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Marshall, Ruth; Pardo, Abelardo; Smith, David; Watson, Tony – British Journal of Educational Technology, 2022
For the developers of next-generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology-mediated learning to gain insights…
Descriptors: Educational Technology, Learning Analytics, Ethics, Privacy
Muller, Ashley Elizabeth; Ames, Heather Melanie R.; Jardim, Patricia Sofia Jacobsen; Rose, Christopher James – Research Synthesis Methods, 2022
Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful…
Descriptors: Artificial Intelligence, Man Machine Systems, Automation, Literature Reviews
Xu, Weiqi; Ouyang, Fan – Education and Information Technologies, 2022
Artificial Intelligence in Education (AIEd) is an emerging interdisciplinary field that applies artificial intelligence technologies to transform instructional design and student learning. However, most research has investigated AIEd from the technological perspective, which cannot achieve a deep understand of the complex roles of AI in…
Descriptors: Artificial Intelligence, Technology Uses in Education, Instructional Design, Role
Schroeders, Ulrich; Schmidt, Christoph; Gnambs, Timo – Educational and Psychological Measurement, 2022
Careless responding is a bias in survey responses that disregards the actual item content, constituting a threat to the factor structure, reliability, and validity of psychological measurements. Different approaches have been proposed to detect aberrant responses such as probing questions that directly assess test-taking behavior (e.g., bogus…
Descriptors: Response Style (Tests), Surveys, Artificial Intelligence, Identification
Hsueh, Sung-Lin; Zhou, Bin; Chen, Yu-Lung; Yan, Min-Ren – International Journal of Technology and Design Education, 2022
Cultural and creative products are incredibly diverse that difficult to mass-produce and fail to meet market demands. The unique characteristics and the cultural and sales value of cultural and creative products must be highlighted through the application of multifunctional design. As a scientific approach, the Delphi method and fuzzy logic theory…
Descriptors: Cultural Influences, Design, Creativity, Semantics
Ambrose, Don – Gifted Education International, 2022
Theoretical and practical work in gifted education has been dominated by mechanistic precision in measurements designed to select students for gifted programs and guide them through their development. Too much faith in mechanistic precision can become a form of dogmatism that obscures very important, less-measurable dimensions of human ability.…
Descriptors: Gifted Education, Academically Gifted, Talent Identification, Definitions
Basnet, Ram B.; Johnson, Clayton; Doleck, Tenzin – Education and Information Technologies, 2022
The nature of teaching and learning has evolved over the years, especially as technology has evolved. Innovative application of educational analytics has gained momentum. Indeed, predictive analytics have become increasingly salient in education. Considering the prevalence of learner-system interaction data and the potential value of such data, it…
Descriptors: Prediction, Dropouts, Predictive Measurement, Data Collection
Collier, Zachary K.; Leite, Walter L. – Journal of Experimental Education, 2022
Artificial neural networks (NN) can help researchers estimate propensity scores for quasi-experimental estimation of treatment effects because they can automatically detect complex interactions involving many covariates. However, NN is difficult to implement due to the complexity of choosing an algorithm for various treatment levels and monitoring…
Descriptors: Artificial Intelligence, Mentors, Beginning Teachers, Teacher Persistence
Siren, Anni; Tzerpos, Vassilios – IEEE Transactions on Learning Technologies, 2022
Learning paths are curated sequences of resources organized in a way that a learner has all the prerequisite knowledge needed to achieve their learning goals. In this article, we systematically map the techniques and algorithms that are needed to create such learning paths automatically. We focus on open educational resources (OER), though a…
Descriptors: Open Educational Resources, Artificial Intelligence, Instructional Materials, Educational Technology
Hussain, Asif; Khan, Muzammil; Ullah, Kifayat – Education and Information Technologies, 2022
Educational institutions are creating a considerable amount of data regarding students, faculty and related organs. This data is an essential asset for academic institutions as it has valuable insights, knowledge and intelligence for the policymakers. Students are the fundamental entities and primary source of data creation in any educational…
Descriptors: Data Analysis, Artificial Intelligence, Prediction, Academic Achievement
Frisby, Joshua C. – ProQuest LLC, 2022
Higher education institutions adopt conversational agents, or chatbots, to perform and automate certain business functions. While chatbots exist to support Enrollment Services, Financial Aid, and other departments within an institution, Institutional Research lacks options. Institutional Research supports higher education institutions by providing…
Descriptors: Institutional Research, Artificial Intelligence, Higher Education, State Universities
Hilbert, Sven; Coors, Stefan; Kraus, Elisabeth; Bischl, Bernd; Lindl, Alfred; Frei, Mario; Wild, Johannes; Krauss, Stefan; Goretzko, David; Stachl, Clemens – Review of Education, 2021
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non-linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but…
Descriptors: Artificial Intelligence, Online Courses, Educational Research, Data Analysis
Nye, Benjamin D.; Core, Mark G.; Jaiswa, Shikhar; Ghosal, Aviroop; Auerbach, Daniel – International Educational Data Mining Society, 2021
Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify…
Descriptors: Learner Engagement, Profiles, Artificial Intelligence, Student Behavior
Kate Powell – Montessori Life: A Publication of the American Montessori Society, 2024
In a span of about three days in the spring of 2023, the author's Instagram feed became inundated with mentions of artificial intelligence (AI), including Chat GPT, text-to-image models, and much more. She would turn on the radio and hear about the controversy surrounding AI, or look at her cousin's social media posts about the injustices of her…
Descriptors: Artificial Intelligence, Teaching Methods, Elementary School Teachers, Montessori Schools
Nur Azlina Mohamed Mokmin; Regania Pasca Rassy – Education and Information Technologies, 2024
One of the most advanced reality technologies for education in recent years is augmented reality (AR). To create a fun learning atmosphere and to aid student learning, several subjects have begun incorporating modern technology into their teaching and learning procedures. In addition to being extensively tested and developed for typical students,…
Descriptors: Artificial Intelligence, Students with Disabilities, Physical Education, Accessibility (for Disabled)

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