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Mengjiao Yin; Hengshan Cao; Zuhong Yu; Xianyu Pan – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…
Descriptors: Predictor Variables, Artificial Intelligence, Performance, Cluster Grouping
Yuxin Zhang – Journal of Information Technology Education: Research, 2025
Aim/Purpose: This study investigates the key factors influencing preschool teachers' sustained use of Artificial Intelligence-Generated Content (AIGC) technology in educational settings. While prior research has extensively examined initial adoption, little attention has been given to understanding the continuous intention of preschool teachers…
Descriptors: Preschool Teachers, Artificial Intelligence, Technology Integration, Technology Uses in Education
Weiqing Shi; Xin Jiang – Reading and Writing: An Interdisciplinary Journal, 2025
This study explores the effectiveness of machine learning and eye movement features in predicting Chinese reading proficiency. Unlike previous research, which focused on one or two specific levels of eye movement features, this study integrates passage-, sentence- and word-level eye movement features to predict reading proficiency. By analyzing…
Descriptors: Foreign Countries, Undergraduate Students, Predictor Variables, Reading Achievement
Schmucker, Robin; Wang, Jingbo; Hu, Shijia; Mitchell, Tom M. – Journal of Educational Data Mining, 2022
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…
Descriptors: Academic Achievement, Electronic Learning, Artificial Intelligence, Predictor Variables
Guangxiang Liu; Chaojun Ma – Innovation in Language Learning and Teaching, 2024
Purpose: This study aims to generate empirical insights into the extent to which ChatGPT, a highly capable AI chatbot building on OpenAI's GPT family, is perceived and leveraged by EFL learners beyond the classroom. Design/Methodology: This quantitative cross-sectional investigation draws upon the technology acceptance model (TAM) as developed by…
Descriptors: English (Second Language), Second Language Learning, Artificial Intelligence, Man Machine Systems
Miaomiao Liu; Yixun Li; Yongqiang Su; Hong Li – Scientific Studies of Reading, 2024
Purpose: This study sought to 1) identify linguistic features important for Chinese text complexity with a theory-based and systematic approach, and 2) address how feature sets and algorithms affect the performance of Chinese text complexity models. Method: Texts from Chinese language arts textbooks from Grades 1 to 6 (N = 1,478) in Mainland China…
Descriptors: Difficulty Level, Textbooks, Algorithms, Artificial Intelligence
Kai Wang; Ching-Sing Chai; Jyh-Chong Liang; Guoyuan Sang – Technology, Pedagogy and Education, 2024
As artificial intelligence (AI) advances rapidly, it has been incorporated into formal education to facilitate subject-based learning. Integration of AI technologies to support learning requires teachers to intentionally design AI-assisted learning. However, there have been a limited number of empirical studies investigating teachers' behavioural…
Descriptors: Foreign Countries, Artificial Intelligence, Educational Technology, Technology Uses in Education
Fang Huang; Dingyang Peng; Timothy Teo – European Journal of Education, 2025
Contextualised in the AI--supported English-speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on-campus vs. on-job) in influencing AI-supported English-speaking engagement. Data…
Descriptors: Learner Engagement, Second Language Learning, Second Language Instruction, English (Second Language)
Jiaozhi Liang; Fang Huang; Timothy Teo – International Journal of Computer-Assisted Language Learning and Teaching, 2024
Artificial intelligence (AI) is useful to English as a foreign language (EFL) learners, but there is a paucity of research on how they perceive AI. Contextualized in a Chinese university setting, this study investigated Chinese university EFL learners' perceptions of Grammarly in English writing. Based on an extended technology acceptance model…
Descriptors: English (Second Language), Second Language Instruction, Second Language Learning, Writing Processes
Yang, Juan; Thomas, Michael S. C.; Qi, Xiaofei; Liu, Xuan – Computer Assisted Language Learning, 2019
From a psycholinguistic perspective of view, there are many cognitive differences that matter to individuals' second-language acquisition (SLA). Although many computer-assisted tools have been developed to capture and narrow the differences among learners, the use of these strategies may be highly risky because changing the environments or the…
Descriptors: Foreign Countries, Cognitive Ability, Phonological Awareness, English Teachers