Measuring inconsistency in written feedback: a case study in politeness

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Abstract

Feedback, indisputably, has been widely recognized as one of the most important forms of communication between teachers and students and a significant lever to enhance learning experience and success. However, there is consistent evidence showing that higher education institutions struggle to deliver consistent, timely, and constructive feedback to students. This study aimed to investigate whether, and to what extent, feedback inconsistency manifested itself in terms of politeness displayed to students of different demographic attributes (i.e., gender and first-language background). To this end, a large-scale dataset consisting of longitudinal feedback given to 3,249 higher-education students in 35 courses were collected and analyzed by applying multi-level regression modeling. We demonstrated that there were significant differences between low-performing and high-performing students as well as between English-as-second-language and English-as-first-language students. However, the majority of variance measured in the politeness of feedback was explained by course-level and assessment-level characteristics, while student-level characteristics accounted for less than 1% variance.

Original languageEnglish
Title of host publication23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part I 123
EditorsMaria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Place of PublicationCham Switzerland
PublisherSpringer
Pages560-566
Number of pages7
ISBN (Electronic)9783031116445
ISBN (Print)9783031116438
DOIs
Publication statusPublished - 2022
EventInternational Conference on Artificial Intelligence in Education 2022 - Durham, United Kingdom
Duration: 27 Jul 202231 Jul 2022
Conference number: 23rd
https://link.springer.com/book/10.1007/978-3-031-11644-5

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13355
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Intelligence in Education 2022
Abbreviated titleAIED 2022
Country/TerritoryUnited Kingdom
CityDurham
Period27/07/2231/07/22
Internet address

Keywords

  • Automatic feedback analysis
  • Feedback inconsistency
  • Hierarchical regression modeling
  • Politeness

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