How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Conijn, Rianne; Martínez-Maldonado, Roberto; Knight, Simon; Shum, Simon Buckingham; Waes, Luuk Van; Zaanen, Menno van

doi:10.1080/09588221.2020.1839503

Cited by 20 publications

(15 citation statements)

References 64 publications

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“…Their reflective writing behaviours in combination with reflective contents could reveal more insights into how students plan and enact the task. This aligned with the recent participatory research in the design of the writing analytics tools that the experts expected higher level and content-related feedback to support writing processes and assessment [14]. Future work should also focus on analytics from the individual reflective writing contents of students.…”

Section: Rq2supporting

confidence: 58%

“…As mentioned above, engaging in writing reflection practices about the learning process may provide benefits for learners, and supporting students during this process by enhancing their SRL strategies can improve their results. Unfortunately, it is very difficult for teachers to gain insight into their students' writing process, which could be one of the reasons why they only provide feedback on the final product [14]. Therefore, it is necessary to use tools that can provide meaningful information about the students' writing process to i) understand students' reflective writing behaviours, and ii) provide timely support to the students [14].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, it is very difficult for teachers to gain insight into their students' writing process, which could be one of the reasons why they only provide feedback on the final product [14]. Therefore, it is necessary to use tools that can provide meaningful information about the students' writing process to i) understand students' reflective writing behaviours, and ii) provide timely support to the students [14]. There are many tools developed to support writing instruction and assessment including automated essay scoring systems to assess writing quality [15], automated writing evaluation systems to provide feedback and correction suggestions [16] and intelligent tutoring systems that can provide automated feedback and provoke students' reflection through questions [17].…”

Section: Introductionmentioning

confidence: 99%

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Examining the Relationship Between Reflective Writing Behaviour and Self-regulated Learning Competence: A Time-Series Analysis

Suraworachet

Villa-Torrano

Zhou

et al. 2021

Technology-Enhanced Learning for a Free, Safe, and Sustainable World

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Self-Regulated Learning (SRL) competence is imperative to academic achievement. For reflective academic writing tasks, which are common for university assessments, this is especially the case since students are often required to plan the task independently to be successful. The purpose of the current study was to examine different reflection behaviours of postgraduate students that were required to reflect on individual tasks over a fifteen-week-long higher education course. Forty students participated in a standardised questionnaire at the beginning of the course to assess their SRL competence and then participated in weekly individual reflection tasks on Google Docs. We examined students' reflective writing behaviours based on time-series and correlation analysis of fine-grained data retrieved from Google Docs. More specifically, reflection behaviours between students with high SRL and low SRL competence were investigated. The results show that students with high SRL competence tend to reflect more frequently and more systematically than students with low SRL competence. Even though no statistically significant difference in academic performance between the two groups was found, there were statistical correlations between academic performance and individual reflective writing behaviours. We conclude the paper with a discussion on the insights into the temporal reflection patterns of different SRL competence student clusters, the impact of these behaviours on students' academic performance, and potential suggestions for appropriate support for students with different levels of SRL.

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Section: Rq2supporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Examining the Relationship Between Reflective Writing Behaviour and Self-regulated Learning Competence: A Time-Series Analysis

Suraworachet

Villa-Torrano

Zhou

et al. 2021

Technology-Enhanced Learning for a Free, Safe, and Sustainable World

View full text Add to dashboard Cite

show abstract

“…In addition, this study built on quantitative data, whereas more research using qualitative and introspective analyses are required to tap into the learners' psycholinguistic mechanisms. Fortunately, studies that aspire to take into account the role of affective considerations in dealing with AWE tools are gradually gaining traction (e.g., Conijn et al, 2020; Lee, 2019). As explained earlier, Criterion is equipped with a ‘peer review’ tool that allows peer groups to view and comment on one another's written work.…”

Section: Discussionmentioning

confidence: 99%

Implementing an automated feedback program for a foreign language writing course: A learner‐centric study

Hassanzadeh

Fotoohnejad

2021

Computer Assisted Learning

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Considering the prominence attached to written corrective feedback (WCF) within the domain of second/foreign language (L2) acquisition, automated writing evaluation (AWE) tools have steadily gained ground over the last two decades. The current study was an attempt to investigate the extent incorporating an AWE program, known as Criterion®, within a process writing framework would affect learners' writing quality in an English as a foreign language context. Moreover, we drew a comparison between the overall effects of computer‐ versus teacher‐generated WCF. Participants consisted of 53 tertiary level students from two intact essay‐writing classes. The within‐group findings revealed that the experimental group's writing scores rose significantly from the pretest to posttest after receiving automated WCF over several weeks. Significant progress was also noticed on all rounds of scores obtained from five essays assigned to this group except between the first and the second. As for the comparison between computer‐ and teacher‐administered WCF, the former condition was found to be more efficient. Overall, the findings provide a deeper insight into the workings of an automated evaluation tool and how it was employed in the L2 classroom to impart consistency to the assessment procedure and ease the burden traditionally placed upon teachers.

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“…In writing courses, clustering finds groups of students with similar writing behavior. Then, teachers can provide more accurate, personalized feedback to students and can help them in optimizing their cognitive potential (Conijn et al, 2020;Kochmar et al, 2020;Zheng et al, 2021). Consequently, they can improve self-regulated learning (Chou & Zou, 2020).…”

Section: Introductionmentioning

confidence: 99%

Clustering students’ writing behaviors using keystroke logging: a learning analytic approach in EFL writing

Talebinamvar

Zarrabi

2022

Lang Test Asia

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Feedback is an essential component of learning environments. However, providing feedback in populated classes can be challenging for teachers. On the one hand, it is unlikely that a single kind of feedback works for all students considering the heterogeneous nature of their needs. On the other hand, delivering personalized feedback is infeasible and time-consuming. Available automated feedback systems have helped solve the problem to some extent. However, they can provide personalized feedback only after a draft is submitted. To help struggling students during the writing process, we can use machine learning to cluster students who benefit the same from feedback using keystroke logs. We can apply the results in automated feedback systems that provide process feedback. In this study, we aim to find homogeneous student profiles based on their writing process indicators. We use fourteen process indicators to find clusters in the data set. We used these measures in a four-stage analysis, including (a) data preprocessing, (b) dimensionality reduction, (c) clustering, and (d) the analysis of the writing quality. Clustering techniques identified five different profiles: Strategic planners, Rapid writers, Emerging planners, Average writers, and Low-performing writers. We further validated the emerged profiles by comparing them concerning students' writing quality. The present work broadens our knowledge of how students interact with writing tasks and addresses how variations in writing behaviors lead to qualitatively different products. We discuss the theoretical underpinnings and potentials of finding profiles of students during writing in higher education.

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How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Cited by 20 publications

References 64 publications

Examining the Relationship Between Reflective Writing Behaviour and Self-regulated Learning Competence: A Time-Series Analysis

Examining the Relationship Between Reflective Writing Behaviour and Self-regulated Learning Competence: A Time-Series Analysis

Implementing an automated feedback program for a foreign language writing course: A learner‐centric study

Clustering students’ writing behaviors using keystroke logging: a learning analytic approach in EFL writing

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