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Dadi Ramesh; Suresh Kumar Sanampudi – European Journal of Education, 2024
Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…
Descriptors: Scoring, Essays, Writing Evaluation, Memory
Dragos Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2023
Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two…
Descriptors: Reading Comprehension, Models, Concept Mapping, Graphs
Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Memory, Inferences, Syntax
Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
Jones, Michael N. – Grantee Submission, 2018
Abstraction is a core principle of Distributional Semantic Models (DSMs) that learn semantic representations for words by applying dimensional reduction to statistical redundancies in language. Although the posited learning mechanisms vary widely, virtually all DSMs are prototype models in that they create a single abstract representation of a…
Descriptors: Abstract Reasoning, Semantics, Memory, Learning Processes
Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer – First Language, 2020
In 'Against Stored Abstractions,' Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious -- why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more -- and implies that his…
Descriptors: Language Processing, Language Acquisition, Computational Linguistics, Linguistic Theory
Luka, Barbara J.; Choi, Heidi – Journal of Memory and Language, 2012
Three experiments examine whether a naturalistic reading task can induce long-lasting changes of syntactic patterns in memory. Judgment of grammatical acceptability is used as an indirect test of memory for sentences that are identical or only syntactically similar to those read earlier. In previous research (Luka & Barsalou, 2005) both sorts of…
Descriptors: Priming, Comprehension, Sentences, Grammar
Friesen, Norm – Mind, Culture, and Activity, 2009
As an alternative to dominant cognitive-constructivist approaches to educational technology, this article makes the case for what has been termed a discursive, or postcognitive, psychological research paradigm. It does so by adapting discursive psychological analyses of conversational activity to the study of educational technology use. It applies…
Descriptors: Constructivism (Learning), Psychological Studies, Educational Technology, Psychology