Can human-machine feedback in a smart learning environment enhance learners’ learning performance? A meta-analysis - Liao, Mengyi; Zhu, Kaige; Wang, Guangshuai (Frontiers in Psychology, 2024)
The human-machine feedback in a smart learning environment can influences learners' learning styles, ability enhancement, and affective interactions. However, whether it has stability in improving learning performance and learning processes, the findings of many empirical studies are controversial. This study aimed to analyze the effect of human-machine feedback on learning performance and the potential boundary conditions that produce the effect in a smart learning environment. Methods: Web of Science, EBSCO, PsycINFO and Science Direct were searched for publications from 2010 to 2022. We included randomized controlled trials with learning performance as outcome. The random effects model was used in the meta-analysis. The main effect tests and the heterogeneity tests were used to evaluate the effect of human-machine feedback mechanism on learning performance, and the boundary conditions of the effect were tested by moderating effects. Moreover, the validity of the meta-analysis was proved by publication bias test. Results: Out of 35 articles identified, 2222 participants were included in this study. Human-machine interaction feedback had significant effects on learners' learning process (d = 0.594, k=26) and learning outcomes (d=0.407, k=42). Also, the positive effects of human-machine interaction feedback were regulated by the direction of feedback, the form of feedback, and the type of feedback technique. Conclusions: To enhance learning performance through human-machine interactive feedback, we should focus on using two-way and multi-subject feedback. The technology that can provide emotional feedback and feedback loops should be used as a priority. Also, pay attention to the feedback process and mechanism, avoid increasing students' dependence on machines, and strengthen learners' subjectivity from feedback mechanism.