Content area
Business analytics has become a critical tool in today's data-driven world, enabling organizations to extract insights, optimize decisions, and gain competitive advantages. As the demand for skilled professionals in business analytics grows, higher education institutions are increasingly focused on equipping students with the necessary knowledge and skills to succeed in this field. Understanding the factors that influence student learning outcomes in business analytics education is essential for designing effective and inclusive learning environments. This study examines key factors affecting perceived learning effectiveness and satisfaction in business analytics courses, including cognitive presence, teaching presence, quantitative analytics self-efficacy, and prior experience. A research model was tested across different demographic groups, highlighting variations based on gender and academic level. Results show that teaching presence consistently drives learning outcomes, while cognitive presence is more critical for undergraduates. Quantitative analytics self-efficacy significantly influences male graduate students' learning outcomes, whereas prior experience is more relevant for graduate students. Notably, the model was less effective for female graduate students, suggesting the need for further exploration of factors influencing this group. This research provides actionable insights for educators, emphasizing the importance of fostering teaching presence and adapting strategies to students' backgrounds and confidence levels. By tailoring approaches to gender and academic level, educators can enhance learning effectiveness and satisfaction, ultimately supporting the development of a skilled workforce in business analytics.
Details
Student Satisfaction;
Self Efficacy;
Decision Making;
Online Courses;
Instructional Effectiveness;
In Person Learning;
MOOCs;
Educational Environment;
Course Content;
Educational Quality;
Graduate Students;
Learning Strategies;
Learning Processes;
Educational Technology;
Meta Analysis;
Mathematics Education;
Communities of Practice;
Electronic Learning;
Data Analysis;
Information Systems;
Outcomes of Education;
Computer Use;
Higher Education;
Educational Needs
Gender;
Influence;
Distance learning;
Hypotheses;
Knowledge;
Decision making;
Effectiveness;
Online instruction;
Higher education institutions;
Self-efficacy;
Higher education;
Investigations;
Analytics;
School environment;
Mathematics education;
Knowledge management;
Students;
Perceptions;
Learning;
Educational objectives;
Confidence intervals;
Mathematics teachers;
Information systems;
Business analytics;
Graduate students;
Quantitative analysis;
Demography;
College students;
Models;
Teaching;
Mapping;
Cognition;
Learning environment;
Organizational effectiveness;
Business;
Satisfaction;
Teachers;
Workforce;
Undergraduate students;
Business students;
Competitive advantage;
Learning outcomes