TY - GEN
T1 - Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction
AU - Nicula, Bogdan
AU - Perret, Cecile A.
AU - Dascalu, Mihai
AU - McNamara, Danielle S.
N1 - Funding Information:
This research was partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689/“Lib2Life-Revitalizing Libraries and Cultural Heritage through Advanced Technologies” within PNCDI III, the Institute of Education Sciences (R305A180144, R305A180261 and R305A190063), and the Office of Naval Research (N00014-17-1-2300). The opinions expressed are those of the authors and do not represent views of the IES or ONR.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Theories of discourse argue that comprehension depends on the coherence of the learner’s mental representation. Our aim is to create a reliable automated representation to estimate readers’ level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: a) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner’s productions, and b) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students’ reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features.
AB - Theories of discourse argue that comprehension depends on the coherence of the learner’s mental representation. Our aim is to create a reliable automated representation to estimate readers’ level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: a) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner’s productions, and b) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students’ reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features.
KW - Cohesion Network Analysis
KW - Multi-document comprehension modeling
KW - Natural Language Processing
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U2 - 10.1007/978-3-030-52240-7_42
DO - 10.1007/978-3-030-52240-7_42
M3 - Conference contribution
AN - SCOPUS:85088569410
SN - 9783030522391
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 233
BT - Artificial Intelligence in Education - 21st International Conference, AIED 2020, Proceedings
A2 - Bittencourt, Ig Ibert
A2 - Cukurova, Mutlu
A2 - Luckin, Rose
A2 - Muldner, Kasia
A2 - Millán, Eva
PB - Springer
T2 - 21st International Conference on Artificial Intelligence in Education, AIED 2020
Y2 - 6 July 2020 through 10 July 2020
ER -