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22 juin 2015
- handle: 10670/1.tzk257
- hal: hal-01205372
- doi: 10.1007/978-3-319-19773-9_10
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-19773-9_10
Ce document est lié à :
info:eu-repo/grantAgreement/EC/FP7/212578/EU/Language Technology for Lifelong Learning/LTFLL
info:eu-repo/semantics/OpenAccess
Mots-clés
Reading strategies textual complexity Summaries assessment Comprehension prediction Support vector machinesSujets proches
School life Students--Education Pupils Student life and customsCiter ce document
Mihai Dascălu et al., « Predicting Comprehension from Students’ Summaries », HAL-SHS : sciences de l'éducation, ID : 10.1007/978-3-319-19773-9_10
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Résumé
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.