking and can be used to review prior knowledge or introduce new concepts. By using warm-up questions, you can prompt learners to think about the topic and explore it using their knowledge and experience, leading to better retention and a deeper understanding of the material. So go ahead, and create some fun and thought-provoking questions. Oh, and look out for zombies!
About Lisa Stringer
Lisa Stringer is a digital learning designer at Award Solutions, where she creates stories to help people learn more about technology topics like Python, cyber security, and 5G.
About Award Solutions, Inc.
Award Solutions is the trusted training partner to the world's best networks. We help companies tackle new technologies by equipping their teams with knowledge and skills. Award Solutions invests heavily in technology, research, engineering, and labs to ensure our customers make the most of their resource and network investments.
Award’s expertise extends across many technologies including 5G/LTE Access 5G/4G Core, VoNR/VoLTE, Transport Networks, Telco Cloud, Virtualization and Orchestration, Data Automation, and more.