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Dragos-Georgian Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…
Descriptors: Computer Software, Artificial Intelligence, Learning Analytics, Natural Language Processing
Cioaca, Valentin Sergiu; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source "ReaderBench" framework, which can be retrofit to cover more than 50 languages. While considering the…
Descriptors: Documentation, Computer Software, Open Source Technology, Algorithms
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Madnani, Nitin; Burstein, Jill; Sabatini, John; Biggers, Kietha; Andreyev, Slava – Grantee Submission, 2016
Current education standards in the U.S. require school students to read and understand complex texts from different subject areas (e.g., social studies). However, such texts usually contain figurative language, complex phrases and sentences, as well as unfamiliar discourse relations. This may present an obstacle to students whose native language…
Descriptors: English Language Learners, Reading Instruction, Natural Language Processing, Learning Activities