Abstract
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.
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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.
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Dascalu, M., Paraschiv, I.C., McNamara, D.S., Trausan-Matu, S. (2018). Towards an Automated Model of Comprehension (AMoC). In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_33
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DOI: https://doi.org/10.1007/978-3-319-98572-5_33
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