Use of Computing Devices as Sensors to Measure Their Impact on Primary and Secondary Students' Performance

Area of Science:

• Educational Technology
• Digital Learning
• Computer Science

Background:

• Digital transformation impacts all sectors, including education.
• Learning Management Systems (LMS) are increasingly adopted, replacing traditional textbooks with digital content.
• Understanding the influence of access devices on student performance in LMS is crucial.

Purpose of the Study:

• To investigate the relationship between computing device type used for LMS access and student academic performance.
• To analyze how device type and operating system affect the number of subjects passed.
• To develop a predictive model for student success based on LMS access factors.

Main Methods:

• Monitoring student LMS access across elementary to bachelor's degree levels.
• Utilizing computing devices as sensors to collect data on device type and operating system.
• Analyzing the correlation between access device characteristics and academic outcomes.

Main Results:

• Students accessing LMS demonstrate significantly improved academic performance.
• Device type and operating system used for LMS access correlate with the number of passed subjects.
• A predictive model accurately forecasts the number of passed subjects based on access factors.

Conclusions:

• LMS access positively impacts student performance.
• The choice of computing device and operating system is a significant factor in academic success within digital learning environments.
• The developed predictive model offers valuable insights for educational institutions.