Construction of Smart Higher Education Teaching Resources Using Data Analysis Technology in Unbalanced Data...

Area of Science:

• Educational Technology
• Data Mining
• Higher Education

Background:

• Developing instructional materials for smart higher education is challenging due to difficulties in gathering teaching resources.
• Existing methods for establishing teaching materials require improvement in resource discovery, marking, and organization.

Purpose of the Study:

• To propose a study utilizing data mining technology for the creation of smart higher education instructional materials.
• To analyze the dynamic scheduling mechanism of intelligent higher education teaching resources in an unbalanced data environment.

Main Methods:

• Data mining technology application for resource discovery, marking, and organization.
• Data analysis of intelligent higher education teaching resources.
• Comparative analysis of student performance between two classes (Class A and Class B).

Main Results:

• Class A students demonstrated significantly higher academic performance compared to Class B students.
• Average scores: Class A (80.125) vs. Class B (71.45).
• Distribution analysis showed more students in Class A scoring between 90-100, while Class B students clustered between 50-60.

Conclusions:

• The development of intelligent teaching resources for higher education using data mining technology is highly successful.
• Data mining provides a robust framework for enhancing educational material development and improving student outcomes.