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Herfort, Jonas Dreyøe; Tamborg, Andreas Lindenskov; Meier, Florian; Allsopp, Benjamin Brink; Misfeldt, Morten – Educational Studies in Mathematics, 2023
Mathematics education is like many scientific disciplines witnessing an increase in scientific output. Examining and reviewing every paper in an area in detail are time-consuming, making comprehensive reviews a challenging task. Unsupervised machine learning algorithms like topic models have become increasingly popular in recent years. Their…
Descriptors: Mathematics Education, Technology Uses in Education, Artificial Intelligence, Algorithms
Landers, Richard N.; Auer, Elena M.; Mersy, Gabriel; Marin, Sebastian; Blaik, Jason – International Journal of Testing, 2022
Assessment trace data, such as mouse positions and their timing, offer interesting and provocative reflections of individual differences yet are currently underutilized by testing professionals. In this article, we present a 10-step procedure to maximize the probability that a trace data modeling project will be successful: (1) grounding the…
Descriptors: Artificial Intelligence, Data Collection, Psychometrics, Data Science
Yim Register – ProQuest LLC, 2024
The field of Data Science has seen rapid growth over the past two decades, with a high demand for people with skills in data analytics, programming, statistics, and ability to visualize, predict from, and otherwise make sense of data. Alongside the rise of various artificial intelligence (AI) and machine learning (ML) applications, we have also…
Descriptors: Artificial Intelligence, Ethics, Algorithms, Data Science
Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
Mike, Koby; Hazzan, Orit – IEEE Transactions on Education, 2023
Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect…
Descriptors: Engineering Education, Computer Science Education, Data Science, Introductory Courses
Susie Gronseth; Amani Itani; Kathryn Seastrand; Bettina Beech; Marino Bruce; Thamar Solorio; Ioannis Kakadiaris – Journal of Interactive Learning Research, 2025
This study examines the design, implementation, and evaluation of a Digital Educational Escape Room (DEER) titled "Escape from the Doctor's Office," developed to enhance artificial intelligence/machine learning (AI/ML) literacy. Grounded in constructivist pedagogy and behaviorist principles, the DEER was designed using the ADDIE…
Descriptors: Educational Games, Artificial Intelligence, Technological Literacy, Teamwork
Ian Lowrie – ProQuest LLC, 2017
This dissertation focuses on elite efforts to restructure work and education in the Russian data sciences. Russia has long had a strong national program in theoretical mathematics, but has been substantially less successful at applying this expertise to develop modern computational science, infrastructure, and business. As the Russian extraction…
Descriptors: Artificial Intelligence, Electronic Learning, Technology Uses in Education, Data Science