The utility of adaptive eLearning data in predicting dental students' learning performance in a blended learning course.

Autor: Alwadei FH; Department of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia., Brown BP; Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, University of Illinois at Chicago, Chicago, Illinois, USA., Alwadei SH; Department of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia., Harris IB; Department of Medical Education, Department of Pathology, Department of Curriculum and Instruction, Curriculum Studies with Emphasis on Health Professions Education, College of Medicine, College of Education, University of Illinois at Chicago, Chicago, Illinois, USA., Alwadei AH; Department of Pediatric Dentistry and Orthodontics, College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
Jazyk: angličtina
Zdroj: International journal of medical education [Int J Med Educ] 2023 Oct 06; Vol. 14, pp. 137-144. Date of Electronic Publication: 2023 Oct 06.
DOI: 10.5116/ijme.64f6.e3db
Abstrakt: Objectives: To examine the impact of dental students' usage patterns within an adaptive learning platform (ALP), using ALP-related indicators, on their final exam performance.
Methods: Track usage data from the ALP, combined with demographic and academic data including age, gender, pre- and post-test scores, and cumulative grade point average (GPA) were retrospectively collected from 115 second-year dental students enrolled in a blended learning review course. Learning performance was measured by post-test scores. Data were analyzed using correlation coefficients and linear regression tests.
Results: The ALP-related variables (without controlling for background demographics and academic data) accounted for 29.6% of student final exam performance (R 2 =0.296, F (10,104) =4.37, p=0.000). Positive significant ALP-related predictors of post-test scores were improvement after activities (β=0.507, t (104) =2.101, p=0.038), timely completed objectives (β=0.391, t (104) =2.418, p=0.017), and number of revisions (β=0.127, t (104) =3.240, p=0.002). Number of total activities, regardless of learning improvement, negatively predicted post-test scores (β= -0.088, t (104) =-4.447, p=0.000). The significant R 2 change following the addition of gender, GPA, and pre-test score (R 2 =0.689, F (13, 101) =17.24, p=0.000), indicated that these predictors explained an additional 39% of the variance in student performance beyond that explained by ALP-related variables, which were no longer significant. Inclusion of cumulative GPA and pre-test scores showed to be the strongest and only predictors of post-test scores (β=18.708, t (101) =4.815, p=0.038) and (β=0.449, t (101) =6.513, p=0.038), respectively.
Conclusions: Track ALP-related data can be valuable indicators of learning behavior. Careful and contextual analysis of ALP data can guide future studies to examine practical and scalable interventions.
Databáze: MEDLINE
Externí odkaz: