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According to the simple view of writing (Berninger, Abbott, Abbott, Graham, & Richards, 2002), the two important components of transcription in writing are handwriting and keyboarding, the third one being spelling. The purpose of this paper is to review the contribution of two writing modes—handwriting and keyboarding to writing performance. In the first section, the contribution of handwriting fluency to writing performance was explored through moderator analyses. We found that handwriting fluency contributes to writing significantly and consistently, and significantly contributes to specific writing measures (e.g., writing quality, writing fluency, substantive quality). We then explored the relationship between handwriting and keyboarding, and compared their contributions to writing. Results indicated that performance on fluency of handwriting and keyboarding were significantly related, particularly on speed. Writing qualities under each mode were relatively competitive; however, keyboarding allows for faster writing. The findings from the two sections emphasized the importance of handwriting on writing development even though keyboarding is accessible.

Keystroke logging is used to automatically record writers’ unfolding typing process and to get insight into moments when they struggle composing text. However, it is not clear which and how features from the keystroke log map to higher-level cognitive processes, such as planning and revision. This study aims to investigate the sensitivity of frequently used keystroke features across tasks with different cognitive demands.Two keystroke datasets were analyzed: one consisting of a copy task and an email writing task, and one with a larger difference in cognitive demand: a copy task and an academic summary task. The differences across tasks were modeled using Bayesian linear mixed effects models. Posterior distributions were used to compare the strength and direction of the task effects across features and datasets. The results showed that the average of all interkeystroke intervals were found to be stable across tasks. Features related to the time between words and (sub)sentences only differed between the copy and the academic task. Lastly, keystroke features related to the number of words, revisions, and total time, differed across tasks in both datasets. To conclude, our results indicate that the latter features are related to cognitive load or task complexity. In addition, our research shows that keystroke features are sensitive to small differences in the writing tasks at hand.

This study contributes to the literature on second language (L2) learners' revision behavior by describing what, when, and how often L2 learners revise their texts when responding to timed writing tasks on the computer and by examining the effects of task type, L2 proficiency, and keyboarding skills on what and when L2 learners revise. Each of 54 participants with 2 levels of L2 proficiency (low vs. high) and 2 levels of keyboarding skills (low vs. high) responded to timed independent and integrated writing tasks on the computer. A keystroke logging program recorded each participant's writing activities. Keystroke data were coded in terms of participants' revision behavior (e.g., orientation, linguistic domain, and temporal location of revisions) and then compared across tasks and learner groups. The findings suggest that the participants tended to revise form more often than content and that L2 proficiency and, to a lesser extent, task type, but not keyboarding skills, affected participants' revision behaviors during the timed writing tasks. Overall, the participants made more precontextual (that is, at the point of inscription) revisions than contextual revisions (that is, revisions of already written text), made considerably more typography and language revisions than content revisions, revised more frequently at the phrase and word level than at higher levels, and tended to make precontextual revisions more frequently in the first two thirds of the writing process and contextual revisions most frequently in the last third of the writing session. The findings and their implications for practice and research are discussed. (Verlag).

While there are many ways to author text today, writing with paper and pen (or pencil) is still quite common at home and work, and predominates writing at school. Because handwriting can bias readers' judgments about the ideas in a text and impact other writing processes, like planning and text generation, it is important to ensure students develop legible and fluent handwriting. This meta-analysis examined true- and quasi-experimental intervention studies conducted with K-12 students to determine if teaching handwriting enhanced legibility and fluency and resulted in better writing performance. When compared to no instmction or non-handwriting instructional conditions, teaching handwriting resulted in statistically greater legibility (ES=0.59) and fluency (ES=0.63). Motor instmction did not produce better handwriting skills (ES=0.10 for legibility and −0.07 for fluency), but individualizing handwriting instmction (ES=0.69) and teaching handwriting via technology (ES= 0.85) resulted in statistically significant improvements in legibility. Finally, handwriting instmction produced statistically significant gains in the quality (ES=0.84), length (ES= 1.33), and fluency of students' writing (ES=0.48). The findings from this meta-analysis provide support for one of the assumptions underlying the Simple View of Writing (Berninger et al., Journal of Educational Psychology, 94, 291–304, 2002): text transcription skills are an important ingredient in writing and writing development.

When responding to a writing task, writers spend a significant amount of their time not writing. These periods of physical inactivity, or pauses, during writing provide observable and measurable cues as to when, where, and how long writers halt to plan and/or revise their texts. Consequently, examining writers’ pausing patterns can provide important insights into the cognitive processes that writers employ when composing and the impact of various individual, task, and contextual factors on those processes. This article discusses theory and research on writers’ pausing behavior; how pause analysis can be used to investigate second language (L2) learners’ writing processes; challenges in researching writers’ pausing behavior (e.g., defining pauses); and some strategies to address these challenges. Next, the article illustrates how L2 writers’ pause data can be collected, analyzed, and interpreted, using keystroke logging data from a research project that aimed to examine the effects of task type, L2 proficiency, and keyboarding skills on L2 learners’ writing processes when writing on the computer. The article concludes with a call for more research on L2 writers’ pausing behavior, particularly how L2 writers’ pausing behavior relates to L2 writing outcomes and development across learners, contexts, and time.

To date, there is no clear evidence to support choosing handwriting over keyboarding or vice versa as the modality children should use when they first learn to write. 102 Norwegian first-grade children from classrooms that used both electronic touchscreen keyboard on a digital tablet and pencil-and-paper for writing instruction wrote narratives in both modalities three months after starting school and were assessed on several literacy-related skills. The students’ texts were then analysed for a range of text features, and were rated holistically. Data were analysed using Bayesian methods. These permitted evaluation both of evidence in favour of a difference between modalities and of evidence in favour of there being no difference. We found moderate to strong evidence in favour of no difference between modalities. We also found moderate to strong evidence against modality effects being moderated by students’ literacy ability. Findings may be specific to students who are just starting to write, but suggest that for children at this stage of development writing performance is independent of modality.

The goal of this study is to model pauses extracted from writing keystroke logs as a way of characterizing the processes students use in essay composition. Low-level timing data were modeled, the interkey interval and its subtype, the intraword duration, thought to reflect processes associated with keyboarding skills and composition fluency. Heavy-tailed probability distributions (lognormal and stable distributions) were fit to individual students' data. Both density functions fit reasonably well, and estimated parameters were found to be robust across prompts designed to assess student proficiency for the same writing purpose, in addition, estimated parameters for both density functions were statistically significantly associated with human essay scores after accounting for total time spent writing the essay, a result consistent with cognitive theory on the role of low-level processes in writing.

Most research into second language (L2) writing has focused on the products of writing tasks; much less empirical work has examined the behaviours in which L2 writers engage and the cognitive processes that underlie writing behaviours. We aimed to fill this gap by investigating the extent to which writing speed fluency, pausing, eye-gaze behaviours and the cognitive processes associated with pausing may vary across independent and integrated tasks throughout the whole, and at five different stages, of the writing process. Sixty L2 writers performed two independent and two integrated TOEFL iBT writing tasks counterbalanced across participants. While writing, we logged participants’ keystrokes and captured their eye-movements. Participants took part in a stimulated recall interview based on the last task they had completed. Mixed effects regressions and qualitative analyses revealed that, apart from source use on the integrated task, L2 writers engaged in similar writing behaviours and cognitive processes during the independent and integrated tasks. The integrated task, however, elicited more dynamic and varied behaviours and cognitive processes across writing stages. Adopting a mixed-methods approach enabled us to gain more complete and specific insights than using a single method.

In many classrooms across the globe, students are expected to comprehend and produce handwritten and computer-generated texts as soon as they start school. As we progress towards digitalisation in education, it has become necessary to understand the effects of writing modality on students’ literacy performance and development. The current meta-analysis integrates findings from 22 international studies involving 6168 participants, comparing the effects of handwriting and keyboarding on the writing and reading performance of primary-aged students. Moderator analyses were executed to determine if grade level, keyboarding experience, timed measurement of letter writing, types of tasks measuring letter writing fluency, and study design moderated modality effects on writing outcomes. Results revealed a significant effect size when comparing writing quality between handwriting and keyboarding, with students producing better quality passages via handwriting than keyboarding (ES = 0.53). Results also revealed that only grade level significantly moderated the effect size for letter writing fluency and written word production. Findings indicated that handwriting and keyboarding practices are associated with improvements on specific reading skills in primary education, with no clear superiority of modality. We discuss implications for literacy research and teaching both locally and globally.

Although fluency is an important subconstruct of language proficiency, it has not received as much attention in L2 writing research as complexity and accuracy have, in part due to the lack of methodological approaches for the analysis of large datasets of writing-process data. This article presents a method of time-aligned keystroke logging and eye-tracking and reports an empirical study investigating L2 writing fluency through this method. Twenty-four undergraduate students at a private university in Turkey performed two writing tasks delivered through a web text editor with embedded keystroke logging and eye-tracking capabilities. Linear mixed-effects models were fit to predict indices of pausing and reading behaviors based on language status (L1 vs. L2) and linguistic context factors. Findings revealed differences between pausing and eye-fixation behavior in L1 and L2 writing processes. The article concludes by discussing the affordances of the proposed method from the theoretical and practical standpoints.