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Dombrowski, Stefan C.; McGill, Ryan J.; Watkins, Marley W.; Canivez, Gary L.; Pritchard, Alison E.; Jacobson, Lisa A. – Contemporary School Psychology, 2022
The Wechsler Intelligence Scale for Children's (WISC) factorial\theoretical structure has undergone numerous substantive changes since it was first developed, and each of these changes has subsequently been questioned by assessment experts. Given remaining questions about the structure of the latest revision, the WISC-V, the present study used…
Descriptors: Children, Intelligence Tests, Factor Structure, Factor Analysis
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Ferrara, Steve; Qunbar, Saed – Journal of Educational Measurement, 2022
In this article, we argue that automated scoring engines should be transparent and construct relevant--that is, as much as is currently feasible. Many current automated scoring engines cannot achieve high degrees of scoring accuracy without allowing in some features that may not be easily explained and understood and may not be obviously and…
Descriptors: Artificial Intelligence, Scoring, Essays, Automation
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Cohausz, Lea – International Educational Data Mining Society, 2022
Despite calls to increase the focus on explainability and interpretability in EDM and, in particular, student success prediction, so that it becomes useful for personalized intervention systems, only few efforts have been undertaken in that direction so far. In this paper, we argue that this is mainly due to the limitations of current Explainable…
Descriptors: Success, Prediction, Social Sciences, Artificial Intelligence
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Nguyen, Andy; Ngo, Ha Ngan; Hong, Yvonne; Dang, Belle; Nguyen, Bich-Phuong Thi – Education and Information Technologies, 2023
The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students' learning and enhance learning performance and experience.…
Descriptors: Ethics, Artificial Intelligence, Educational Policy, Privacy
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Liang, Zibo; Mu, Lan; Chen, Jie; Xie, Qing – Education and Information Technologies, 2023
In recent years, online learning methods have gradually been accepted by more and more people. A large number of online teaching courses and other resources (MOOCs) have also followed. To attract students' interest in learning, many scholars have built recommendation systems for MOOCs. However, students need a variety of different learning…
Descriptors: MOOCs, Artificial Intelligence, Graphs, Educational Resources
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Nwosu, Kingsley Chinaza; Wahl, Williem Petrus; Anyanwu, Adeline Nne; Ezenwosu, Ngozi Elizabeth; Okwuduba, Emmanuel Nkemakolam – Journal of Research in Special Educational Needs, 2023
Our study determined the impact of emotional intelligence (EI) on teachers' attitudes, concerns and sentiments about inclusive education while controlling for teachers' professional-related factors. This is predicated on the increasing influence of EI on teacher effectiveness. The sample size consisted of 508 regular classroom teachers. Using…
Descriptors: Teacher Characteristics, Emotional Intelligence, Predictor Variables, Teacher Attitudes
Breit, Moritz; Preuß, Julian; Scherrer, Vsevolod; Moors, Tobias; Preckel, Franzis – Gifted Child Quarterly, 2023
The threshold hypothesis and the necessary-but-not-sufficient hypothesis represent popular views on the relationship between intelligence and creativity. However, most studies investigating these hypotheses used suboptimal or even inappropriate statistical methods, calling into question the robustness of the available evidence. The ability…
Descriptors: Creativity, Intelligence, Secondary School Students, Foreign Countries
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Belzak, William C. M. – Educational Measurement: Issues and Practice, 2023
Test developers and psychometricians have historically examined measurement bias and differential item functioning (DIF) across a single categorical variable (e.g., gender), independently of other variables (e.g., race, age, etc.). This is problematic when more complex forms of measurement bias may adversely affect test responses and, ultimately,…
Descriptors: Test Bias, High Stakes Tests, Artificial Intelligence, Test Items
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Kumar, Rahul – International Journal for Educational Integrity, 2023
This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers--including discretion, convenience, pedagogical merits of consistent feedback for…
Descriptors: College Faculty, Artificial Intelligence, Grading, Research Papers (Students)
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Ingrisone, Soo Jeong; Ingrisone, James N. – Educational Measurement: Issues and Practice, 2023
There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering…
Descriptors: Identification, Cooperation, Computer Assisted Testing, Artificial Intelligence
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Matthews, Benjamin; Shannon, Barrie; Roxburgh, Mark – International Journal of Art & Design Education, 2023
Digital automation is on the rise in a diverse range of industries. The technologies employed here often make use of artificial intelligence (AI) and its common form, machine learning (ML) to augment or replace the work completed by human agents. The recent emergence of a variety of design automation platforms inspired the authors to undertake a…
Descriptors: Artificial Intelligence, Automation, Design, Electronic Learning
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Kubsch, Marcus; Krist, Christina; Rosenberg, Joshua M. – Journal of Research in Science Teaching, 2023
Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has…
Descriptors: Science Education, Educational Research, Artificial Intelligence, Models
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Albornoz-De Luise, Romina Soledad; Arevalillo-Herraez, Miguel; Arnau, David – IEEE Transactions on Learning Technologies, 2023
In this article, we analyze the potential of conversational frameworks to support the adaptation of existing tutoring systems to a natural language form of interaction. We have based our research on a pilot study, in which the open-source machine learning framework Rasa has been used to build a conversational agent that interacts with an existing…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Artificial Intelligence, Models
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Chen, Yawen; Zhai, Linbo – Education and Information Technologies, 2023
Accompanied with the development of storage and processing capacity of modern technology, educational data increases sharply. It is difficult for educational researchers to derive useful information from much educational data. Therefore, educational data mining techniques are important for the development of modern education field. Recently,…
Descriptors: Academic Achievement, Artificial Intelligence, Data Use, Information Retrieval
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Bellaiche, Lucas; Shahi, Rohin; Turpin, Martin Harry; Ragnhildstveit, Anya; Sprockett, Shawn; Barr, Nathaniel; Christensen, Alexander; Seli, Paul – Cognitive Research: Principles and Implications, 2023
With the recent proliferation of advanced artificial intelligence (AI) models capable of mimicking human artworks, AI creations might soon replace products of human creativity, although skeptics argue that this outcome is unlikely. One possible reason this may be unlikely is that, independent of the physical properties of art, we place great value…
Descriptors: Artificial Intelligence, Art Products, Creativity, Preferences
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