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Xiang Feng; Keyi Yuan; Xiu Guan; Longhui Qiu – Interactive Learning Environments, 2024
Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…
Descriptors: MOOCs, Psychological Patterns, Artificial Intelligence, Prediction
Ya Xiao; Khe Foon Hew – Interactive Learning Environments, 2024
In recent years, many studies have highlighted the need to go beyond the "one-size-fits-all" gamification approach to tailored or personalised gamification to optimise students' engagement based on their user attributes. However, little is known about its effectiveness on student engagement. To advance the understanding of personalized…
Descriptors: Individualized Instruction, Gamification, Student Participation, Learner Engagement
Yun-Qi Bai; Ya-Qian Xu; Jian-Jun Xiao – Interactive Learning Environments, 2024
This study takes the value-based adoption model and CIE model of the learning process as the theoretical basis and combines them to explore the influencing factors and mechanisms of learners' online interaction and perceived value. Based on the questionnaire survey data of 81 learners' potential factors and their 45,166 real-time behavior data on…
Descriptors: MOOCs, Interaction, Student Behavior, Learning Processes
Ilana Dubovi – Interactive Learning Environments, 2024
Virtual reality (VR) has been shown to induce excessive affective processing, which in turn impacts the learning process and learning outcomes. Therefore, a better understanding of emotional dynamics and how emotions are instigated while learning with VR is needed. For this purpose, the students learning process with a VR simulation was captured…
Descriptors: Nonverbal Communication, Psychological Patterns, Learner Engagement, Computer Simulation
Maslin Masrom; Abdelsalam Busalim; Mark D. Griffiths; Shahla Asadi; Raihana Mohd Ali – Interactive Learning Environments, 2024
The use of Instagram is becoming increasingly popular among students. Excessive Instagram use (EIU) has become a growing problem that can impact students' lives psychosocially. This study applied uses and gratifications theory (UGT) to explore the impact of social gratification, content gratification, and entertainment along with social presence,…
Descriptors: Social Media, Delay of Gratification, Social Influences, Interpersonal Relationship
Zhongling Pi; Renjia Liu; Hongjuan Ling; Xingyu Zhang; Shuo Wang; Xiying Li – Interactive Learning Environments, 2024
A video lecture instructor exhibiting positive emotion has been shown to induce similar emotions in students, improving the students' motivation and increasing their attention, thus improving their learning performance. However, little systematic research exists on which specific design features with regards to the instructor can induce such…
Descriptors: Foreign Countries, Undergraduate Students, Nonverbal Communication, Affective Behavior
Ibrahim Arpaci; Mahadi Bahari – Interactive Learning Environments, 2024
Metaverse is an immersive three-dimensional (3D) virtual world inhabited by avatars beyond the physical realm. The COVID-19 pandemic has disrupted the education system and the need to accelerate the digitalization of education has received a lot of attention. Metaverse can be an alternative solution for sociocultural interaction and to continue…
Descriptors: Computer Simulation, Sustainability, Personal Autonomy, Technology Uses in Education
Tayebeh Sargazi Moghadam; Ali Darejeh; Mansoureh Delaramifar; Sara Mashayekh – Interactive Learning Environments, 2024
Learners' emotional states might change during the learning process, and unpredictable variations of a person's emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the…
Descriptors: Artificial Intelligence, Decision Making, Electronic Learning, Psychological Patterns
Ding-Chau Wang; Yong-Ming Huang – Interactive Learning Environments, 2024
Tiny and affordable computers (e.g. Raspberry Pi and Arduino) have been widely applied to technology-enhanced hands-on learning (THL). However, little scholarly attention has been devoted to the key factors behind students' performance in THL contexts. Therefore, this study not only helped the participants learn computer science through THL, but…
Descriptors: Handheld Devices, Self Efficacy, Educational Technology, Computer Science Education
Ilana Dubovi; Idit Adler – Interactive Learning Environments, 2024
Computer-based simulations are highly effective in supporting students' deep conceptual understanding of scientific ideas. However, in the unprecedented era of the COVID-19 outbreak, students around the world experienced an induced state anxiety, which may have affected their engagement with the learning environments and ultimately their academic…
Descriptors: COVID-19, Pandemics, Anxiety, Learner Engagement
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
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
Li Jin; Dawei Shang – Interactive Learning Environments, 2024
Massive open online courses (MOOC) have become important in the learning process and have been adopted in higher education, especially during the COVID-19 pandemic. However, few studies investigated MOOC continuance intention (CI) for arts disciplines. Thus, an integrated framework was proposed based on the expectation-confirmation model (ECM) and…
Descriptors: Art Education, MOOCs, Computer System Design, Continuing Education
Pooja Rana; Mithilesh Kumar Dubey; Lovi Raj Gupta; Amit Kumar Thakur – Interactive Learning Environments, 2024
In recent years, the system of student learning and academic emotions has been taken seriously to re-engineer the teaching-learning process at all levels of education. This research paper considers both aspects of assessing the translation of knowledge i.e. qualitative and quantitative. In the current scenario, quantitative and qualitative…
Descriptors: Educational Assessment, Outcomes of Education, Models, Evaluation Methods
Bilal Hamamra; Ahmad Qabaha – Interactive Learning Environments, 2024
Drawing on both Barthes' concepts of weariness, laziness, and boredom and Sartre's bad faith, this article analyzes Palestinian students' psychological discomfort with online education. As instructors of English, we have drawn not only on our own teaching experiences but have also analyzed the testimonies of our colleagues. We contend that online…
Descriptors: Psychological Patterns, Alienation, Electronic Learning, Foreign Countries
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