Single Paper Session 18

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Effects of removing visual feedback on writing to learn

Keywords
academic writing;cognitive processes;speaking and writing;writing to learn
Domain
Cognitive Science

Abstract

This study examined the effect of removing visual feedback while writing summaries of source texts on participants’ subsequent recognition memory for words contained in the source texts. Previous research has established a consistent writing superiority effect whereby words from the original text are recognised faster following a written summary compared to a spoken summary. The present study examined whether this advantage persists when visual feedback is removed during the production of a written summary. In a within subjects’ design, 32 university students were asked to read and then summarise text under three different conditions: (i) written summaries; (ii) spoken summaries and (iii) invisibly written summaries. Each condition contained 4 texts about randomly varying topics so that performance in the 3 different conditions was based on performance across 4 trials. In each trial, participants were asked to: (i) read a brief text: (ii) rate their understanding of the text; (iii) summarize the text; (iv) rate their understanding of the text again, before; (v) responding true / false to a recognition test of 30 words, 15 of which were taken from the original text and 15 of which had not been present in the text. The results showed that the writing superiority effect was preserved even when visual feedback was removed during writing. Participants in both writing conditions responded equally faster to words from the original texts compared to the participants in the spoken condition (F(2, 277) = 2.65, p

Exploring Keystroke Logging Behavior to Investigate Self-Regulated Writing of Undergraduate Students

Keywords
academic writing;cognitive processes;keystroke logging data processing and analysis;metacognition
Domain
Cognitive Science

Abstract

When supporting undergraduate students in a first-year writing course, we utilized Downs & Wardle’s (2007) evidenced-based model of writing-about-writing (WaW) to foster metacognitive monitoring and self-regulated writing (SRW) practices. After gaining IRB approval, 62 student volunteers (n=62) from a first-year writing course spent a 30-minute writing session in a lab setting. Students were asked to write about their writing process, and keystroke logging behavior (production, deletion, insertion, and pause time) was captured at the millisecond-level via InputLog (Leijten & Van Waes, 2013). Since the prompt is reflective in nature, we deductively coded participants’ sentences through the lens of self-regulated learning (SRL): planning, performance, and reflection (Zimmerman, 1998). Through the lens of Graham’s (2018) Writer(s)-Within-Community Model of Writing, a model that utilizes Zimmerman’s (1998) interpretation of SRL, we investigate how students may engage in keystroke logging behavior to investigate SRW strategies concurrently with behaviors enacted during the writing session by asking two research questions: (1) Are there distinct keystroke logging behavior patterns when responding to a self-reflective writing prompt? (2) Does the frequency of coded SRL sentences relate to the patterns that emerge? We investigated these research questions via Markov Chain Analysis to analyze the nominal keystroke logging behavior to identify patterns students enacted while writing; 6 common patterns suggested students engaged in metacognitive monitoring or revision behavior (e.g., delete → insert → insert). For the second question, we anticipate a logistic regression will demonstrate that students with a higher frequency of reflection codes will have a positive likelihood of enacting a pattern of metacognitive monitoring and/or revision. These results inform how students are engaging with the writing process when reflecting on their writing, a tool that might help us better understand students’ writing behaviors towards adapting pedagogical practices. Selected References Downs, D., & Wardle, E. (2007). Teaching about writing, righting misconceptions: (Re)envisioning “first-year composition” as “introduction to writing studies.” College Composition & Communication, 58(4), 552–584.Graham, S. (2018). A revised Writer(s)-Within-Community model of writing. Educational Psychologist, 53(4), 258–279. https://doi.org/10.1080/00461520.2018.1481406

Prompt – write – revise – repeat: a writing-process study of AI-assisted writing in higher education

Keywords
academic writing;AI writing;cognitive processes;writing processes
Domain
Cognitive Science

Abstract

With the widespread adoption of generative AI for (academic) writing, established models of the writing process such as Hayes (2012) need to be re-conceptualized. It has been suggested that writing could be viewed as a “co-activity of humans and machines” (Steinhoff 2023, Brommer & Rezat in print).To date, extensive survey-based research documents students’ AI use in higher education based on self-reports (cf. Ravšelj et al. 2025), whereas observational studies examining how students shape and appropriate human-AI co-activity in writing processes remain scarce (cf., however, Jelson et al. 2025).This study aimed to investigates writing strategies students use in AI-assisted writing, in particular, how students adapt and combine sub-processes, such as prompting, treatment of the AI output, AI-assistant revision, and their own revisions, and how different strategies impact the characteristics and quality of texts. To this end, several data-collection instruments were used: screen capture (OBS Studio) and keystroke logging (Leijten & Van Waes 2013) to record text production processes and the interaction between human input and AI output; stimulated recall (Gass 2000) to capture (meta-)cognitive processes; and a short questionnaire on AI-supported writing strategies and participants’ self-efficacy beliefs.The paper reports on a study comprising 12 writing sessions with students of German studies who varied in their experience with academic writing and AI use, testing the combination of methods and exploring writing processes and strategies with the aim of developing a category system for their description and analysis. ReferencesBrommer, S., Rezat, S. (pre-print). Mensch-KI-Interaktion beim Schreiben – Theoretische Überlegungen zur Modellierung des Schreibprozesses. In: Weder, M., Bubenhofer, N. (eds.): Schreiben mit KI. transcipt.Hayes, J. R. (2012). Modeling and Remodeling Writing. In: Written Communication 29, 369–388. Leijten, M., Van Waes, L. (2013). Keystroke Logging in Writing Research: Using Inputlog to Analyze Writing Processes. Written Communication 30(3), 358-392. Ravšelj, D., et al. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. In: PLOS ONE, 20. https://doi.org/10.1371/journal.pone.0315011.Steinhoff, T. (2023): Der Computer schreibt (mit). Digitales Schreiben mit Word, Whatsapp, ChatGPT & Co. als Koaktivität von Mensch und Maschine. In: MiDU-Medien im Deutschunterricht, IDSL II. (1), 1–16.