Grading Programming Assignments with an Automated Grading and Feedback Assistant

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Abstract

Over the last few years, Computer Science class sizes have increased, resulting in a higher grading workload. Universities often use multiple graders to quickly deliver the grades and associated feedback to manage this workload. While using multiple graders enables the required turnaround times to be achieved, it can come at the cost of consistency and feedback quality. Partially automating the process of grading and feedback could help solve these issues. This project will look into methods to assist in grading and feedback partially subjective elements of programming assignments, such as readability, maintainability, and documentation, to increase the marker’s amount of time to write meaningful feedback. We will investigate machine learning and natural language processing methods to improve grade uniformity and feedback quality in these areas. Furthermore, we will investigate how using these tools may allow instructors to include open-ended requirements that challenge students to use their ideas for possible features in their assignments.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings
EditorsMaria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
PublisherSpringerLink
Pages35-40
Number of pages6
ISBN (Print)9783031116469
Publication statusPublished - 28 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13356 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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