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Xiao 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
Xia, Xiaona – Interactive Learning Environments, 2023
Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are…
Descriptors: Interaction, Intervention, Student Behavior, Educational Environment
Mutahar Qassem – Interactive Learning Environments, 2024
This article reports on an empirical study on cognitive processes [i.e. (dis) fluency, temporal management of the translation process, and revision] and their relationship with fluency and adequacy. Based on Jakobsen and Schou's (1999) computational model of human translation, the author utilizes keylogging data retrieved from a Translog-II-based…
Descriptors: Cognitive Processes, Language Fluency, Translation, English
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
Suping Yi; Rustam Shadiev; Yanyan Zhang – Interactive Learning Environments, 2024
This study reviewed thirty-seven articles on intercultural learning supported by technology. The results are reported in terms of strength of evidence and relationship among research variables. The results indicated the following strength of evidence: (1) moderate evidence showed higher frequency of the technology usage in higher education or…
Descriptors: Literature Reviews, Multicultural Education, Technology Uses in Education, Predictor Variables
Lajoie, Susanne P.; Li, Shan; Zheng, Juan – Interactive Learning Environments, 2023
Monitoring one's learning activities is a key component of self-regulated learning (SRL) leading to successful learning and performance outcomes across settings. Achievement emotions also play an important part in SRL and consequently student learning outcomes. However, there is little research on how specific types of monitoring (i.e.…
Descriptors: Medical Students, Metacognition, Medical Evaluation, Evaluative Thinking
Astatke, Melese; Weng, Cathy; Chen, Sufen – Interactive Learning Environments, 2023
Due to COVID-19 pandemic, schools all over the world have gone from full face-to-face to online lessons. This paper analyzed the influences of social networking sites (SNS) on secondary school students' academic achievement. The original studies were extracted from the Web of Science database, and the review of the 27 selected journal articles…
Descriptors: Literature Reviews, Social Networks, Secondary School Students, Academic Achievement
Ahmad, Faizan; Zongwei, Luo; Ahmed, Zeeshan; Muneeb, Sara – Interactive Learning Environments, 2023
An insight regarding few of the experiences during video games playing activity is still fuzzy. This paper presents an extensive empirical study that analyzes the experiences of 100 participants (i.e. 25 children, younger adults, older adults, and elders each) during brain games play. This concludes a number of significant correlations among the…
Descriptors: Children, Young Adults, Older Adults, Experience
Qian Fu; Wenjing Tang; Yafeng Zheng; Haotian Ma; Tianlong Zhong – Interactive Learning Environments, 2024
In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…
Descriptors: Predictor Variables, Psychological Patterns, Programming, Self Management
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
Liu, Na; Pu, Quanlin – Interactive Learning Environments, 2023
One-to-one online learning has become pervasive in distance education. However, factors affecting learners' continuance intention toward one-to-one online learning are not well known. This study proposed a model to explain learners' continuance intention toward one-to-one online learning. The model extends previous technology acceptance models and…
Descriptors: Intention, Individualized Instruction, Electronic Learning, Distance Education
Yu, Liang; Zhang, Na; Zhang, Yuanyuan – Interactive Learning Environments, 2023
The aim of this study was to explore the relationships among participation, peer support, self-regulated learning (SRL), course discipline and academic performance in small private open course (SPOC) environments. The study sample comprised of 309 undergraduates who completed SPOCs within the past year and consisted of 53% males (N = 164) and 47%…
Descriptors: Online Courses, Small Group Instruction, Undergraduate Students, Learning Strategies
Zalazar-Jaime, Mauricio Federico; Moretti, Luciana Sofía; García-Batista, Zoilo Emilio; Medrano, Leonardo Adrián – Interactive Learning Environments, 2023
The urgent imperative to "move online," caused by the recent COVID-19 pandemic, has led to an in-depth study of the psychological factors involved in designing successful online learning experiences. The social-cognitive model of academic satisfaction has been widely researched in conventional educational contexts in different countries.…
Descriptors: Student Satisfaction, Electronic Learning, COVID-19, Pandemics
Jon-Chao Hong; Jhen-Ni Ye; Jian-Hong Ye; Ling-Wen Kung – Interactive Learning Environments, 2024
Attentional control theory indicates that concentration is considered an important variable that contributes to learning. There are some devices for players to practice their concentration, but there are few virtual reality (VR) designs which can increase the level of difficulty for students to discipline their mental concentration with…
Descriptors: Attention Control, Predictor Variables, Cognitive Processes, Difficulty Level
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