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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
Al-Rahmi, Waleed Mugahed; Yahaya, Noraffandy; Alturki, Uthman; Alrobai, Amen; Aldraiweesh, Ahmed A.; Omar Alsayed, Alhuseen; Kamin, Yusri Bin – Interactive Learning Environments, 2022
Social media (SM) provide new opportunities to foster collaboration and engagement between students. However, the moderating effect of cyberstalking and cyberbullying on the relationship between students' academic performance and collaborative learning has not yet been addressed. Therefore, this study aims to bridge the literature gap concerning…
Descriptors: Social Media, Cooperative Learning, Predictor Variables, Academic Achievement
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
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
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
Silvia Wen-Yu Lee; Jyh-Chong Liang; Chung-Yuan Hsu; Meng-Jung Tsai – Interactive Learning Environments, 2024
While research has shown that students' epistemic beliefs can be a strong predictor of their academic performance, cognitive abilities, or self-efficacy, studies of this topic in computer education are rare. The purpose of this study was twofold. First, it aimed to validate a newly developed questionnaire for measuring students' epistemic beliefs…
Descriptors: Student Attitudes, Beliefs, Computer Science Education, Programming
Al-Maroof, Rana Saeed; Salloum, Said A.; Hassanien, Aboul Ella; Shaalan, Khaled – Interactive Learning Environments, 2023
This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation…
Descriptors: COVID-19, Pandemics, Fear, Educational Technology
Cole, Andrew W.; Lennon, Lauren; Weber, Nicole L. – Interactive Learning Environments, 2021
Student engagement in online courses may be more ambiguous, and complex to understand than in face-to-face (FtF) classrooms settings. Without regular FtF interaction, instructors may feel unable to accurately gauge student engagement, and respond accordingly. This research explored student perceptions of online active learning practices (Koohang,…
Descriptors: College Students, Student Attitudes, Active Learning, Learner Engagement
Ruiz, Samara; Urretavizcaya, Maite; Rodríguez, Clemente; Fernández-Castro, Isabel – Interactive Learning Environments, 2020
A positive emotional state of students has proved to be essential for favouring student learning, so this paper explores the possibility of obtaining student feedback about the emotions they feel in class in order to discover emotion patterns that anticipate learning failures. From previous studies about emotions relating to learning processes, we…
Descriptors: College Students, Computer Science Education, Emotional Response, Student Reaction
Calvo-Ferrer, José Ramón – Interactive Learning Environments, 2020
Although the "digital natives" theory is still used to justify the implementation of technologies such as video games in the language classroom, whether digital nativeness (i.e. the extent to which an individual embodies the characteristics attributed to digital natives) may be used as a predictor of digital game-based learning outcomes…
Descriptors: Computer Games, Video Games, Second Language Instruction, Second Language Learning
Teo, Timothy; Huang, Fang; Hoi, Cathy Ka Weng – Interactive Learning Environments, 2018
Given the paradox between pervasive promotion of technology use in English teaching and lack of studies about teachers' technology acceptance in China, this study aims to examine intentions of English teachers in China to use technology in their classroom teaching. Based on the technology acceptance model, eight variables including perceived…
Descriptors: Foreign Countries, Technology Uses in Education, Intention, English (Second Language)
Teo, Timothy – Interactive Learning Environments, 2012
This study examined pre-service teachers' self-reported intention to use technology. One hundred fifty-seven participants completed a survey questionnaire measuring their responses to six constructs from a research model that integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Structural equation modeling was…
Descriptors: Foreign Countries, Educational Technology, Structural Equation Models, Computer Uses in Education
Shin, Dong-Hee; An, Hyeri; Kim, Jang Hyun – Interactive Learning Environments, 2016
The use of a second screen can enhance information processing and the execution of search tasks within a given period. In this study, we examined the learner's attentional shift (AS) between two screens and controlled secondary tasks (STs) in the media multitasking setting and its effect on the learning process. In particular, we analyzed how…
Descriptors: Cognitive Processes, Search Strategies, Attention Control, Learning Processes
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