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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
Nicolas Pope; Juho Kahila; Henriikka Vartiainen; Matti Tedre – IEEE Transactions on Learning Technologies, 2025
The rapid advancement of artificial intelligence and its increasing societal impacts have turned many computing educators' focus toward early education in machine learning (ML). Limited options for educational tools for teaching novice learners about the mechanisms of ML and data-driven systems presents a recognized challenge in K-12 computing…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Computer Science Education, Grade 4
Jiawei Xiong; George Engelhard; Allan S. Cohen – Measurement: Interdisciplinary Research and Perspectives, 2025
It is common to find mixed-format data results from the use of both multiple-choice (MC) and constructed-response (CR) questions on assessments. Dealing with these mixed response types involves understanding what the assessment is measuring, and the use of suitable measurement models to estimate latent abilities. Past research in educational…
Descriptors: Responses, Test Items, Test Format, Grade 8
Kim, Keunjae; Kwon, Kyungbin; Ottenbreit-Leftwich, Anne; Bae, Haesol; Glazewski, Krista – Education and Information Technologies, 2023
This study aims to explore the middle schoolers' common naive conceptions of AI and the evolution of these conceptions during an AI summer camp. Data were collected from 14 middle school students (12 boys and 2 girls) from video observations and learning artifacts. The findings revealed 6 naive conceptions about AI concepts: (1) AI was the same as…
Descriptors: Middle School Students, Misconceptions, Artificial Intelligence, Summer Programs
Ünal Çakiroglu; Volkan Selçuk – Education and Information Technologies, 2025
In recent years, when computational thinking (CT) has become increasingly important, utilizing machine learning (ML) techniques provides a revolutionary method for comprehending and improving cognitive skills for young students. However, few studies deepen the process of learning ML and CT. This exploratory study aims to investigate the impact of…
Descriptors: Thinking Skills, Computation, Grade 5, Secondary School Students
Man Huang – Education and Information Technologies, 2025
As educational technology advances, the role of artificial intelligence (AI) in enhancing language education becomes increasingly prominent. However, there is a scarcity of empirical research assessing how AI integration influences student engagement and contributes to the language learning performance. This mixed-methods study seeks to fill the…
Descriptors: Foreign Countries, Middle School Students, Artificial Intelligence, Learner Engagement
Ayse Alkan; Ezgi Pelin Yildiz – International Journal of Research in Education and Science, 2024
The main goal of this study is to reveal special talented primary school students' perceptions of artificial intelligence, one of the popular concepts of recent times, through metaphors. In this study, the phenomenological design, which is within the scope of qualitative research, was used. In this study, Türkiye Science and Art Center included…
Descriptors: Foreign Countries, Gifted, Elementary School Students, Middle School Students
Yong Zhao; Ruojun Zhong – ECNU Review of Education, 2025
Purpose: The purpose of this article is to analyze educational changes, in particular transformational changes, and suggest a new approach to shift the paradigm in education using an ecological conceptual framework. Design/Approach/Methods: An ecological analysis of two key factors in education: prescribed curriculum and student autonomy.…
Descriptors: Educational Change, Holistic Approach, Personal Autonomy, Curriculum
Kimin Chung; Soohwan Kim; Yeonju Jang; Seongyune Choi; Hyeoncheol Kim – Education and Information Technologies, 2025
As artificial intelligence(AI) is utilised throughout society, the need to improve AI literacy as an essential competency, not only for specific experts but also for general citizens, is increasing. Therefore, several studies are being conducted on AI education, and attempts are being made to introduce it into the regular education curriculum.…
Descriptors: Artificial Intelligence, Technological Literacy, Diagnostic Tests, Elementary School Students
Kason Ka Ching Cheung; Jack Pun; Wangyin Kenneth-Li; Jiayi Mai – Journal of Science Education and Technology, 2025
As students read scientific texts created in generative artificial intelligence (GenAI) tools, they need to draw on their epistemic knowledge of GenAI as well as that of science. However, only a few research discussed multimodality as a methodological approach in characterising students' ideas of GenAI-science epistemic reading. This study…
Descriptors: Epistemology, Reading Processes, Concept Formation, Science Education
Conrad Borchers; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2025
Setting practice goals, which helps students regulate their effort toward achieving engagement and mastery, can enhance the benefits of active learning. However, traditional goal-setting approaches, such as homework contingency contracts, often lack frequent feedback and require substantial human intervention. The presented research investigates…
Descriptors: Goal Orientation, Active Learning, Rewards, Academic Achievement
Lu Cai – International Journal of Technology and Design Education, 2024
Based on Folk theory, Media Equation, and AI literacy research, the study constructed an interview outline and selected 72 students in 4th and 5th grade in three primary schools located in the Minhang and Putuo districts of Shanghai (two in the Minhang district and one in the Putuo district) as the study participants for focus group interviews.…
Descriptors: Foreign Countries, Grade 4, Grade 5, Artificial Intelligence
Yannik Fleischer; Susanne Podworny; Rolf Biehler – Statistics Education Research Journal, 2024
This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test…
Descriptors: Decision Making, Data Use, Cognitive Processes, Artificial Intelligence
Karumbaiah, Shamya; Zhang, Jiayi; Baker, Ryan S.; Scruggs, Richard; Cade, Whitney; Clements, Margaret; Lin, Shuqiong – International Educational Data Mining Society, 2022
Considerable amount of research in educational data mining has focused on developing efficient algorithms for Knowledge Tracing (KT). However, in practice, many real-world learning systems used at scale struggle to implement KT capabilities, especially if they weren't originally designed for it. One key challenge is to accurately label existing…
Descriptors: Artificial Intelligence, Middle School Students, Models, Concept Mapping
Helen Zhang; Anthony Perry; Irene Lee – International Journal of Artificial Intelligence in Education, 2025
The rapid expansion of Artificial Intelligence (AI) in our society makes it urgent and necessary to develop young students' AI literacy so that they can become informed citizens and critical consumers of AI technology. Over the past decade many efforts have focused on developing curricular materials that make AI concepts accessible and engaging to…
Descriptors: Test Construction, Test Validity, Measures (Individuals), Artificial Intelligence