Predicting Academic Success in Large Online Courses at a Mega ODL University

SAYKILI A., AYDIN S., UĞURHAN Y. Z. C., ÖZTÜRK A., BİRGİN M. K.

Technology, Knowledge and Learning, 2024 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s10758-024-09757-y
  • Dergi Adı: Technology, Knowledge and Learning
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, IBZ Online, Applied Science & Technology Source, Compendex, EBSCO Education Source, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC, Psycinfo
  • Anahtar Kelimeler: Academic success, Disciplinary differences, Learner demographics, Learning analytics, Online learning, Open and distance learning
  • Anadolu Üniversitesi Adresli: Evet

Özet

Learning analytics offer unprecedented opportunities for tracking and storing learning behaviors, thereby providing chances for optimizing learner engagement and success. The limited adoption of learning analytics by educational institutions hinders efforts to optimize learning processes through organizational and educational interventions, especially in open and distance learning settings. Therefore, this study attempts to investigate whether learning analytics data and learner demographic characteristics predict academic success in two distinct online large-scale undergraduate courses delivered at a mega distance teaching university. The prediction research design was employed, and correlation and multiple regression analyses were conducted to investigate the relationships between the predictor and outcome variables in this study. The results revealed that both learning analytics and learner demographics significantly predict academic success. However, there were disciplinary differences in terms of access to learning resources, their relative impact on academic success, and the impact of learner demographics on academic success.