TY - GEN
T1 - How Well Can You Articulate that Idea? Insights from Automated Formative Assessment
AU - Karizaki, Mahsa Sheikhi
AU - Gnesdilow, Dana
AU - Puntambekar, Sadhana
AU - Passonneau, Rebecca J.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85200249430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85200249430&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-64299-9_16
DO - 10.1007/978-3-031-64299-9_16
M3 - Conference contribution
AN - SCOPUS:85200249430
SN - 9783031642982
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 225
EP - 233
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
T2 - 25th International Conference on Artificial Intelligence in Education, AIED 2024
Y2 - 8 July 2024 through 12 July 2024
ER -