LOSARI: A novel R-based statistical software to facilitate students' self-regulated learning in statistics courses

Area of Science:

• Educational Technology
• Statistical Software Development
• Learning Sciences

Background:

• Students often struggle with statistics due to complex software and coding requirements.
• Facilitating self-regulated learning (SRL) is crucial for effective statistics education.
• Existing tools may lack integrated support for independent learning and error correction.

Purpose of the Study:

• To develop and evaluate LOSARI, an R-based statistical software designed to enhance students' SRL in statistics.
• To provide an accessible, no-code platform for statistical analysis and learning.
• To integrate features that support independent learning, error correction, and result interpretation.

Main Methods:

• Development of LOSARI using R, incorporating a point-and-click interface.
• Integration of interactive video tutorials, real-time error notifications, and automatic result interpretation.
• Validation through a student satisfaction survey based on the End-User Computing Satisfaction (EUCS) model.

Main Results:

• LOSARI was successfully developed as an R-based statistical learning tool.
• Key features like interactive tutorials and real-time error feedback were implemented.
• Student satisfaction survey indicated positive perceptions and effectiveness for out-of-classroom learning.

Conclusions:

• LOSARI effectively supports students' self-regulated learning in statistics.
• The software's features contribute to a positive and effective learning experience.
• Further enhancements are planned to expand LOSARI's capabilities.