Provision Of Comments Research Articles

EvaluMate: Using AI to support students’ feedback provision in peer assessment for writing

Peer feedback plays an important role in promoting learning in the writing classroom. However, providing high-quality feedback can be demanding for student reviewers. To address this challenge, this article proposes an AI-enhanced approach to peer feedback provision. I introduce EvaluMate, a newly developed online peer review system that leverages ChatGPT, a large language model (LLM), to scaffold student reviewers’ feedback generation. I discuss the design and functionality of EvaluMate, highlighting its affordances in supporting student reviewers’ provision of comments on peers’ essays. I also address the system’s limitations and propose potential solutions. Furthermore, I recommend future research on students’ engagement with this learning approach and its impact on learning outcomes. By presenting EvaluMate, I aim to inspire researchers and practitioners to explore the potential of AI technology in the teaching, learning, and assessment of writing.

Pre-class online video learning and class style expectation: patterns, association, and precision medical education

Background Online video learning has been gaining substantial attention in medical education. The purpose of the study was to evaluate medical students’ online video-viewing patterns as well as to identify features associated with their class style preferences in precision medicine courses. Methods A mixed methods research design was used. Part of the cognitive load of the class content of “acute liver failure” was shifted to a 10-minute pre-class online video learning which was further reduced using threshold concept strategy. In the 2019 academic year, all fifth-year medical students who had viewed the pre-class video were invited to take a survey on their learning process and four class style expectations for the upcoming in-person class. For each round of medical students, teaching assistants helped collect anonymous survey data before the class. The teacher then adjusted the in-person class as part of precision medical education. Furthermore, the researchers coded the video-viewing patterns through an action log transformation, along with the questionnaire results, for analysis. Mann–Whitney U and Kruskal–Wallis tests were employed to compare group differences. Qualitative data were content-coded through a descriptive approach using thematic analysis. Results Of the 130 medical students, 114 (87.7%) joined the pre-class video learning, 113 (86.9%) responded to the questionnaires, and 87 (66.9%) provided their comments. Most medical students preferred the class styles of a thorough introduction (42.5%) and concept orientation (44.2%). High-engagement viewing patterns were associated with difficult concepts and the provision of comments. Class style expectation and video-viewing patterns did not demonstrate significant linkages. A majority of the substantial comments initiated thought-provoking questions after the online video learning. Conclusion Association between medical students’ preferred class styles and online video-viewing patterns was not necessarily linked. However, medical teachers are recommended to modify class styles based on medical students’ expectations after pre-class learning, thereby providing precision medical education. KEY MESSAGE Implementing precision medical education in the blended class is feasible. Online video learning is an ideal platform for balancing the dilemma between increasing the cognitive load of class content and the practice of precision medical education. The association between medical students’ preferred class styles and online video-viewing patterns was not clearly seen.