Utilizing Learning Analytics in Measuring Students’ Learning Outcomes: Re-examining an Online Course Grounded in the Cognitive-Affective Theory of Learning with Media (CATLM)
Abstract: The purpose of this study was to use learning analytics to measure students’ learning outcomes in an online course using the design principles of cognitive-affective theory of learning with media (CATLM). The results indicated that the use of learning analytics can effectively measure students’ learning outcomes and reiterate the validity of the CATLM design principles in an online course. Participants were from two 4-year universities in the United States and the Republic of Turkey. Regression analysis was conducted to examine a proposed model to predict students’ learning outcomes through their effort, metacognitive and motivational factors. The results indicated that students’ meaningful learning (measured by retention and transfer tests) was a result of students’ effort (measured by the average learning session duration with the online learning content), motivation (measured by the number of sessions spent on the online learning module), and metacognition (measured by the time spent watching video, viewing the presentation and navigating the online reading). Learner’s effort was a significant predictor of retention and transfer test scores. There was a strong, positive correlation between the number of viewing sessions of the learning content and retention and transfer test scores. Finally, motivational elements in an online module positively impacted students’ cognitive engagement with the learning content and resulted in improvement in transfer test scores.
Presider: Emese Felvegi, University of Houston