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Zheng, Lanqin; Long, Miaolang; Chen, Bodong; Fan, Yunchao – International Journal of Educational Technology in Higher Education, 2023
Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated…
Descriptors: Learning Analytics, Computer Assisted Testing, Cooperative Learning, Graphs
Bünyami Kayali; Mehmet Yavuz; Sener Balat; Mücahit Çalisan – Australasian Journal of Educational Technology, 2023
The purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data…
Descriptors: Student Experience, Artificial Intelligence, Natural Language Processing, Electronic Learning
Thanh Pham; Binh Nguyen; Son Ha; Thanh Nguyen Ngoc – Australasian Journal of Educational Technology, 2023
This research explored the potential of artificial intelligence (AI)-assisted learning using ChatGPT in an engineering course at a university in South-east Asia. The study investigated the benefits and challenges that students may encounter when utilising ChatGPT-3.5 as a learning tool. This research developed an AI-assisted learning flow that…
Descriptors: Artificial Intelligence, Engineering Education, Universities, Foreign Countries
Barrett, Alex; Pack, Austin – International Journal of Educational Technology in Higher Education, 2023
Generative artificial intelligence (GenAI) can be used to author academic texts at a similar level to what humans are capable of, causing concern about its misuse in education. Addressing the role of GenAI in teaching and learning has become an urgent task. This study reports the results of a survey comparing educators' (n = 68) and university…
Descriptors: Artificial Intelligence, Writing (Composition), Writing Processes, Teacher Attitudes
Kornwipa Poonpon; Paiboon Manorom; Wirapong Chansanam – Contemporary Educational Technology, 2023
Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers' writing skills. This research addresses this gap…
Descriptors: Automation, Essays, Scoring, English (Second Language)
Dasgupta, Ishita; Guo, Demi; Gershman, Samuel J.; Goodman, Noah D. – Cognitive Science, 2020
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present…
Descriptors: Natural Language Processing, Man Machine Systems, Heuristics, Sentences
Cole, Brian S.; Lima-Walton, Elia; Brunnert, Kim; Vesey, Winona Burt; Raha, Kaushik – Journal of Applied Testing Technology, 2020
Automatic item generation can rapidly generate large volumes of exam items, but this creates challenges for assembly of exams which aim to include syntactically diverse items. First, we demonstrate a diminishing marginal syntactic return for automatic item generation using a saturation detection approach. This analysis can help users of automatic…
Descriptors: Artificial Intelligence, Automation, Test Construction, Test Items
Milat, Iness Nedji; Seridi, Hassina; Moudjari, Abdelkader – International Journal of Distance Education Technologies, 2020
Recently, discovering learner behaviour has taken more attention in the field of e-learning. It aims to gain useful insights into the learning process of students despite the absence of direct interaction with teachers. In fact, the only available source of information in such environments is the log file that represents all possible interactions…
Descriptors: Student Behavior, Behavior Patterns, Electronic Learning, Learning Analytics
Anglin, Kylie; Boguslav, Arielle; Hall, Todd – Grantee Submission, 2020
Text classification has allowed researchers to analyze natural language data at a previously impossible scale. However, a text classifier is only as valid as the the annotations on which it was trained. Further, the cost of training a classifier depends on annotators' ability to quickly and accurately apply the coding scheme to each text. Thus,…
Descriptors: Documentation, Natural Language Processing, Classification, Research Design
Kim, Ju-Ri – Eurasian Journal of Applied Linguistics, 2021
Background/Objectives: There is no attempt to investigate the relationships between dependency and markedness even though the syntactic roles in language are decided by dependency relations and markers. The main objective of this study was to understand markedness beyond syntactical tables and propose a syntax graph with various syntax structures…
Descriptors: Grammar, Correlation, Syntax, Classification
Sanosi, Abdulaziz; Abdalla, Mohamed – Australian Journal of Applied Linguistics, 2021
This study aimed to examine the potentials of the NLP approach in detecting discourse markers (DMs), namely okay, in transcribed spoken data. One hundred thirty-eight concordance lines were presented to human referees to judge the functions of okay in them as a DM or Non-DM. After that, the researchers used a Python script written according to the…
Descriptors: Natural Language Processing, Computational Linguistics, Programming Languages, Accuracy
Filik, Ruth; Ingram, Joanne; Moxey, Linda; Leuthold, Hartmut – Journal of Psycholinguistic Research, 2021
According to the Presupposition-Denial Account, complement set reference arises when focus is on the "shortfall" between the amount conveyed by a natural language quantifier and a larger, expected amount. Negative quantifiers imply a shortfall, through the denial of a presupposition, whereas positive quantifiers do not. An exception may…
Descriptors: Figurative Language, Linguistic Theory, Natural Language Processing, Form Classes (Languages)
Bosker, Hans Rutger; Badaya, Esperanza; Corley, Martin – Discourse Processes: A Multidisciplinary Journal, 2021
Speech in everyday conversations is riddled with discourse markers (DMs), such as "well," "you know," and "like." However, in many lab-based studies of speech comprehension, such DMs are typically absent from the carefully articulated and highly controlled speech stimuli. As such, little is known about how these DMs…
Descriptors: Discourse Analysis, Language Usage, Word Recognition, Eye Movements
Allen, Laura Kristen; Magliano, Joseph P.; McCarthy, Kathryn S.; Sonia, Allison N.; Creer, Sarah D.; McNamara, Danielle S. – Grantee Submission, 2021
The current study examined the extent to which the cohesion detected in readers' constructed responses to multiple documents was predictive of persuasive, source-based essay quality. Participants (N=95) completed multiple-documents reading tasks wherein they were prompted to think-aloud, self-explain, or evaluate the sources while reading a set of…
Descriptors: Reading Comprehension, Connected Discourse, Reader Response, Natural Language Processing
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, Reading Processes, Memory, Schemata (Cognition)