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.
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Communication dans un congrès
C. Conati; N. Heffernan; A. Mitrovic; M. F. Verdejo. 17th Int. Conf. on Artificial Intelligence in Education (AIED 2015), Jun 2015, Madrid, Spain. 2015, Artificial Intelligence in Education (AIED 2015). 〈http://www.springer.com/fr/〉. 〈10.1007/978-3-319-19773-9_10〉
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Mihai Dascălu, Lucia Larise Stavarache, Philippe Dessus, Stefan Trausan-Matu, Danielle Mcnamara, et al.. Predicting Comprehension from Students’ Summaries. C. Conati; N. Heffernan; A. Mitrovic; M. F. Verdejo. 17th Int. Conf. on Artificial Intelligence in Education (AIED 2015), Jun 2015, Madrid, Spain. 2015, Artificial Intelligence in Education (AIED 2015). 〈http://www.springer.com/fr/〉. 〈10.1007/978-3-319-19773-9_10〉. 〈hal-01205372〉

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