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CS: Objects in Python

Introduction to Objects in Python with native Codio content promoting engagement and active learning with fully auto-graded assessments and minimal text.

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Topics

  1. Introduction to Objects
  2. Mutability
  3. Inheritance*
  4. Encapsulation*
  5. Polymorphism*
  6. Advanced Topics*
    * coming soon

Encouraging Customization Through Modularity

This content is not a one-size-fits-all solution. Instead, it implements a modular format. Natural break points occur in the curriculum where instructors can make the changes they deem necessary. Instructors can re-name, re-order, or remove units.

Using Codio’s excellent content authoring tools, they can author new material. This modular approach gives instructors flexibility when designing the learner’s experience

CS Trajectory

CS: Objects in Python is the second set of resources in Codio's Python trajectory. This content assumes students are familiar with topics in CS: Introduction in Python, and makes a natural continuation.

Constructing Knowledge Through Coding

CS: Introduction in Python emphasizes students applying and exploring the information presented. A code editor accompanies each page with new concepts so students can see for themselves how the computer responds to code. In addition, the content provides code snippets to get students started as well as suggested avenues for investigation.

Auto-Graded Assessments

Students receive immediate, rich feedback. In addition to correctness feedback (i.e. right or wrong), students will also see an explanation with the complete solution. There are a wide variety of questions — all of which are auto-graded, giving students a sense of their understanding of the material right after they are introduced to it and as they attempt harder and harder problems.

Lowering the Barrier to Entry

CS: Introduction in Python reflects the need for computer science education to meet students where they are. Like any specialized community, computer science has its own jargon. The formal teaching of computer science should not burden students with the assumption that they are fluent in this special language. The material is presented in smaller units that are more manageable for the students. The same vocabulary and concepts are covered, but in a more approachable way — state things as plainly as possible, and, when appropriate, use images, tables, or lists.

Another way in which this content is more approachable is that it is using many small programs instead of one large program. Research shows that a variety of smaller problems increase student performance and reduce stress. Using many small programs leads to students spend a sufficient amount of time on their work, and they do not wait until the last moment to begin their work. 

Take a peek inside...

  • Images help call out important details
  • Minimal text
  • Example code snippets that can be copied
  • Full IDE in the same browser window
  • Interactive content allows you to highlight lines of code
  • Run code with a click of the button
  • Use the code visualizer to see under the hood
  • Parsons problems and other formative assessments help students check their understanding

cs-objects-pythoncs-objects-python

 

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