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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
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
Xia, Xiaona – Interactive Learning Environments, 2023
The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential…
Descriptors: Behavior Patterns, Learning Analytics, Decision Making, Correlation
Xing, Wanli; Pei, Bo; Li, Shan; Chen, Guanhua; Xie, Charles – Interactive Learning Environments, 2023
Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…
Descriptors: Learning Analytics, Engineering Education, Prediction, Design
Changhao Liang; Rwitajit Majumdar; Yuta Nakamizo; Brendan Flanagan; Hiroaki Ogata – Interactive Learning Environments, 2024
In-class group work activities are found to promote the interpersonal skills of learners. To support the teachers in facilitating such activities, we designed a learning analytics-enhanced technology framework, Group Learning Orchestration Based on Evidence (GLOBE) using data-driven approaches. In this study, we implemented the algorithmic group…
Descriptors: Algorithms, Group Dynamics, Group Activities, Learning Analytics
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Interactive Learning Environments, 2024
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games