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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
Päivi Kousa; Hannele Niemi – Interactive Learning Environments, 2023
The aim of this study is to identify the ethical challenges, solutions and needs of educational technology (EdTech) companies. Qualitative data was collected in interviews with seven experts from four companies, and the data was analysed using inductive content analysis. The four main areas of challenges were ambiguous regulations, inequalities in…
Descriptors: Ethics, Artificial Intelligence, Educational Technology, Social Responsibility
Gwo-Jen Hwang; Kai-Yu Tang; Yun-Fang Tu – Interactive Learning Environments, 2024
This study provides research-based evidence to profile: (1) the roles of artificial intelligence in nursing; (2) its research applications; and (3) the research trends for future study. On the basis of the PRISMA statement, a series of AI and nursing education related keywords from the literature were used to retrieve high-quality journal articles…
Descriptors: Foreign Countries, Nursing Education, Nursing, Nursing Research
Chen, Dyi-Cheng; You, Ci-Syong; Su, Ming-Shang – Interactive Learning Environments, 2022
This study identified the competency requirement for artificial intelligence in finite element analysis. The 10 Delphi group members included 5 field engineers in mechanical fields and 5 scholars from a technology institute. Next, 10 field experts were invited to participate. Using the Delphi technique and analytic hierarchy process,…
Descriptors: Engineering, Technical Occupations, Competence, Artificial Intelligence
Hanxiang Du; Wanli Xing; Bo Pei – Interactive Learning Environments, 2023
Participating in online communities has significant benefits to students learning in terms of students' motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to…
Descriptors: Electronic Learning, Communities of Practice, Automation, Artificial Intelligence
Deneil D. Christian; Kenny A. Hendrickson; Ameeta Jadav – Interactive Learning Environments, 2024
Cameras may be viewed as an essential tool in online synchronous classes. They may give a sense of connectedness between the students and faculty. In fact, social presence is considered a vital factor in distance education. In our study, we examine faculty's perception of accepted reasons for students to turn off their cameras and perceived…
Descriptors: Video Technology, Electronic Learning, Synchronous Communication, College Faculty
Chenglu Li; Wanli Xing; Walter Leite – Interactive Learning Environments, 2024
As instruction shifts away from traditional approaches, online learning has grown in popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics methods such as machine learning have been used by educational scholars to support online learners on a large scale. However, the fairness of AI prediction in educational…
Descriptors: Artificial Intelligence, Prediction, Mathematics Achievement, Algorithms
Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence
Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
Soomaiya Hamid; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high…
Descriptors: Feedback (Response), Electronic Learning, Automation, Classification
Mohammed A. Al-Sharafi; Mostafa Al-Emran; Mohammad Iranmanesh; Noor Al-Qaysi; Noorminshah A. Iahad; Ibrahim Arpaci – Interactive Learning Environments, 2023
Artificial intelligence (AI)-based chatbots have received considerable attention during the last few years. However, little is known concerning what affects their use for educational purposes. This research, therefore, develops a theoretical model based on extracting constructs from the expectation confirmation model (ECM) (expectation…
Descriptors: Knowledge Management, Sustainability, Artificial Intelligence, Technology Uses in Education
Jiahong Su; Weipeng Yang – Interactive Learning Environments, 2024
The issue of Artificial Intelligence (AI) literacy is gaining popularity in the field of education. Most research on AI literacy has focused on primary, secondary, and higher education, and there has been limited examination of AI literacy programs in early childhood education. This study aimed to evaluate the impact of an eight-week AI literacy…
Descriptors: Foreign Countries, Artificial Intelligence, Technological Literacy, Early Childhood Education
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 Adeshola; Adeola Praise Adepoju – Interactive Learning Environments, 2024
The launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the…
Descriptors: Opportunities, Barriers, Artificial Intelligence, Natural Language Processing
Desheng Yan; Guangming Li – Interactive Learning Environments, 2024
Smart education, with its intelligent, individualized, and technologized content, represents people's lofty expectations for future education. It provides a good learning platform for teaching and an important environment in which students' digital learning power can be developed in the context of the information technology era. Digital learning…
Descriptors: Electronic Learning, Information Technology, Artificial Intelligence, Educational Environment

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