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Showing 1 to 15 of 17 results Save | Export
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Ayse Alkan – International Journal of Technology in Education and Science, 2024
Artificial intelligence (AI) based education represents a significant transformation in the field of education of our age. Artificial intelligence (AI) technology has great potential to enrich the learning experience of special needs students, provide support to teachers, and reduce inequalities in education. Artificial intelligence (AI)…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Students with Disabilities
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Nesra Yannier; Scott E. Hudson; Henry Chang; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2024
Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a…
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education
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Devika Venugopalan; Ziwen Yan; Conrad Borchers; Jionghao Lin; Vincent Aleven – Grantee Submission, 2025
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…
Descriptors: Homework, Computational Linguistics, Teaching Methods, Learning Analytics
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Yanping Pei; Adam Sales; Johann Gagnon-Bartsch – Grantee Submission, 2024
Randomized A/B tests within online learning platforms enable us to draw unbiased causal estimators. However, precise estimates of treatment effects can be challenging due to minimal participation, resulting in underpowered A/B tests. Recent advancements indicate that leveraging auxiliary information from detailed logs and employing design-based…
Descriptors: Randomized Controlled Trials, Learning Management Systems, Causal Models, Learning Analytics
Vincent Aleven; Jori Blankestijn; LuEttaMae Lawrence; Tomohiro Nagashima; Niels Taatgen – Grantee Submission, 2022
Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced…
Descriptors: Educational Technology, Artificial Intelligence, Problem Solving, Intelligent Tutoring Systems
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Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2022
Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains,…
Descriptors: Student Projects, Feedback (Response), Natural Language Processing, Guidelines
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Ig Ibert Bittencourt; Geiser Chalco; Jário Santos; Sheyla Fernandes; Jesana Silva; Naricla Batista; Claudio Hutz; Seiji Isotani – International Journal of Artificial Intelligence in Education, 2024
The unprecedented global movement of school education to find technological and intelligent solutions to keep the learning ecosystem working was not enough to recover the impacts of COVID-19, not only due to learning-related challenges but also due to the rise of negative emotions, such as frustration, anxiety, boredom, risk of burnout and the…
Descriptors: Artificial Intelligence, COVID-19, Pandemics, Computer Software
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Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
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Arnbjörnsdóttir, Birna, Ed.; Bédi, Branislav, Ed.; Bradley, Linda, Ed.; Friðriksdóttir, Kolbrún, Ed.; Garðarsdóttir, Hólmfríður, Ed.; Thouësny, Sylvie, Ed.; Whelpton, Matthew James, Ed. – Research-publishing.net, 2022
The 2022 EUROCALL conference was held in Reykjavik on 17-19 August 2022 as a fully online event hosted by the Vigdís Finnbogadóttir Institute for Foreign Languages, the University of Iceland, and the Árni Magnússon Institute for Icelandic Studies. The conference theme was "Intelligent CALL, granular systems and learner data." This theme…
Descriptors: Learning Analytics, Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Experience
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Barollet, Théo; Bouchez Tichadou, Florent; Rastello, Fabrice – International Educational Data Mining Society, 2021
In Intelligent Tutoring Systems (ITS), methods to choose the next exercise for a student are inspired from generic recommender systems, used, for instance, in online shopping or multimedia recommendation. As such, collaborative filtering, especially matrix factorization, is often included as a part of recommendation algorithms in ITS. One notable…
Descriptors: Intelligent Tutoring Systems, Prediction, Internet, Purchasing
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Yuan, Chia-Ching; Li, Cheng-Hsuan; Peng, Chin-Cheng – Interactive Learning Environments, 2023
Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization…
Descriptors: Artificial Intelligence, Man Machine Systems, Technology Uses in Education, Educational Technology
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Kim, Byungsoo; Yu, Hangyeol; Shin, Dongmin; Choi, Youngduck – International Educational Data Mining Society, 2021
The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which…
Descriptors: Academic Achievement, Intelligent Tutoring Systems, Prediction, Scores
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Roux, Lisa; Dagorret, Pantxika; Etcheverry, Patrick; Nodenot, Thierry; Marquesuzaa, Christophe; Lopisteguy, Philippe – International Association for Development of the Information Society, 2021
Distance computer-assisted learning is increasingly common, owing largely to the expansion and development of e-technology. Nevertheless, the available tools of the learning platforms have demonstrated their limits during the pandemic context, since many students, who were used to "face-to-face" education, got discouraged and dropped out…
Descriptors: Distance Education, Computer Software, Teacher Student Relationship, Supervision
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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
Steven Moore; John Stamper; Norman Bier; Mary Jean Blink – Grantee Submission, 2020
In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…
Descriptors: Learning Analytics, Educational Improvement, Classification, Learning Processes
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