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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
Zhou, Yizhuo; Zhao, Jin; Zhang, Jianjun – Interactive Learning Environments, 2023
On e-learning platforms, most e-learners didn't complete the course successfully. It means that reducing dropout is a critical problem for the sustainability of e-learning. This paper aims to establish a predictive model to describe e-learners' dropout behavior, which can help the commercial e-learning platforms to make appropriate interventions…
Descriptors: Electronic Learning, Prediction, Dropouts, Student Behavior
Wang, Yu-Yin; Wang, Yi-Shun – Interactive Learning Environments, 2022
While increasing productivity and economic growth, the application of artificial intelligence (AI) may ultimately require millions of people around the world to change careers or improve their skills. These disruptive effects contribute to the general public anxiety toward AI development. Despite the rising levels of AI anxiety (AIA) in recent…
Descriptors: Test Construction, Test Validity, Artificial Intelligence, Anxiety
Yajun Wu; Xia Kang; Lisheng Li – Interactive Learning Environments, 2024
Previous studies have verified that teacher-student relationship quality has a positive effect on students' academic engagement. Few studies, however, have examined whether school psychological capital mediates the linkage between teacher-student relationship and academic engagement. The present study aimed to examine the mediated role of school…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Language Teachers
Alshurideh, Muhammad; Al Kurdi, Barween; Salloum, Said A.; Arpaci, Ibrahim; Al-Emran, Mostafa – Interactive Learning Environments, 2023
Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in…
Descriptors: Electronic Learning, Prediction, Least Squares Statistics, Structural Equation Models
Chen, Jyun-Chen – Interactive Learning Environments, 2022
"Learning by doing" involves completing a practice activity through the method of inquiry. This study combined the predict-observe-explain (POE) inquiry method and hands-on doing processes to develop a cycle-mode POED (predict-observe-explain-do) model with an e-learning system to help students complete a practice activity using…
Descriptors: Inquiry, Prediction, Observation, Hands on Science
Yang, Jinzhong; Wang, Qiyun; Wang, Jingying; Huang, Muxiong; Ma, Yongjun – Interactive Learning Environments, 2021
E-Schoolbag is an integrated platform presented on the portable digital devices for educational purposes. Successful and sustainable integration of e-Schoolbag into the classroom often demands teachers to be equipped with essential knowledge, skills, and willingness to employ it on a voluntary basis. The purpose of this study was to examine if…
Descriptors: Elementary School Teachers, Secondary School Teachers, Pedagogical Content Knowledge, Technological Literacy
Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
Er, Erkan; Gómez-Sánchez, Eduardo; Dimitriadis, Yannis; Bote-Lorenzo, Miguel L.; Asensio-Pérez, Juan I.; Álvarez-Álvarez, Susana – Interactive Learning Environments, 2019
This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data…
Descriptors: Alignment (Education), Learning Analytics, Instructional Design, Teacher Participation
Tseng, Sheng-Shiang; Yeh, Hui-Chin – Interactive Learning Environments, 2018
Reciprocal teaching (RT) has been used to improve English as Foreign Language (EFL) students' reading comprehension in face-to-face instruction. However, little was known about how they use the RT to comprehend English texts in an online environment. This study explored how the implementation of RT strategies with the use of an annotation tool to…
Descriptors: Reading Comprehension, Low Achievement, English (Second Language), Second Language Learning
Using Web-Based Collaborative Forecasting to Enhance Information Literacy and Disciplinary Knowledge
Buckley, Patrick; Doyle, Elaine – Interactive Learning Environments, 2016
This paper outlines how an existing collaborative forecasting tool called a prediction market (PM) can be integrated into an educational context to enhance information literacy skills and cognitive disciplinary knowledge. The paper makes a number of original contributions. First, it describes how this tool can be packaged as a pedagogical…
Descriptors: Prediction, Information Literacy, Information Skills, Decision Support Systems
Hong, Jon-Chao; Hwang, Ming-Yueh; Liu, Yeu-Ting; Lin, Pei-Hsin; Chen, Yi-Ling – Interactive Learning Environments, 2016
Educational games can be viewed in two ways, "learning to play" or "playing to learn." The Chinese Idiom String Up Game was specifically designed to examine the effect of "learning to play" on the interrelatedness of players' gameplay interest, competitive anxiety, and perceived utility of pre-game learning (PUPGL).…
Descriptors: Educational Games, Prediction, Anxiety, Competition
Wu, Jiun Yu; Peng, Ya-Chun – Interactive Learning Environments, 2017
This study tested the effects of the modality of reading formats (electronic vs. print), online reading habits (engagement in different online reading activities), use of cognitive strategies, metacognitive knowledge, and navigation skills on printed and electronic reading literacy across regions. Participants were 31,784 fifteen-year-old students…
Descriptors: Reading Habits, Literacy, Printed Materials, Information Seeking
Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao – Interactive Learning Environments, 2016
To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…
Descriptors: Reading Strategies, Prediction, Models, Quasiexperimental Design
Wu, Pai-Hsing; Wu, Hsin-Kai; Kuo, Che-Yu; Hsu, Ying-Shao – Interactive Learning Environments, 2015
Computer-based learning tools include design features to enhance learning but learners may not always perceive the existence of these features and use them in desirable ways. There might be a gap between what the tool features are designed to offer (intended affordance) and what they are actually used (actual affordance). This study thus aims at…
Descriptors: Science Instruction, Computer Uses in Education, Educational Technology, High School Students
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