Publication Date
| In 2025 | 6 |
| Since 2024 | 7 |
Descriptor
Source
| Journal of Educational… | 7 |
Author
| Albert D. Ritzhaupt | 1 |
| Bo Pei | 1 |
| Chengliang Wang | 1 |
| Cuilan Qiao | 1 |
| Eunsung Park | 1 |
| Fanbo Li | 1 |
| Fenglin Wu | 1 |
| Florence Martin | 1 |
| Haoming Wang | 1 |
| Hongfeng Zhang | 1 |
| Jongpil Cheon | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 7 |
| Reports - Research | 7 |
| Information Analyses | 1 |
Education Level
| Higher Education | 3 |
| Postsecondary Education | 3 |
| Secondary Education | 3 |
| High Schools | 2 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
Audience
Location
| China | 4 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkHongfeng Zhang; Fanbo Li; Xiaolong Chen – Journal of Educational Computing Research, 2025
This study addresses the gap in understanding graduate students' sustained engagement behavior (SEB) with generative artificial intelligence (GAI) by integrating the Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT), and Theory of Reasoned Action (TRA) into a comprehensive embedding model. It introduces the Technology…
Descriptors: Graduate Students, Artificial Intelligence, Learner Engagement, Foreign Countries
Eunsung Park; Jongpil Cheon – Journal of Educational Computing Research, 2025
Debugging is essential for identifying and rectifying errors in programming, yet time constraints and students' trivialization of errors often hinder progress. This study examines differences in debugging challenges and strategies among students with varying computational thinking (CT) competencies using weekly coding journals from an online…
Descriptors: Undergraduate Students, Programming, Computer Software, Troubleshooting
Lin Zhang; Qiang Jiang; Weiyan Xiong; Wei Zhao – Journal of Educational Computing Research, 2025
This study seeks to deepen the understanding of the direct and indirect effects of human-computer dialogic interaction programming activities, facilitated by ChatGPT, on student engagement. Data were collected from 109 Chinese high school students who engaged in programming tasks using either ChatGPT-driven dialogic interaction or traditional pair…
Descriptors: Artificial Intelligence, Computer Software, Computer Science Education, Programming
Xiaoman Wang; Rui Huang; Max Sommer; Bo Pei; Poorya Shidfar; Muhammad Shahroze Rehman; Albert D. Ritzhaupt; Florence Martin – Journal of Educational Computing Research, 2024
The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45…
Descriptors: Meta Analysis, Outcomes of Education, Artificial Intelligence, Learning Management Systems
Chengliang Wang; Xiaojiao Chen; Yifei Li; Pengju Wang; Haoming Wang; Yuanyuan Li – Journal of Educational Computing Research, 2025
This study explored the impact of MetaClassroom, a virtual immersive programming learning environment designed based on the three-dimensional learning progression (3DLP) concept, on students' multidimensional development. Utilizing a quasi-experimental research design, this study compared students' programming learning achievements (PLA),…
Descriptors: Programming, Computer Science Education, Metacognition, Computer Simulation
Qing Guo; Junwen Zhen; Fenglin Wu; Yanting He; Cuilan Qiao – Journal of Educational Computing Research, 2025
The rapid development of large language models (LLMs) presented opportunities for the transformation of science and STEM education. Research on LLMs was in the exploratory phase, characterized by discussions and observations rather than empirical investigations. This study presented a framework for incorporating LLMs into Science and Engineering…
Descriptors: STEM Education, Computational Linguistics, Teaching Methods, Educational Change
Lijuan Feng – Journal of Educational Computing Research, 2025
This study investigates the impact of AI-assisted language learning (AIAL) strategies on cognitive load and learning outcomes in the context of language acquisition. Specifically, the study explores three distinct AIAL strategies: personalized feedback and adaptive learning, interactive exercises with speech recognition, and intelligent tutoring…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Second Language Learning, Second Language Instruction
