TY - GEN
T1 - Predicting comprehension from students’ summaries
AU - Dascalu, Mihai
AU - Stavarache, Larise Lucia
AU - Dessus, Philippe
AU - Trausan-Matu, Stefan
AU - McNamara, Danielle
AU - Bianco, Maryse
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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 constructing 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.
AB - 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 constructing 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.
KW - Comprehension prediction
KW - Reading strategies
KW - Summaries assessment
KW - Support vector machines
KW - Textual complexity
UR - http://www.scopus.com/inward/record.url?scp=84949009785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949009785&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19773-9_10
DO - 10.1007/978-3-319-19773-9_10
M3 - Conference contribution
AN - SCOPUS:84949009785
SN - 9783319197722
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 95
EP - 104
BT - Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
A2 - Conati, Cristina
A2 - Heffernan, Neil
A2 - Mitrovic, Antonija
A2 - Felisa Verdejo, M.
PB - Springer Verlag
T2 - 17th International Conference on Artificial Intelligence in Education, AIED 2015
Y2 - 22 June 2015 through 26 June 2015
ER -