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Direct linkXiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Zheng, Lanqin; Niu, Jiayu; Zhong, Lu; Gyasi, Juliana Fosua – Interactive Learning Environments, 2023
Recently, artificial intelligence (AI) technologies have been widely used in the field of education, and artificial intelligence in education (AIEd) has gained increasing attention. However, no quantitative meta-analysis has been conducted on the overall effectiveness of AI on learning achievement and learning perception. To close this research…
Descriptors: Instructional Effectiveness, Artificial Intelligence, Academic Achievement, Student Attitudes
Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
Artur Strzelecki – Interactive Learning Environments, 2024
ChatGPT is an AI tool that assisted in writing, learning, solving assessments and could do so in a conversational way. The purpose of the study was to develop a model that examined the predictors of adoption and use of ChatGPT among higher education students. The proposed model was based on a previous theory of technology adoption. Seven…
Descriptors: Computer Software, Artificial Intelligence, Synchronous Communication, Technology Uses in Education
Pozón-López, I.; Kalinic, Zoran; Higueras-Castillo, Elena; Liébana-Cabanillas, Francisco – Interactive Learning Environments, 2020
The purpose of this study is to classify the predictors of satisfaction and intention to use in Massive Open Online Courses (MOOC). Informed by a scientific literature review, this work poses a behavioral model to explain intention to use via various constructs. To this end, the authors have carried out a study through an online survey of Spanish…
Descriptors: Online Courses, Large Group Instruction, Predictor Variables, Student Satisfaction
Chen, Wei-Wen; Chen, Ching-Chen; Dai, Chia-Liang; U, Nok Man; Cheng, Lin – Interactive Learning Environments, 2018
The incremental theory of intelligence has been identified as a strong predictor of students' learning motivation. Recent research has suggested various moderators of its effect. The present study sought to examine the moderating effects of self-enhancement and self-criticism on the relation between incremental intelligence beliefs and students'…
Descriptors: Foreign Countries, Intelligence, Student Motivation, Junior High School Students
