Can human-machine feedback in a smart learning environment enhance learners' learning performance? A meta-analysis

Mengyi Liao; Kaige Zhu; Guangshuai Wang

doi:10.3389/fpsyg.2023.1288503

Can human-machine feedback in a smart learning environment enhance learners' learning performance? A meta-analysis

Front Psychol. 2024 Jan 10:14:1288503. doi: 10.3389/fpsyg.2023.1288503. eCollection 2023.

Authors

Mengyi Liao¹, Kaige Zhu¹, Guangshuai Wang²

Affiliations

¹ School of Education, Pingdingshan University, Pingdingshan, Henan, China.
² National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China.

Abstract

Objective: 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, 2,222 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.

Conclusion: 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.

Keywords: feedback direction; feedback form; feedback technique type; human-machine feedback; meta-analysis; smart learning environment.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Humanities and Social Sciences Program of the Ministry of Education of China (grant no. 22YJCZH098), the Key Scientific and Technological Project of Henan Province of China (grant no. 222102310336), the Philosophy and Social Science Program of Henan Province of China (grant nos. 2021BJY030 and 2023BJY032), the Key Scientific Research Program of Universities in Henan Province of China (grant nos. 23B880012 and 24A880018), the Humanities and Social Sciences Program of Universities in Henan Province of China (grant no. 2024-ZZJH-102), the Natural Science Foundation of Hubei Province (grant no. 2023AFB854), Self-determined Research Funds of CCNU from the Colleges’ basic Research and Operation of MOE (grant no. CCNU23XJ006), the Key Project of Hubei Province Education and Science Plan (grant no. 2023GA007), and China Postdoctoral Science Foundation (grant no. 2023M741306).