TY - GEN
T1 - VerAs
T2 - 25th International Conference on Artificial Intelligence in Education, AIED 2024
AU - Atil, Berk
AU - Sheikhi Karizaki, Mahsa
AU - Passonneau, Rebecca J.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role. A recently published dataset of two sets of college level lab reports from an inquiry-based physics curriculum relies on analytic assessment rubrics that utilize multiple dimensions, specifying subject matter knowledge and general components of good explanations. Each analytic dimension is assessed on a 6-point scale, to provide detailed feedback to students that can help them improve their science writing skills. Manual assessment can be slow, and difficult to calibrate for consistency across all students in large enrollment courses with many sections. While much work exists on automated assessment of open-ended questions in STEM subjects, there has been far less work on long-form writing such as lab reports. We present an end-to-end neural architecture that has separate verifier and assessment modules, inspired by approaches to Open Domain Question Answering (OpenQA). VerAs first verifies whether a report contains any content relevant to a given rubric dimension, and if so, assesses the relevant sentences. On the lab reports, VerAs outperforms multiple baselines based on OpenQA systems or Automated Essay Scoring (AES). VerAs also performs well on an analytic rubric for middle school physics essays.
AB - With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role. A recently published dataset of two sets of college level lab reports from an inquiry-based physics curriculum relies on analytic assessment rubrics that utilize multiple dimensions, specifying subject matter knowledge and general components of good explanations. Each analytic dimension is assessed on a 6-point scale, to provide detailed feedback to students that can help them improve their science writing skills. Manual assessment can be slow, and difficult to calibrate for consistency across all students in large enrollment courses with many sections. While much work exists on automated assessment of open-ended questions in STEM subjects, there has been far less work on long-form writing such as lab reports. We present an end-to-end neural architecture that has separate verifier and assessment modules, inspired by approaches to Open Domain Question Answering (OpenQA). VerAs first verifies whether a report contains any content relevant to a given rubric dimension, and if so, assesses the relevant sentences. On the lab reports, VerAs outperforms multiple baselines based on OpenQA systems or Automated Essay Scoring (AES). VerAs also performs well on an analytic rubric for middle school physics essays.
UR - http://www.scopus.com/inward/record.url?scp=85200259843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85200259843&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-64302-6_10
DO - 10.1007/978-3-031-64302-6_10
M3 - Conference contribution
AN - SCOPUS:85200259843
SN - 9783031643019
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 148
BT - Artificial Intelligence in Education - 25th International Conference, AIED 2024, Proceedings
A2 - Olney, Andrew M.
A2 - Chounta, Irene-Angelica
A2 - Liu, Zitao
A2 - Santos, Olga C.
A2 - Bittencourt, Ig Ibert
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 July 2024 through 12 July 2024
ER -