Enhancing Programming Ability with Playful Learning and Karel
Part of the Education specialist track
Beginner programmers often struggle to understand and trace program execution, which is worsened by underdeveloped debugging and testing skills. Beginners may also lack confidence or are easily demotivated, which can hinder learning. To assist students in developing these skills and build confidence we created a set of playful programming challenges and competition using Karel the Robot. The Karel system provides a 2D “grid world” where the Karel character can move and interact with its environment. The 2D world is visualised for students so that they can immediately see how their program changes the environment step by step as well as the final program state. This is in contrast to traditional languages where learners must develop and maintain a mental model of the program state. This talk will cover our approach, preliminary results and feedback from students showing an increase in confidence and interest in programming. We will also share how this approach can be applied to other learning contexts.
See this talk and many more by getting your ticket to PyCon AU now!
I want a ticket!Beginner programmers often struggle to understand and trace program execution, which is worsened by underdeveloped debugging and testing skills. Beginners may also lack confidence or are easily demotivated, which can hinder learning. To assist students in developing these skills and build confidence we created a set of playful programming challenges and competition using Karel the Robot.
Karel the Robot was developed at Stanford to help students learn programming concepts without the burden of syntax and technicalities of a more general language. Karel provides a 2D ‘grid world’ where the Karel character can move and interact with the environment to carry out various tasks, e.g. walk through a maze. This gives students the opportunity for playful experimentation with code, which helps develop their understanding of programming concepts. Furthermore the 2D world is visualised for students so that they can immediately see how their program changes the environment step by step as well as the final program state. Karel makes the code tangible to the students, which is in contrast to traditional languages where learners must develop and maintain a mental model of the program state.
Fortunately Karel uses a subset of Python, which allows students to naturally extend their learning to real Python, without learning new syntax. We found that Karel provides a convenient way to implement inductive teaching, which has been shown to enhance student's higher order thinking abilities and strengthen their understanding of concepts when compared to deductive teaching.
This talk will cover our approach, preliminary results and feedback from students showing an increase in confidence and interest in programming. We will also share how this approach can be applied to other learning contexts.
Stephen is a Senior Lecturer at the University of Sydney in the fields of Statistics, Data Science and Machine Learning
Alison is a lecturer at the University of Sydney in Business Analytics specialising in teaching programming, mathematics and machine learning.