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Xiaona Xia – Interactive Learning Environments, 2023
Effective analysis and demonstration of these data features is of great significance for the optimization of interactive learning environment and learning behavior. Therefore, we take the big data set of learning behavior generated by an online interactive learning environment as the research object, define the features of learning behavior, and…
Descriptors: Learning Strategies, Interaction, Educational Environment, Learning Analytics
Xia, Xiaona – Interactive Learning Environments, 2023
The research of multi-category learning behaviors is a hot issue in interactive learning environment, and there are many challenges in data statistics and relationship modeling. We select the massive learning behaviors data of multiple periods and courses and study the decision application of regression analysis. First, based on the definition of…
Descriptors: Learning Analytics, Decision Making, Regression (Statistics), Bayesian Statistics
Jewoong Moon; Daeyeoul Lee; Gi Woong Choi; Jooyoung Seo; Jaewoo Do; Taehyeong Lim – Interactive Learning Environments, 2024
We implemented a systematic literature review to investigate the trends and issues of learning analytics in seamless learning environments. We collected and analyzed a total of 27 empirical journal articles that study and discuss learning analytics design and implementation in seamless learning environments. In a recent decade, researchers have…
Descriptors: Learning Analytics, Literature Reviews, Active Learning, Inquiry
Lei Xie; Cixiao Wang – Interactive Learning Environments, 2024
Connectionist MOOC (cMOOC) is a type of MOOC, which can provide an online learning space for learners to sustainable connect. This study analyzed the learning behavior and motivation of repeat learners in the cMOOC "Internet + Education: a dialogue between theory and practice" to study the repeat learning needs and characteristics of…
Descriptors: Learning Analytics, Motivation Techniques, MOOCs, Sustainability
Taihe Cao; Zhaoli Zhang; Wenli Chen; Jiangbo Shu – Interactive Learning Environments, 2023
Online learning with the characteristics of flexibility and autonomy has become a widespread and popular mode of higher education in which students need to engage in self-regulated learning (SRL) to achieve success. The purpose of this study is to utilize clickstream data to reveal the time management of SRL. This study adopts learning analytics…
Descriptors: Time Management, Self Management, Online Courses, Learning Analytics
MOOC Student Dropout Prediction Model Based on Learning Behavior Features and Parameter Optimization
Jin, Cong – Interactive Learning Environments, 2023
Since the advent of massive open online courses (MOOC), it has been the focus of educators and learners around the world, however the high dropout rate of MOOC has had a serious negative impact on its popularity and promotion. How to effectively predict students' dropout status in MOOC for early intervention has become a hot topic in MOOC…
Descriptors: MOOCs, Potential Dropouts, Prediction, Models
Catalina Lomos; J. W. Luyten; Frauke Kesting; Filipe Lima da Cunha – Interactive Learning Environments, 2024
Significant attention has been paid to the use of ICT by teachers, especially during the COVID-19 health crisis. This usage has mostly been captured through self-reported survey measurements. Learning analytics can complement such findings, by using log data to document precisely how long teachers use ICT, and what ICT behaviors they perform…
Descriptors: Learning Analytics, Information Technology, Teacher Behavior, Mathematics Education
Muhittin Sahin – Interactive Learning Environments, 2024
Learning analytics aims to improve learning and teaching in digital learning environments by optimizing them. Real-time feedback, suggestions, directions, and interventions are structured in the digital learning environments. In order to structure more effective interventions, it is crucial to ascertain which of these services offered to students…
Descriptors: Learning Analytics, Intervention, Student Attitudes, Preferences
Alonso-Fernández, Cristina; Calvo-Morata, Antonio; Freire, Manuel; Martínez-Ortiz, Iván; Fernández-Manjón, Baltasar – Interactive Learning Environments, 2023
Game Learning Analytics can be used to conduct evidence-based evaluations of the effect that serious games produce on their players by combining in-game user interactions and traditional evaluation methods. We illustrate this approach with a case-study where we conduct an evidence-based evaluation of a serious game's effectiveness to increase…
Descriptors: Educational Games, Learning Analytics, Game Based Learning, Computer Mediated Communication
Yousef, Ahmed Mohamed Fahmy; Khatiry, Ahmed Ramadan – Interactive Learning Environments, 2023
Several governments across the world have temporarily closed educational institutions due to the COVID-19 pandemic. In response, numerous universities have seen a growing trend towards online learning scenarios. Thus, learning takes place not just within a person, but within and across the networks. However, the current implementations of open…
Descriptors: Learning Analytics, Individualized Instruction, Reflection, Learning Processes
Yuanyuan Yang; Rwitajit Majumdar; Huiyong Li; Brendan Flanagan; Hiroaki Ogata – Interactive Learning Environments, 2024
Self-directed learning (SDL) requires students to take initiative to learn and control their own learning process. Literature highlights the importance of SDL for lifelong learning. Yet, little understanding is known regarding how to support SDL at the school level, specifically for out-of-class learning context. To fill up this gap, this research…
Descriptors: Learning Analytics, Independent Study, Learning Processes, Reading Habits
Knoop-van Campen, Carolien A. N.; Wise, Alyssa; Molenaar, Inge – Interactive Learning Environments, 2023
Teacher dashboards provide real-time information about students' performance and progress, which help K-12 teachers to adjust feedback to student' specific needs during learning. Prior research indicated two problems with respect to how teachers provide feedback: (i) teachers do not always select the most effective feedback to support student'…
Descriptors: Educational Technology, Learning Analytics, Feedback (Response), Low Achievement
Yang, Tzu-Chi; Chen, Sherry Y. – Interactive Learning Environments, 2023
Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field…
Descriptors: Student Behavior, Educational Technology, Electronic Learning, Cognitive Style
Sonsoles López-Pernas; Mohammed Saqr; Aldo Gordillo; Enrique Barra – Interactive Learning Environments, 2023
Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while…
Descriptors: Learning Analytics, Educational Games, Student Behavior, Computer Uses in Education
MOOC Performance Prediction and Analysis via Bayesian Network and Maslow's Hierarchical Needs Theory
Luyu Zhu; Jia Hao; Jianhou Gan – Interactive Learning Environments, 2024
Nowadays, Massive Open Online Courses (MOOC) has been gradually accepted by the public as a new type of education and teaching method. However, due to the lack of timely intervention and guidance from educators, learners' performance is not as effective as it could be. To address this problem, predicting MOOC learners' performance and providing…
Descriptors: MOOCs, Academic Achievement, Prediction, Bayesian Statistics