Giving Feedback on Feedback: An Assessment of Grader Feedback Construction on Student Performance

Serena Nicoll, Kerrie Douglas, Christopher Brinton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Feedback is a critical element of student-instructor interaction: it provides a direct manner for students to learn from mistakes. However, with student to teacher ratios growing rapidly, challenges arise for instructors to provide quality feedback to individual students. While significant efforts have been directed at automating feedback generation, relatively little attention has been given to underlying feedback characteristics. We develop a methodology for analyzing instructor-provided feedback and determining how it correlates with changes in student grades using data from online higher education engineering classrooms. Specifically, we featurize written feedback on individual assignments using Natural Language Processing (NLP) techniques including sentiment analysis, bigram splitting, and Named Entity Recognition (NER) to quantify post-, sentence-, and word-dependent attributes of grader writing. We demonstrate that student grade improvement can be well approximated by a multivariate linear model with average fits across course sections between 67% and 83%. We determine several statistically significant contributors to and detractors from student success contained in instructor feedback. For example, our results reveal that inclusion of student name is significantly correlated with an improvement in post-feedback grades, as is inclusion of specific assignment-related keywords. Finally, we discuss how this methodology can be incorporated into educational technology systems to make recommendations for feedback content from observed student behavior.

Original languageEnglish (US)
Title of host publicationLAK 2022 - Conference Proceedings
Subtitle of host publicationLearning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages239-249
Number of pages11
ISBN (Electronic)9781450395731
DOIs
StatePublished - Mar 21 2022
Event12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States
Duration: Mar 21 2022Mar 25 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/TerritoryUnited States
CityVirtual, Online
Period3/21/223/25/22

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Keywords

  • Instructor feedback
  • Learning analytics
  • Student engagement

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