Computer Science
|
9th, 10th, 11th
O(n) review session with real life examples
Have AI conduct a review session with students by providing practical engineering problems where the O(n) time complexity has to be determined.
Teaching goals
Learning time complexity is a critical part of improving students’ algorithmic thinking skills.
Typically, teachers might have students read about comparisons between different time complexities (logarithmic, quadratic, etc.), along with example algorithms for each. Or, teachers may talk through examples with slides or by playing an instructional YouTube video.
With Flint, teachers can take pre-existing static content and turn it into an interactive experience tailored to each student's level. Below, we can see that the teacher has entered a learning objective into Flint, along with a textbook chapter covering examples of time complexity as well as a YouTube video on the topic:
Learning objective:
Students should be able to identify the optimal time complexity (constant, logarithmic, linear, or quadratic) of an algorithm based on a description of a practical engineering problem. After the student gets it right, they should be given the code and should be able to write the time complexity using Big O notation.
YouTube | Big-O notation in 5 minutes
Extra customization
Based on the information provided by the teacher, Flint automatically creates an AI tutor that will give students examples of practical engineering problems and then ask students to identify the optimal time complexity.
Because the uploaded textbook chapter had examples given in Python, the AI tutor will also provide code in Python. However, the teacher can customize this behavior by using the “revise” feature to ask the AI to provide coding snippets in Java (or any other language) instead.
Student experience
Once students start a session with this AI tutor, they’ll immediately get practice in identifying time complexity based on real-life examples.
Once students correctly describe the time complexity, the AI will provide them with a code snippet and will ask them to write the time complexity using Big O notation.
If the student needs extra help in understanding time complexity, they can ask additional questions to the AI, which can use graphing when appropriate to better explain the concept.
Extra customization
Based on the information provided by the teacher, Flint automatically creates an AI tutor that will give students examples of practical engineering problems and then ask students to identify the optimal time complexity.
Because the uploaded textbook chapter had examples given in Python, the AI tutor will also provide code in Python. However, the teacher can customize this behavior by using the “revise” feature to ask the AI to provide coding snippets in Java (or any other language) instead.
Computer Science
|
9th, 10th, 11th
O(n) review session with real life examples
Teaching goals
Learning time complexity is a critical part of improving students’ algorithmic thinking skills.
Typically, teachers might have students read about comparisons between different time complexities (logarithmic, quadratic, etc.), along with example algorithms for each. Or, teachers may talk through examples with slides or by playing an instructional YouTube video.
With Flint, teachers can take pre-existing static content and turn it into an interactive experience tailored to each student's level. Below, we can see that the teacher has entered a learning objective into Flint, along with a textbook chapter covering examples of time complexity as well as a YouTube video on the topic:
Learning objective:
Students should be able to identify the optimal time complexity (constant, logarithmic, linear, or quadratic) of an algorithm based on a description of a practical engineering problem. After the student gets it right, they should be given the code and should be able to write the time complexity using Big O notation.
YouTube | Big-O notation in 5 minutes
Extra customization
Based on the information provided by the teacher, Flint automatically creates an AI tutor that will give students examples of practical engineering problems and then ask students to identify the optimal time complexity.
Because the uploaded textbook chapter had examples given in Python, the AI tutor will also provide code in Python. However, the teacher can customize this behavior by using the “revise” feature to ask the AI to provide coding snippets in Java (or any other language) instead.
Student experience
Once students start a session with this AI tutor, they’ll immediately get practice in identifying time complexity based on real-life examples.
Once students correctly describe the time complexity, the AI will provide them with a code snippet and will ask them to write the time complexity using Big O notation.
If the student needs extra help in understanding time complexity, they can ask additional questions to the AI, which can use graphing when appropriate to better explain the concept.
Other Computer Science teacher testimonials:
My high-achieving students are exploring deeper topics via the ability to question and explore areas of interest. I've seen some students spend an hour questioning the standard tutor for ideas. My lower-achieving students are getting basic questions answered without embarrassment or frustration. They move more quickly through the content and stumbling blocks are removed as soon as they arise.
Spencer Wagner
AP Computer Science teacher at Regis Jesuit
"I can't emphasize enough how Flint has revolutionized my teaching. Flint has been an invaluable tool for introducing new concepts and assessing student understanding. My students have embraced Flint wholeheartedly. My high flyers love how they can deep-dive into course content with an AI expert. Other students who need more attention can get a one-on-one tutor to help with their specific needs."
Matthew Davis
Computer science teacher at Episcopal
"Even as the initial novelty of Flint wore off, engagement has stayed exceptionally high. With any other activity, some top students want to move to more complex material, and others need more time on basics. As a teacher, you are stuck trying to find a middle ground. In Flint's activities, I can rotate as a facilitator and Flint automatically scales the assignments to each student's skill level."
Jake Kazlow
Computer science teacher at Westminster
My high-achieving students are exploring deeper topics via the ability to question and explore areas of interest. I've seen some students spend an hour questioning the standard tutor for ideas. My lower-achieving students are getting basic questions answered without embarrassment or frustration. They move more quickly through the content and stumbling blocks are removed as soon as they arise.
Spencer Wagner
AP Computer Science teacher at Regis Jesuit
"I can't emphasize enough how Flint has revolutionized my teaching. Flint has been an invaluable tool for introducing new concepts and assessing student understanding. My students have embraced Flint wholeheartedly. My high flyers love how they can deep-dive into course content with an AI expert. Other students who need more attention can get a one-on-one tutor to help with their specific needs."
Matthew Davis
Computer science teacher at Episcopal
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Spark AI-powered learning at your school.
Sign up to start using Flint, free for up to 80 users.
Watch the video
Spark AI-powered learning at your school.
Sign up to start using Flint, free for up to 80 users.
Watch the video
Spark AI-powered learning at your school.
Sign up to start using Flint, free for up to 80 users.
Watch the video