Coding lab

Overview

• Code similarity check
• Environment configuration for Jupyter Notebook lab
• Input data for failed IO tests

What’s new? 

Code similarity check 

Coding labs can now be configured to automatically check learners’ submissions for similar code. The system compares the code in a learner’s solution to the lab with the solutions of other learners and measures the similarity of the code. If the code in some solutions looks similar, a warning is displayed when the instructor assesses the lab. The instructor can open both solutions side by side, compare the code, and either decline or accept the solution.

Environment configuration for Jupyter Notebook lab 

An author can define a set of software libraries that are required to complete a lab. A learner can quickly install all the required libraries and their dependencies, which makes it easier to set up the lab environment.

Input data for failed IO tests 

Failed Input/Output tests now display the information about the input data that caused errors. This makes it easier to diagnose code issues.