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Exploring college students' continuance learning intention in data analysis technology courses: the moderating role
Liqiong Liu1, Pinghao Ye1, Joseph Tan2
1School of Business Administration, Wuhan Business University, Wuhan, China.
Frontiers in Psychology
|October 30, 2023
Summary
College students
Area of Science:
- Educational Technology
- Data Science Education
Background:
- Data analysis skills are crucial in the digital economy.
- Colleges are focusing on data analysis technology courses.
- Understanding student learning intentions is key for effective course design.
Purpose of the Study:
- To explore factors influencing college students' intention to continue learning data analysis technologies.
- To investigate the moderating role of self-efficacy between interactivity and learning intention.
Main Methods:
- Utilized the Stimulus-Organism-Response model and flow theory.
- Collected 314 valid questionnaires via online surveys.
- Validated the research model using SmartPLS.
Main Results:
- Cognitive interest and self-efficacy positively impacted continuance learning intention.
- Interactivity positively influenced self-efficacy.
- Self-efficacy significantly moderated the relationship between interactivity and continuance learning intention.
Conclusions:
- Cognitive interest and self-efficacy are vital for sustained learning in data analysis.
- Interactivity enhances self-efficacy, which in turn boosts learning persistence.
- Educational technology interventions should focus on building self-efficacy and fostering interest.
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