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Fatma Merve Mustafaoglu; Fatma Alkan – Science Education International, 2025
Recycling waste is essential to mitigate environmental damage caused by human activity. Environmentally responsible behaviors, shaped during early ages, are closely linked to environmental attitudes, as demonstrated by prior research. This study aims to predict middle school students' recycling behaviors using machine learning algorithms. A…
Descriptors: Middle School Students, Recycling, Student Behavior, Artificial Intelligence
Jian-hui Wu – Journal of Educational Computing Research, 2025
The objective of this research is to investigate how AI-improved dynamic physical education materials impact middle school education in physical settings. Utilizing a randomized controlled crossover approach, a 16-week study involved 120 students aged 12 to 18 to evaluate the impact of AI-enhanced physical education courses against traditional…
Descriptors: Artificial Intelligence, Physical Education, Instructional Materials, Middle School Students
Yaohua Huang; Chengbo Zhang – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study combines link grammar (LG) detector with N-grammar model to analyze and evaluate grammar in compositions. And then the composition level is judged through information entropy. Finally, the composition score is calculated based on the overall composition level and grammar weight. The experimental results show that the combined weight of…
Descriptors: Grammar, Student Evaluation, Artificial Intelligence, Writing (Composition)
Asselman, Amal; Khaldi, Mohamed; Aammou, Souhaib – Interactive Learning Environments, 2023
Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by…
Descriptors: Algorithms, Artificial Intelligence, Factor Analysis, Student Behavior
Levy, Ben; Hershkovitz, Arnon; Tabach, Michal; Cohen, Anat; Segal, Avi; Gal, Kobi – IEEE Transactions on Learning Technologies, 2023
This report describes a randomized controlled study that compared the personalization of educational content based on neural networks to personalization by human experts. The study was conducted in a graphically rich online learning environment for elementary school mathematics, in which N = 135 fourth- and sixth-grade students learn via…
Descriptors: Online Courses, Elementary School Students, Grade 4, Grade 6
Zhai, Xiaoming; Haudek, Kevin C.; Ma, Wenchao – Research in Science Education, 2023
In this study, we developed machine learning algorithms to automatically score students' written arguments and then applied the cognitive diagnostic modeling (CDM) approach to examine students' cognitive patterns of scientific argumentation. We abstracted three types of skills (i.e., attributes) critical for successful argumentation practice:…
Descriptors: Persuasive Discourse, Artificial Intelligence, Cognitive Measurement, Diagnostic Tests
Jessica Adams-Grigorieff – ProQuest LLC, 2023
With recent advances in generative artificial intelligence, it may come as no surprise that AI influences our society in profound ways. This dissertation project examines how educational and social media platforms and their algorithms shape educational practices and the identity, digital literacies and views of middle school youth participants.…
Descriptors: Digital Literacy, Critical Literacy, Algorithms, Artificial Intelligence
Amy Adair; Ellie Segan; Janice Gobert; Michael Sao Pedro – Grantee Submission, 2023
Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS). However, students often struggle with these two intersecting practices, particularly when developing mathematical models about scientific phenomena. Formative…
Descriptors: Artificial Intelligence, Mathematical Models, Science Process Skills, Inquiry
Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students

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