TY - GEN
T1 - Towards an Automated Model of Comprehension (AMoC)
AU - Dascalu, Mihai
AU - Paraschiv, Ionut Cristian
AU - McNamara, Danielle
AU - Trausan-Matu, Stefan
N1 - Funding Information:
Acknowledgment. The work presented in this paper was funded by the European Funds of Regional Development with the Operation Productivity Program 2014–2020 Priority Axe 1, Action 1.2.1 D-2015, “Innovative Technology Hub based on Semantic Models and High Performance Computing” Contract no. 6/1 09/2016.
Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Reading is a complex cognitive process wherein learners acquire new information and consolidate their knowledge. Readers create a mental representation for a given text by processing relevant words that, along with prior inferred concepts, become activated and establish meaningful associations. Our automated model of comprehension (AMoC) uses an automated approach for simulating the ways in which learners read and conceptualize by considering both text-based information consisting of syntactic dependencies, as well as inferred concepts from semantic models. AMoC makes use of cutting edge Natural Language Processing techniques, transcends beyond existing models, and represents a novel alternative for modeling how learners potentially conceptualize read information. This study presents side-by-side comparisons of the results generated by our model versus the ones generated by the Landscape model.
AB - Reading is a complex cognitive process wherein learners acquire new information and consolidate their knowledge. Readers create a mental representation for a given text by processing relevant words that, along with prior inferred concepts, become activated and establish meaningful associations. Our automated model of comprehension (AMoC) uses an automated approach for simulating the ways in which learners read and conceptualize by considering both text-based information consisting of syntactic dependencies, as well as inferred concepts from semantic models. AMoC makes use of cutting edge Natural Language Processing techniques, transcends beyond existing models, and represents a novel alternative for modeling how learners potentially conceptualize read information. This study presents side-by-side comparisons of the results generated by our model versus the ones generated by the Landscape model.
KW - Comprehension modeling
KW - Landscape Model
KW - Natural Language Processing
KW - Semantic models
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U2 - 10.1007/978-3-319-98572-5_33
DO - 10.1007/978-3-319-98572-5_33
M3 - Conference contribution
AN - SCOPUS:85053213015
SN - 9783319985718
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 427
EP - 436
BT - Lifelong Technology-Enhanced Learning - 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Proceedings
A2 - Elferink, Raymond
A2 - Drachsler, Hendrik
A2 - Pammer-Schindler, Viktoria
A2 - Perez-Sanagustin, Mar
A2 - Scheffel, Maren
PB - Springer Verlag
T2 - 13th European Conference on Technology Enhanced Learning, EC-TEL 2018
Y2 - 3 September 2018 through 6 September 2018
ER -