Facilitating learners' self‐assessment during formative writing tasks using writing analytics toolkit.
Background: Learners' writing skills are critical to their academic and professional development. Previous studies have shown that learners' self‐assessment during writing is essential for assessing their writing products and monitoring their writing processes. However, conducting practical self‐ass...
| Publicado en: | Journal of Computer Assisted Learning Vol. 40; no. 6; pp. 2822 - 2840 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180899670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180899670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2024 vid: 40 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 180899670 178352129 180899670 180899670 10.1111/jcal.13036 180899670 ppf: 2822 ppct: 18 formats: tig: atl: Facilitating learners' self‐assessment during formative writing tasks using writing analytics toolkit. aug: au: Tang, Luzhen Shen, Kejie Le, Huixiao Shen, Yuan Tan, Shufang Zhao, Yueying Juelich, Torsten Li, Xinyu Gašević, Dragan Fan, Yizhou affil: Graduate School of Education, Peking University, Beijing, China sug: subj: Self Assessment Learning Writing Data Display Checklists Human Machine Learning Feedback Validity Random Assignment Male Female Adult T-Tests Interrater Reliability Intraclass Correlation Coefficient Spearman's Rank Correlation Coefficient Funding Source Adult: 19-44 years Male Female ab: Background: Learners' writing skills are critical to their academic and professional development. Previous studies have shown that learners' self‐assessment during writing is essential for assessing their writing products and monitoring their writing processes. However, conducting practical self‐assessments of writing remains challenging for learners without help, such as formative feedback. Objectives: To facilitate learners' self‐assessment in writing, we developed a writing analytics toolkit and used data visualisation and cutting‐edge machine learning technology that provides real‐time and formative feedback to learners. Methods: To investigate whether our newly‐developed tool affects the accuracy and process of learners' self‐assessment, we conducted a lab study. We assigned 59 learners to complete writing (2 h) and revising (1 h) tasks. During the revision stage, we randomly assigned the learners to two groups: one group used the writing analytics toolkit while the second group was not granted access to the toolkit. Learners' self‐assessment accuracy and process of self‐assessment were compared between the two groups. Results: In our study, we found the toolkit helped learners in the experimental group improve the self‐assessment accuracy of their writing products compared to the learners in the control group. In addition, we also found that the affordances of the toolkit affected the learners' self‐assessment process, and poor design affordances may have prevented the learners from reflecting by themselves. Conclusions: Together, our empirical study shed light on the design of future writing analytics tools which aim at improving learners' self‐assessment during formative writing processes. Lay Description: What is already known about this topic: Learners' self‐assessment during writing is essential for assessing their writing products and monitoring their writing processes.Formative feedback is vital for improving learners' self‐assessment capabilities.Writing analytics tools offer feedback to learners, however, how these tools facilitate self‐assessment remains under investigated. What this paper adds: We developed a writing analytics toolkit and used data visualisation and cutting‐edge artificial intelligence technology that provides real‐time and formative feedback to learners.We found the toolkit helped learners improve the self‐assessment accuracy of their writing products.We also found that the affordances of the toolkit affected the learners' self‐assessment process, and poor design affordances may have prevented the learners from reflecting by themselves. Implications for practice and/or policy: It is crucial for learning analytics and educational technology developers to design feedback tools and provide formative assessments.Learning analytics tools should have high affordance so that learners can easily understand the formative feedback, thereby promoting actionable self‐assessment. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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