Optimizing Classroom Ecology through Learning Analytics to Improve Teaching and Student Engagement
Classroom teaching and students' learning have been widely concerned. To support the implementation of effective classroom teaching, this study examines the current state of instructional practices and student learning by surveying 541 college students. The questionnaire is divided into three main aspects, in which students are asked to assess their learning experience in college: motivations for choosing their major, their learning status, classroom performance, and feedback from teachers. Research results show that the motivation for choosing undergraduate majors positively affects students' learning. However, if teachers do not continue to provide the necessary guidance and high-quality courses to students, they will lose interest in their majors. For freshmen, teachers should be more concerned about their learning status, because they have the lowest adaptability to university learning. In addition, we also find that dumb classroom teaching, performing classroom teaching, still exists, and classroom teaching efficiency is low. Further analysis indicates that the teaching effectiveness of teachers, including teacher-student communication, positive motivation from teachers to students, and the content of their teaching, has a direct effect on students' learning. The low efficiency of classroom teaching is primarily attributed to an underdeveloped and ineffective classroom ecological environment. In the concluding section, this paper proposes a set of targeted strategies to rebuild and enhance the classroom teaching ecology, aiming to foster more effective teaching and meaningful student engagement.
Authors
- Y Zu
- Xiaohan Ren (ORCID: https://orcid.org/0000-0003-2973-2793)
- Syed Zaheer Ud Din
Institutions
- Qilu University of Technology (CN)
- Shandong Academy of Sciences (CN)
Publication Details
- Journal
- Interciencia
- Published
- 2025-01-01
- DOI
- https://doi.org/10.59671/anvbo
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Qilu University of Technology