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Karimah, Shofiyati Nur; Hasegawa, Shinobu – Smart Learning Environments, 2022
Recognizing learners' engagement during learning processes is important for providing personalized pedagogical support and preventing dropouts. As learning processes shift from traditional offline classrooms to distance learning, methods for automatically identifying engagement levels should be developed. This article aims to present a literature…
Descriptors: Learner Engagement, Automation, Electronic Learning, Literature Reviews
Camperos, July Andrea Gomez; Jaramillo, Haidee Yulady; Castrillón, Alexci Suárez – Journal of Language and Linguistic Studies, 2022
With the rapid growth of the technology era, the conventional teaching approach is not sufficient for students of the millennial generation. With this in mind, this article presents the justification for incorporating Kolb's experiential learning, based on laboratory practices with an environment supported by simulation software, combined with…
Descriptors: Experiential Learning, Problem Based Learning, Automation, Foreign Countries
Jessica Andrews-Todd; Jonathan Steinberg; Michael Flor; Carolyn M. Forsyth – Grantee Submission, 2022
Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals' CPS competency are necessary, as traditional assessment types such as multiple-choice items are not well suited for such a process-oriented…
Descriptors: Automation, Classification, Cooperative Learning, Problem Solving
Jessica Andrews-Todd; Jonathan Steinberg; Michael Flor; Carolyn M. Forsyth – Journal of Intelligence, 2022
Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals' CPS competency are necessary, as traditional assessment types such as multiple-choice items are not well suited for such a process-oriented…
Descriptors: Automation, Classification, Cooperative Learning, Problem Solving
Rianne Conijn; Emily Dux Speltz; Evgeny Chukharev-Hudilainen – Reading and Writing: An Interdisciplinary Journal, 2024
Revision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear…
Descriptors: Automation, Revision (Written Composition), Word Processing, Computers
Galit Agmon; Sameer Pradhan; Sharon Ash; Naomi Nevler; Mark Liberman; Murray Grossman; Sunghye Cho – Journal of Speech, Language, and Hearing Research, 2024
Purpose: Multiple methods have been suggested for quantifying syntactic complexity in speech. We compared eight automated syntactic complexity metrics to determine which best captured verified syntactic differences between old and young adults. Method: We used natural speech samples produced in a picture description task by younger (n = 76, ages…
Descriptors: Young Adults, Older Adults, Undergraduate Students, Caregivers
Lena Schmidt; Saleh Mohamed; Nick Meader; Jaume Bacardit; Dawn Craig – Research Synthesis Methods, 2024
The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or…
Descriptors: Automation, Natural Language Processing, Literature Reviews, Data Collection
Thuy Thi-Nhu Ngo; Howard Hao-Jan Chen; Kyle Kuo-Wei Lai – Interactive Learning Environments, 2024
The present study performs a three-level meta-analysis to investigate the overall effectiveness of automated writing evaluation (AWE) on EFL/ESL student writing performance. 24 primary studies representing 85 between-group effect sizes and 34 studies representing 178 within-group effect sizes found from 1993 to 2021 were separately meta-analyzed.…
Descriptors: Writing Evaluation, Automation, Computer Software, English (Second Language)
Lishan Zhang; Linyu Deng; Sixv Zhang; Ling Chen – IEEE Transactions on Learning Technologies, 2024
With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically…
Descriptors: Automation, Classification, Artificial Intelligence, Tutoring
Mohammad Rajiur Rahman; Raga Shalini Koka; Shishir K. Shah; Thamar Solorio; Jaspal Subhlok – Education and Information Technologies, 2024
Video is an increasingly important resource in higher education. A key limitation of lecture video is that it is fundamentally a sequential information stream. Quickly accessing the content aligned with specific learning objectives in a video recording of a classroom lecture is challenging. Recent research has enabled automatic reorganization of a…
Descriptors: Lecture Method, Video Technology, Navigation (Information Systems), Artificial Intelligence
Jessie S. Barrot – Education and Information Technologies, 2024
This bibliometric analysis attempts to map out the scientific literature on automated writing evaluation (AWE) systems for teaching, learning, and assessment. A total of 170 documents published between 2002 and 2021 in Social Sciences Citation Index journals were reviewed from four dimensions, namely size (productivity and citations), time…
Descriptors: Educational Trends, Automation, Computer Assisted Testing, Writing Tests
Jimmy Tobin; Phillip Nelson; Bob MacDonald; Rus Heywood; Richard Cave; Katie Seaver; Antoine Desjardins; Pan-Pan Jiang; Jordan R. Green – Journal of Speech, Language, and Hearing Research, 2024
Purpose: This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode--specific training on ASR accuracy. Method: Recordings of read and…
Descriptors: Foreign Countries, Speech Impairments, Computational Linguistics, Artificial Intelligence
Helen N. Levenson; Sara Amato; Ian Bogus; Fern E. Brody; Mary Miller; Jacob Nadal – College & Research Libraries, 2024
Shared print programs are helping their member libraries right-size their collections. As they do, there are concerns about the adverse impact of bibliographic inaccuracies. This paper studies bibliographic record inaccuracies and the resulting frequency of mismatches between an item owned and the record representing ownership. Through analysis of…
Descriptors: Libraries, Library Automation, Library Materials, Library Services
Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
Sarah C. Adcock – ProQuest LLC, 2024
Over the past twenty-five years, many academic libraries have shifted from print to digital collections. One consequence of this change has been the increased use of a library's website to access books, journals, and databases in a digital format. Thus, library space, once used to house print material, is now available for other purposes, creating…
Descriptors: Academic Libraries, Medical Libraries, Space Utilization, Library Automation

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