Abstract
Over the last few years, Computer Science class sizes have increased, resulting in a higher grading workload.
To manage this workload, universities often use multiple graders to deliver the grades and associated feedback quickly.
While using multiple graders enables the required turnaround times to be achieved, it does come at the cost of consistency and feedback quality.
Automating the process of grading and feedback could help solve these issues.
This project will look into methods to fully or partially automate grading and feedback, such as machine learning and natural language processing, to improve grade uniformity and feedback quality.
To manage this workload, universities often use multiple graders to deliver the grades and associated feedback quickly.
While using multiple graders enables the required turnaround times to be achieved, it does come at the cost of consistency and feedback quality.
Automating the process of grading and feedback could help solve these issues.
This project will look into methods to fully or partially automate grading and feedback, such as machine learning and natural language processing, to improve grade uniformity and feedback quality.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education Vol 2 |
| Publisher | ACM |
| Number of pages | 2 |
| ISBN (Electronic) | 978-1-4503-9200-6/22/07 |
| Publication status | Published - 7 Jul 2022 |
Keywords
- Automated Grading
- Feedback
- Assessment