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Ragheb Al-Ghezi; Katja Voskoboinik; Yaroslav Getman; Anna Von Zansen; Heini Kallio; Mikko Kurimo; Ari Huhta; Raili Hildén – Language Assessment Quarterly, 2023
The development of automated systems for evaluating spontaneous speech is desirable for L2 learning, as it can be used as a facilitating tool for self-regulated learning, language proficiency assessment, and teacher training programs. However, languages with fewer learners face challenges due to the scarcity of training data. Recent advancements…
Descriptors: Speech Tests, Automation, Artificial Intelligence, Finno Ugric Languages
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Rodrigues, Luiz; Toda, Armando M.; Oliveira, Wilk; Palomino, Paula Toledo; Vassileva, Julita; Isotani, Seiji – IEEE Transactions on Learning Technologies, 2022
Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several…
Descriptors: Automation, Game Based Learning, Individualized Instruction, Attitudes
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Yousef, Ahmed M.; Deliyski, Dimitar D.; Zacharias, Stephanie R. C.; de Alarcon, Alessandro; Orlikoff, Robert F.; Naghibolhosseini, Maryam – Journal of Speech, Language, and Hearing Research, 2022
Purpose: Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details. Analysis of vocal fold vibration using HSV requires accurate segmentation of the vocal fold edges.…
Descriptors: Voice Disorders, Speech, Video Technology, Equipment
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Ngoc My Bui; Jessie S. Barrot – Education and Information Technologies, 2025
With the generative artificial intelligence (AI) tool's remarkable capabilities in understanding and generating meaningful content, intriguing questions have been raised about its potential as an automated essay scoring (AES) system. One such tool is ChatGPT, which is capable of scoring any written work based on predefined criteria. However,…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Automation
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Rebecca L. Pharmer; Christopher D. Wickens; Benjamin A. Clegg – Cognitive Research: Principles and Implications, 2025
In two experiments, we examine how features of an imperfect automated decision aid influence compliance with the aid in a simplified, simulated nautical collision avoidance task. Experiment 1 examined the impact of providing transparency in the pre-task instructions regarding which attributes of the task that the aid uses to provide its…
Descriptors: Accountability, Automation, Compliance (Psychology), Task Analysis
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Konstantinos Gavriil; Ioannis Giannikos – Education Policy Analysis Archives, 2025
This paper presents a model for automatically selecting and allocating secondary education teachers to schools while considering various factors such as the diversity of sections and lessons, school distances, teacher specializations, teaching workloads, and other constraints. This poses a complex challenge that educational authorities in…
Descriptors: Foreign Countries, Secondary School Teachers, Teacher Placement, Teacher Distribution
Sungbok Shin – ProQuest LLC, 2024
Data visualization is a powerful strategy for using graphics to represent data for effective communication and analysis. Unfortunately, creating effective data visualizations is a challenge for both novice and expert design users. The task often involves an iterative process of trial and error, which by its nature, is time-consuming. Designers…
Descriptors: Artificial Intelligence, Computer Simulation, Visualization, Feedback (Response)
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Blaženka Divjak; Barbi Svetec; Damir Horvat – Journal of Computer Assisted Learning, 2024
Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic…
Descriptors: Automation, Formative Evaluation, Test Validity, Test Reliability
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Hosnia M. M. Ahmed; Shaymaa E. Sorour – Education and Information Technologies, 2024
Evaluating the quality of university exam papers is crucial for universities seeking institutional and program accreditation. Currently, exam papers are assessed manually, a process that can be tedious, lengthy, and in some cases, inconsistent. This is often due to the focus on assessing only the formal specifications of exam papers. This study…
Descriptors: Higher Education, Artificial Intelligence, Writing Evaluation, Natural Language Processing
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Héctor J. Pijeira-Díaz; Sophia Braumann; Janneke van de Pol; Tamara van Gog; Anique B. H. Bruin – British Journal of Educational Technology, 2024
Advances in computational language models increasingly enable adaptive support for self-regulated learning (SRL) in digital learning environments (DLEs; eg, via automated feedback). However, the accuracy of those models is a common concern for educational stakeholders (eg, policymakers, researchers, teachers and learners themselves). We compared…
Descriptors: Computational Linguistics, Independent Study, Secondary School Students, Causal Models
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Bin Tan; Hao-Yue Jin; Maria Cutumisu – Computer Science Education, 2024
Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Student Evaluation
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Abdulkadir Kara; Eda Saka Simsek; Serkan Yildirim – Asian Journal of Distance Education, 2024
Evaluation is an essential component of the learning process when discerning learning situations. Assessing natural language responses, like short answers, takes time and effort. Artificial intelligence and natural language processing advancements have led to more studies on automatically grading short answers. In this review, we systematically…
Descriptors: Automation, Natural Language Processing, Artificial Intelligence, Grading
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Monica Tatasciore; Luke Strickland; Shayne Loft – Cognitive Research: Principles and Implications, 2024
Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence…
Descriptors: Automation, Access to Information, Information Technology, Bias
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Walter Gander – Informatics in Education, 2024
When the new programming language Pascal was developed in the 1970's, Walter Gander did not like it because because many features which he appreciated in prior programming languages were missing in Pascal. For example the block structure was gone, there were no dynamical arrays, no functions or procedures were allowed as parameters of a procedure,…
Descriptors: Computer Software, Programming Languages, Algorithms, Automation
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Ulrike Padó; Yunus Eryilmaz; Larissa Kirschner – International Journal of Artificial Intelligence in Education, 2024
Short-Answer Grading (SAG) is a time-consuming task for teachers that automated SAG models have long promised to make easier. However, there are three challenges for their broad-scale adoption: A technical challenge regarding the need for high-quality models, which is exacerbated for languages with fewer resources than English; a usability…
Descriptors: Grading, Automation, Test Format, Computer Assisted Testing
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