Extending Canvas/H5P to support adaptive learning in large programming courses
Following a successful application for a Learning Enhancement and Innovation (LEI) Grant in 2020, Dr Cheryl Pope, School of Computer Science, shares the new Parsons Puzzles H5P learning activity built collaboratively with students as partners.
Practice is crucial to deep learning, but not all practice is equally productive and it can be difficult to provide timely feedback at scale. Cognitive load theory indicates that time spent practicing a concept helps the concept to be embedded in long-term memory, but equally the theory shows that practicing a concept already mastered can be detrimental (‘reversal effect’). Students as learners benefit from feedback on when they have mastered a concept and are ready to move on. Providing such feedback is challenging in large courses, where feedback time is very limited.
Within Computer Science, traditional approaches to student practice have been to provide worked examples and then ask students to code from scratch. Although this is effective, Parsons Puzzles (where students are given parts of the code to assemble and/or complete), have been shown to be equally effective while taking significantly less time. This opens the possibility for more practice for students who need it, when they need it, without overloading students who have mastered a concept.
Listen to Dr Cheryl Pope as she introduces you to Parsons Puzzles and the new opportunities this approach has created.