Content area
The rapid development of online education has underscored the necessity of data-driven teaching functions for enhancing teaching quality and efficiency. This paper investigates the role of data-driven approaches in online education, with a particular focus on the practical application of data for evaluating learning outcomes. It highlights the importance of integrating diverse evaluation methods to provide a comprehensive understanding of student performance. Additionally, the paper emphasizes the need for effective data visualization and interpretation to support informed decision-making in educational settings. It also addresses the challenges encountered in implementing data-driven teaching, such as data privacy concerns, technological limitations, and teachers' data literacy, proposing targeted countermeasures to overcome these obstacles. The findings aim to provide valuable insights and practical guidance for the advancement of data-driven teaching practices in online education.
Details
Learning Motivation;
Educational Theories;
Online Courses;
Student Motivation;
MOOCs;
Educational Environment;
Learner Engagement;
Educational Strategies;
Educational Quality;
Educational Resources;
Influence of Technology;
Distance Education;
Learning Processes;
Computers;
Learning Experience;
Educational Technology;
Student Experience;
Instructional Materials;
Educational Psychology;
Accuracy;
Learning Management Systems;
Electronic Learning;
Teaching Skills;
Educational Experience
Scientific visualization;
Teaching methods;
Computer assisted instruction--CAI;
Education;
Data analysis;
Learning management systems;
Data quality;
Human-computer interaction;
Distance learning;
Teachers;
Efficiency;
Educational psychology;
Teaching;
Personalized learning;
Online instruction;
Data;
Privacy;
Teacher evaluations;
Learning;
Visualization;
Internet;
Accuracy;
Literacy;
Information literacy;
School environment;
Decision making;
Educational objectives;
Learning outcomes