Big data analytics platform enhances teaching abilities in normal university students through triple helix...

Area of Science:

• Educational Technology
• Data Science in Education
• Teacher Professional Development

Background:

• Traditional teacher education programs lack systematic assessment and data-driven feedback, hindering the development of teaching competencies.
• University pedagogical expertise, industry technology, and government standards are often siloed, limiting synergistic educational advancements.

Purpose of the Study:

• To develop and validate an educational practice big data analysis platform.
• To enhance teaching competencies in university students through data-driven insights.
• To foster collaboration between academia, industry, and government in teacher education.

Main Methods:

• A 16-week quasi-experimental study involving 378 participants.
• Development and implementation of a big data analysis platform integrating Triple Helix theory principles.
• Comparison of teaching abilities between an experimental group using the platform and a control group.

Main Results:

• Significant improvements in teaching implementation (28.4%), student engagement (25.9%), and technology integration (22.6%) in the experimental group.
• The platform effectively provided data-driven feedback to participants.
• Demonstrated the value of cross-sector collaboration in educational practice.

Conclusions:

• Data-driven approaches within cross-sector collaboration frameworks are effective for transforming teacher education.
• The developed big data platform offers a systematic mechanism for assessing and improving teaching competencies.
• Future teacher education should integrate technological solutions and interdisciplinary partnerships for enhanced outcomes.