Peer review is useful for providing students with formative feedback, yet it is used less frequently in STEM classrooms and for supporting writing-to-learn (WTL). While research indicates the benefits of incorporating peer review into classrooms, less research is focused on students’ perceptions thereof. Such research is important as it speaks to the mechanisms whereby peer review can support learning. This study examines students’ self-reported approaches to and perceptions of peer review and revision associated with WTL assignments implemented in an organic chemistry course. Students responded to a survey covering how they approached peer review and revision and the benefits they perceived from participating in each. Findings indicate that the assignment materials guided students’ approaches during both peer review and revision. Furthermore, students described various ways both receiving feedback from their peers and reading their peers’ drafts were beneficial, but primarily connected their revisions to receiving feedback.
This content will become publicly available on January 1, 2025
- Award ID(s):
- 2121123
- NSF-PAR ID:
- 10540550
- Editor(s):
- East, Martin; Slomp, David
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Assessing Writing
- Volume:
- 59
- Issue:
- C
- ISSN:
- 1075-2935
- Page Range / eLocation ID:
- 100808
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Student Experiences With Peer Review and Revision for Writing-to-Learn in a Chemistry Course Context
-
Lewis, Scott (Ed.)
Research on student learning in organic chemistry indicates that students tend to focus on surface level features of molecules with less consideration of implicit properties when engaging in mechanistic reasoning. Writing-to-learn (WTL) is one approach for supporting students’ mechanistic reasoning. A variation of WTL incorporates peer review and revision to provide opportunities for students to interact with and learn from their peers, as well as revisit and reflect on their own knowledge and reasoning. However, research indicates that the rhetorical features included in WTL assignments may influence the language students use in their responses. This study utilizes machine learning to characterize the mechanistic features present in second-semester undergraduate organic chemistry students’ responses to two versions of a WTL assignment with different rhetorical features. Furthermore, we examine the role of peer review on the mechanistic reasoning captured in students’ revised drafts. Our analysis indicates that students include both surface level and implicit features of mechanistic reasoning in their drafts and in the feedback to their peers, with slight differences depending on the rhetorical features present in the assignment. However, students’ revisions appeared to be primarily connected to the peer review process
via the presence of surface features in the drafts students read (as opposed to the feedback received). These findings indicate that further scaffolding focused on how to utilize information gained from the peer review process (i.e. , both feedback received and drafts read) and emphasizing implicit properties could help support the utility of WTL for developing students’ mechanistic reasoning in organic chemistry. -
Writing and revising scientific explanations helps students integrate disparate scientific ideas into a cohesive understanding of science. Natural language processing technologies can help assess students’ writing and give corresponding feedback, which supports their writing and revision of their scientific ideas. However, the feedback is not always helpful to students. Our study investigated 241 middle school students’ a) use of feedback, b) how it affected their revisions, and c) how these factors affected students’ writing improvement. We found that students made more content-related revisions when they used feedback. Making content-related revisions also assisted students in improving their writing. But students still found it difficult to make integrated revisions and did not use feedback often. Additional support to assist students to understand and use feedback, especially for students with limited science knowledge, is needed.more » « less
-
null (Ed.)We present the design and evaluation of a web-based intelligent writing assistant that helps students recognize their revisions of argumentative essays. To understand how our revision assistant can best support students, we have implemented four versions of our system with differences in the unit span (sentence versus sub-sentence) of revision analysis and the level of feedback provided (none, binary, or detailed revision purpose categorization). We first discuss the design decisions behind relevant components of the system, then analyze the efficacy of the different versions through a Wizard of Oz study with university students. Our results show that while a simple interface with no revision feedback is easier to use, an interface that provides a detailed categorization of sentence-level revisions is the most helpful based on user survey data, as well as the most effective based on improvement in writing outcomes.more » « less
-
Revision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear S-notations, and the automated extraction of backspace keypresses. However, these approaches are time-intensive, vulnerable to construct, or restricted. Therefore, this article presents a computational approach to the automatic extraction of full revision events from keystroke logs, including both insertions and deletions, as well as the characters typed to replace the deleted text. Within this approach, revision candidates are first automatically extracted, which allows for a simplified manual annotation of revision events. Second, machine learning is used to automatically detect revision events. For this, 7120 revision events were manually annotated in a dataset of keystrokes obtained from 65 students conducting a writing task. The results showed that revision events could be automatically predicted with a relatively high accuracy. In addition, a case study proved that this approach could be easily applied to a new dataset. To conclude, computational approaches can be beneficial in providing automated insights into revisions in writing.more » « less