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Chen, Su; Fang, Ying; Shi, Genghu; Sabatini, John; Greenberg, Daphne; Frijters, Jan; Graesser, Arthur C. – Grantee Submission, 2021
This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Attention, Adult Literacy
Tsiola, Anna – ProQuest LLC, 2021
Naturalistic language learning is contextually grounded. When people learn their first (L1) and often their second (L2) language, they do so in various contexts. In this dissertation I examine the effect of various contexts on language development. Part 1 describes the effects of textual, linguistic context in reading. I employed an eye-tracking…
Descriptors: Natural Language Processing, Second Language Learning, Language Processing, Language Acquisition
Stephanie Link; Robert Redmon; Yaser Shamsi; Martin Hagan – CALICO Journal, 2024
Artificial intelligence (AI) for supporting second language (L2) writing processes and practices has garnered increasing interest in recent years, establishing AI-mediated L2 writing as a new norm for many multilingual classrooms. As such, the emergence of AI-mediated technologies has challenged L2 writing instructors and their philosophies…
Descriptors: English for Academic Purposes, Teaching Methods, Second Language Learning, Second Language Instruction
Rybinski, Krzysztof; Kopciuszewska, Elzbieta – Assessment & Evaluation in Higher Education, 2021
This article presents the first-ever big data study of the student evaluation of teaching (SET) using artificial intelligence (AI). We train natural language processing (NLP) models on 1.6 million student evaluations from the US and the UK. We address two research questions: (1) are these models able to predict student ratings from the student…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Evaluation of Teacher Performance, Natural Language Processing
Bosch, Nigel; Crues, R. Wes; Shaik, Najmuddin; Paquette, Luc – Grantee Submission, 2020
Online courses often include discussion forums, which provide a rich source of data to better understand and improve students' learning experiences. However, forum messages frequently contain private information that prevents researchers from analyzing these data. We present a method for discovering and redacting private information including…
Descriptors: Privacy, Discussion Groups, Asynchronous Communication, Methods
Silva, Valtemir A.; Bittencourt, Ig Ibert; Maldonado, Jose C. – IEEE Transactions on Learning Technologies, 2019
Question classification is a key point in many applications, such as Question Answering (QA, e.g., Yahoo! Answers), Information Retrieval (IR, e.g., Google search engine), and E-learning systems (e.g., Bloom's tax. classifiers). This paper aims to carry out a systematic review of the literature on automatic question classifiers and the technology…
Descriptors: Questioning Techniques, Classification, Man Machine Systems, Information Retrieval
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Educational Data Mining Society, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature
Mihai Dascalu; Scott A. Crossley; Danielle S. McNamara; Philippe Dessus; Stefan Trausan-Matu – Grantee Submission, 2018
A critical task for tutors is to provide learners with suitable reading materials in terms of difficulty. The challenge of this endeavor is increased by students' individual variability and the multiple levels in which complexity can vary, thus arguing for the necessity of automated systems to support teachers. This chapter describes…
Descriptors: Reading Materials, Difficulty Level, Natural Language Processing, Artificial Intelligence
Alexopoulou, Theodora; Michel, Marije; Murakami, Akira; Meurers, Detmar – Language Learning, 2017
Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: How does the prompt and input of a…
Descriptors: Linguistics, Accuracy, Natural Language Processing, Linguistic Performance
Crossley, Scott; Kyle, Kristopher; Davenport, Jodi; McNamara, Danielle S. – International Educational Data Mining Society, 2016
This study introduces the Constructed Response Analysis Tool (CRAT), a freely available tool to automatically assess student responses in online tutoring systems. The study tests CRAT on a dataset of chemistry responses collected in the ChemVLab+. The findings indicate that CRAT can differentiate and classify student responses based on semantic…
Descriptors: Intelligent Tutoring Systems, Chemistry, Natural Language Processing, High School Students
Allen, Laura K.; Mills, Caitlin; Perret, Cecile; McNamara, Danielle S. – Grantee Submission, 2019
This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146)…
Descriptors: Language Processing, Science Instruction, Computational Linguistics, Teaching Methods
Huang, Tao; Liang, Mengyi; Yang, Huali; Li, Zhi; Yu, Tao; Hu, Shengze – International Educational Data Mining Society, 2021
Influenced by COVID-19, online learning has become one of the most important forms of education in the world. In the era of intelligent education, knowledge tracing (KT) can provide excellent technical support for individualized teaching. For online learning, we come up with a new knowledge tracing method that integrates mathematical exercise…
Descriptors: Mathematics Instruction, Teaching Methods, Online Courses, Distance Education
Pareja-Lora, Antonio – Research-publishing.net, 2016
For the new approaches to language e-learning (e.g. language blended learning, language autonomous learning or mobile-assisted language learning) to succeed, some automatic functions for error correction (for instance, in exercises) will have to be included in the long run in the corresponding environments and/or applications. A possible way to…
Descriptors: Electronic Learning, Automation, Error Correction, Natural Language Processing
Meunier, Fanny, Ed.; Van de Vyver, Julie, Ed.; Bradley, Linda, Ed.; Thouësny, Sylvie, Ed. – Research-publishing.net, 2019
The 27th European Association of Computer-Assisted Language Learning (EUROCALL) conference was hosted by UCLouvain in Louvain-la-Neuve (Belgium), in collaboration with KU Leuven, from the 28th to the 31st of August 2019. The theme selected for the 2019 EuroCALL conference held in Louvain-la-Neuve was "CALL and complexity." As languages…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Teaching Methods

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