Skip to Main content Skip to Navigation
Conference papers

Predicting Comprehension from Students’ Summaries

Abstract : Comprehension among young students represents a key component of their formation throughout the learning process. Moreover, scaffolding students as they learn to coherently link information, while organically construct- ing a solid knowledge base, is crucial to students’ development, but requires regular assessment and progress tracking. To this end, our aim is to provide an automated solution for analyzing and predicting students’ comprehension levels by extracting a combination of reading strategies and textual complexity factors from students’ summaries. Building upon previous research and enhancing it by incorporating new heuristics and factors, Support Vector Machine classification models were used to validate our assumptions that automatically identified reading strategies, together with textual complexity indices applied on students’ summaries, represent reliable estimators of comprehension.
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01205372
Contributor : Philippe Dessus <>
Submitted on : Tuesday, September 29, 2015 - 10:40:36 AM
Last modification on : Friday, March 27, 2020 - 10:31:11 AM
Document(s) archivé(s) le : Wednesday, December 30, 2015 - 10:12:47 AM

File

aied15.pdf
Files produced by the author(s)

Identifiers

Collections

TICE | LSE | UGA | TEL

Citation

Mihai Dascălu, Lucia Larise Stavarache, Philippe Dessus, Stefan Trausan-Matu, Danielle Mcnamara, et al.. Predicting Comprehension from Students’ Summaries. 17th Int. Conf. on Artificial Intelligence in Education (AIED 2015), Jun 2015, Madrid, Spain. ⟨10.1007/978-3-319-19773-9_10⟩. ⟨hal-01205372⟩

Share

Metrics

Record views

402

Files downloads

721