Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC
Domain:
educationRecord type:
paper
Creator:
ChaBac
Editor:
EduUniCen
Publisher:
CCSD
Host:
International audience
This paper uses data mining from a French project management MOOC to study learners' performance (i.e., grades and persistence) based on a series of variables: age, educational background, socioprofessional status, geographical area, gender, self-versus mandatory-enrollment, and learning intentions. Unlike most studies in this area, we focus on learners from the French-speaking world: France and French-speaking European countries, the Caribbean, North Africa, and Central and West Africa. Results show that the largest gaps in MOOC achievements occur between 1) learners from partner institutions versus self-enrolled learners 2) learners from European countries versus low-and middle-income countries, and 3) learners who are professionally active versus inactive learners (i.e., with available time). Finally, we used the CHAID data-mining method to analyze the main characteristics and discriminant factors of MOOC learner performance and dropout.
Similar
Challenges of using Data Mining Techniques to Analyze and Forecast COVID-19 Pandemic in ZambiaAdoption of Massive Open Online Courses (MOOC) for Librarians' Professional Development in AfricaEducational data mining for students’ academic performance analysis in selected Ethiopian universitiesApplying Data Mining Techniques As a Tool To Analyze The Safety Ratios Of The Algerian National Bank (BNA) – Using DATAtab SoftwareUsing Data Mining to Predict Secondary School Student Performance for Zambiakojonketia/Using-hurdle-models-to-analyze-schistosomiasis-count-data