LMS quality predicts blended-learning satisfaction, which predicts academic performance (β = .604 and β = .535)¶

Claim · All claims
Evidence · 1 study · associational r? · q2 · i3 large
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 LMS quality significantly predicted blended-learning satisfaction (β = .604, p < .001), which then predicted academic performance (β = .535, p < .001). → Charles Atta Koduah 2025

Evidence¶

Charles Atta Koduah 2025¶

Charles Atta Koduah, Harry Barton Essel, Prosper Setsoafia, Edmund Boamah Acheampong, and 5 more. (2025). AI Literacy and Academic Performance among Non-STEM Students in KNUST: The Roles of LMS Quality and Blended-Learning Satisfaction. Research Square preprint. https://doi.org/10.21203/rs.3.rs-11239563/v1

q2 · i3 · associational · r?

PLS-SEM paths in the 432-student KNUST survey: "The LMS quality significantly predicted BLS (β = .604, p < .001), which then predicted academic performance (β = .535, p < .001)".

"The LMS quality significantly predicted BLS (β = .604, p < .001), which then predicted academic performance (β = .535, p < .001)."

Discussion¶

Related Claims¶

• AI literacy has a significant direct effect on academic performance among non-STEM students (β = .222, p = .001) — related
• AI literacy strongly predicts LMS quality among non-STEM students (β = .814, p < .001) — related
• LMS quality did not significantly influence academic performance and did not independently mediate the AI literacy–performance relationship — reports the opposite
• The sequential pathway from AI literacy through LMS quality and blended-learning satisfaction to academic performance is significant (β = .263, p < .001) — related