@inproceedings{e840ee8045dd4cbaa72b97083d540ccf,
title = "Peer Feedback in Online Learning Communities: Its Effectiveness on Internal Motivation from the Perspective of Self-Determination Theory",
abstract = "To interrogate the inequitable amount of feedback provided by the instructor to each student in class, this study targeted at exploring the possibility of facilitating learning via peer feedback. Learning with peers is found to be beneficial to both disciplinary skills and learning motivation. Without time and space limits, educators can easily take advantage of social media develop online learning communities that empower students to conduct peer learning by giving feedback to each other. This research collected peer feedback in a Facebook private group of an introductory statistics course at a national university in northern Taiwan. Participants were 34 graduate students in the course. After analyzing and coding messages in the Facebook group into four levels (i.e., task, process, self-regulation, and self), this study found that students primarily received task-level feedback (40.79%) but infrequently received self-level feedback (8.48%). In addition, this study utilized machine learning techniques to examine the effect of different feedback levels on students' learning motivation. Results showed that self-regulation-level feedback stimulated autonomous regulation, but process-level feedback undermined it. From a student-centered perspective, this study proposed a practical framework promoting learning equity about receiving feedback. The present study implemented learning analytics, linking empirical evidence and motivational theory. It implies that teachers can promote learning equity by engaging students to initiate self-regulation level feedback for each other.",
keywords = "Equitable Educatio, Learning Analytics, Learning motivation, Machine learning, Peer feedback, Self-determination theory",
author = "Liao, {Chen Hsuan} and Chung, {Hsin Jung} and Wu, {Jiun Yu}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Advanced Learning Technologies, ICALT 2023 ; Conference date: 10-07-2023 Through 13-07-2023",
year = "2023",
doi = "10.1109/ICALT58122.2023.00048",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "144--148",
editor = "Maiga Chang and Nian-Shing Chen and Rita Kuo and George Rudolph and Sampson, {Demetrios G} and Ahmed Tlili",
booktitle = "Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023",
address = "美國",
}