How Well Can You Articulate that Idea? Insights from Automated Formative Assessment

Mahsa Sheikhi Karizaki, Dana Gnesdilow, Sadhana Puntambekar, Rebecca J. Passonneau

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

Abstract

Automated methods are becoming increasingly used to support formative feedback on students’ science explanation writing. Most of this work addresses students’ responses to short answer questions. We investigate automated feedback on students’ science explanation essays, which discuss multiple ideas. Feedback is based on a rubric that identifies the main ideas students are prompted to include in explanatory essays about the physics of energy and mass. We have found that students revisions generally improve their essays. Here, we focus on two factors that affect the accuracy of the automated feedback. First, learned representations of the six main ideas in the rubric differ with respect to their distinctiveness from each other, and therefore the ability of automated methods to identify them in student essays. Second, sometimes a student’s statement lacks sufficient clarity for the automated tool to associate it more strongly with one of the main ideas above all others.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 25th International Conference, AIED 2024, Proceedings
EditorsAndrew M. Olney, Irene-Angelica Chounta, Zitao Liu, Olga C. Santos, Ig Ibert Bittencourt
PublisherSpringer Science and Business Media Deutschland GmbH
Pages225-233
Number of pages9
ISBN (Print)9783031642982
DOIs
StatePublished - 2024
Event25th International Conference on Artificial Intelligence in Education, AIED 2024 - Recife, Brazil
Duration: Jul 8 2024Jul 12 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14830 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Artificial Intelligence in Education, AIED 2024
Country/TerritoryBrazil
CityRecife
Period7/8/247/12/24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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