Writing process feedback

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Writing process feedback

Type
Symposium
Keywords
feedback;Generative AI;writing processes;writing tools and writing technology
Domain
Learning and Instructional Technology

Abstract

This symposium continues the growing conversation on process-focused writing feedback, extending work presented at SIG Writing 2024 (Paris). Building on earlier work using process data and real-time analytics to inform pedagogy, the 2026 session turns toward the next frontier: advancing writing process feedback through AI-integrated and other technology-rich environments that foreground writers’ intentions and decision-making. Across three empirical projects, contributors examine how fine-grained writing-process data —from keystroke logs to GenAI interaction data— can be translated into actionable feedback for both researchers and educators.Together, the presentations explore how writers at different levels of expertise use and reflect on their writing processes: from expert and student integration of GenAI tools, to students’ alignment of intentions and actions during complex source-based writing, to the pedagogical value of process reports and exemplars (grounded in keystroke logging data) combined with dialogic peer feedback. We consider how process-focused feedback can foster aspects of learning such as self-awareness and reflection, regulation, and agency across learning contexts. By bringing these strands together, the symposium invites discussion on methodological innovation, data ethics, and pedagogical design in the next generation of process-focused writing research. It also aims to bridge insights from different methodologies (such as qualitative case studies, process-tracing technologies, and classroom interventions) to envision how process-focused feedback can most effectively be provided to student writers.The symposium on writing process feedback will consist of three paper presentations followed by the discussant’s response, with time for Q&A among presenters and an open, structured discussion with participants to identify future directions for process-focused feedback research.

Beyond Text-Focused Feedback: The Added Value of Keystroke Logging Feedback & Dialogic Peer Feedback

Keywords
feedback;Generative AI;keystroke logging;writing processes
Domain
Learning and Instructional Technology

Abstract

Master’s students in Professional Communication & Management revise their texts several times before submitting a final version, guided by feedback. In addition to traditional, text-focused feedback, we introduced a combination of technologically supported process feedback (based on keystroke logging data) and a human-centred approach in which teachers supported students in reflecting on their writing processes. This process-oriented feedback was complemented by dialogic peer feedback, prompting students to engage in dialogue about their texts and underlying writing strategies.A total of 126 students wrote a bad-news email. Their writing processes were logged with Inputlog. After submitting a first draft, 57 students received an individual process report based on KSL data (Vandermeulen et al., 2020). Reflection was stimulated through comparisons with exemplar processes, some of which illustrated diverse ways of integrating GenAI tools into the writing process. A new KSL-based visualisation, the dynamic source network graph, was also piloted, mapping all consulted sources and their interconnections. Students subsequently clustered these sources into meaningful categories (e.g., GenAI tools, theory on bad-news emails, internet searches on content or formulation).All students then received text-focused feedback and revised their texts. Results showed that students exposed to both process- and text-focused feedback achieved significantly higher scores on their second drafts than those receiving text-focused feedback only.Subsequently, 53 students attended a session on requesting, giving, and processing feedback (De Kleijn, 2022; Tielemans et al., 2021), and were provided with tools to foster peer feedback dialogue (Bouwer et al., 2024; Landrieu et al., 2024). Analyses of third and final versions are underway to assess the added value of this dialogic peer exchange.Questionnaires and focus group discussions showed that students found the process reports clear and the exemplar comparisons insightful. Students emphasised, however, the need for teacher support in interpreting process data. Overall, 75% considered dialogic peer feedback useful, with more than half rating it more valuable than traditional peer feedback.Future research should further explore how combining KSL-based insights with teacher-guided reflection and dialogic peer feedback might foster students’ writing development and help them navigate GenAI tools more deliberately.

Designing Intention and Process-Informed Strategies for Self-Regulation of Writing

Keywords
feedback;reading and writing;writing instruction;writing processes
Domain
Learning and Instructional Technology

Abstract

Writing from sources requires students to coordinate complex reading and writing processes, yet many struggle to connect their intentions with their actions during composition. This presentation reports on a three-part research project that explores how students’ mental representations, process behaviors, and self-regulatory strategies interact during source-based writing.The first study examines intermediate composition students’ behaviors, their actions, while reading-to-write using qualitative coding of process measures. First, a corpus of student syntheses and source texts are diagrammed using Rhetorical Structure Theory (Mann & Thompson, 1988). Then, spans of students’ syntheses are matched to source texts using semantic similarity measures and qualitatively coded to describe how students adapt source material, considering rhetorical relations, hierarchical depth, and reading history. Students next write new syntheses, which are analyzed using the same RST-based coding scheme, but here the coding is applied to their real-time composing process rather than to a pre-existing corpus. After writing, the students are shown playback segments of their writing session and are asked, through stimulated recall, what they intended to do with the sentence they were writing and why they chose to write it. These student interviews are then coded with the same scheme as the corpus to allow for direct comparison to their coded writing session. By comparing students' stated intentions to their observed behaviors, this study identifies moments where writers’ actions diverge from their goals — what might be called “regulatory blind spots.”Early pilot work in this project has already shown some mismatches between what students believe they are doing during synthesis and what their writing processes reveal. These regulatory blind spots are then targeted through short, pre-writing instruction in setting intentions, monitoring their reading and writing coordination, and adapting strategies in real time. A second phase of this research will evaluate to what extent the targeted instruction on regulation of writing better aligns writers’ intentions with behaviors. Together, these studies argue for intention-informed process pedagogy: instruction that helps students notice, align, and adjust their writing processes to match their communicative goals. The study will be completed before the symposium, with full results ready for presentation.

From Expert Habits to Student Support: Using Process-Tracing to Build GenAI Writing Guidance

Keywords
AI writing;feedback;Generative AI;writing processes
Domain
Learning and Instructional Technology

Abstract

This presentation introduces an exploratory study that investigates how expert and student writers integrate generative AI (GenAI) into their writing processes and how we might translate expert strategies into meaningful process-focused guidance for students. Motivated by the need for more situated support for GenAI-assisted writing, this research combines qualitative case studies with process-tracing technologies to uncover patterns in writers’ use of GenAI tools.The study proceeded in three phases. First, we observed eight self-identified “expert” users: professionals across industry and academia who use GenAI regularly in communication-centric work. These participants engaged in an authentic writing task while using GenAI tools. Through screen capture, keystroke logging (via Grammarly Authorship), think-aloud and stimulated recall protocols, and retrospective interviews, we documented how these experts strategically incorporated AI assistance into drafting, revising, and decision-making processes. Second, we conducted parallel sessions with ten novice student writers to capture how less-experienced users navigated similar GenAI-supported tasks. In both of these phases, we extracted observable patterns across sessions by inductively developing a codebook of actions throughout the writing process.In the third phase, we compared expert and student process behaviors to identify key differences in GenAI usage, such as when writers pause to reflect, reject, or revise AI-generated suggestions, or engage in iterative prompting. Using our codebook of process actions, we developed a set of process-focused GenAI writing strategies based on expert behaviors, which we then used to systematically develop feedback for students displaying certain patterns of actions. This phase of data collection is ongoing but will be completed prior to the conference; we will describe how students responded to the scaffolded feedback provided to them on the basis of their process behaviors. This presentation will highlight preliminary findings from both expert and student process behaviors, share insights on integrating consumer-facing tools like Grammarly Authorship into writing research, and discuss the process-focused feedback developed for GenAI-integrated writing. We argue that pairing process-tracing data with qualitative case study methods enables more nuanced, scalable observations of GenAI-integrated writing, which can advance both writing process research and pedagogical design for AI-assisted composition.