Facilitating learners' self-assessment during formative writing tasks using writing analytics toolkit
Open accessAbstract
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.
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Multi-source assessment ecosystems combining self-assessment, peer evaluation, AI analytics, and instructor judgment provide triangulated evidence of competence.
“Multi-source assessment ecosystems combining self-assessment, peer evaluation, AI analytics, and instructor judgment provide triangulated evidence of competence (Elshall and Badir, 2025; Francis et al., 2025; Tang et al., 2024; Zhang and Zhang, 2022).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Tang et al., 2024
Section 2.1 recommends integrating feedback from learners, peers, teachers, and AI into writing analytics tools, and the Abstract identifies the lack of such integration as a design gap. The paper does not establish that an ecosystem combining all four provides triangulated evidence of competence; its discussion concerns formative writing feedback rather than competence assessment generally. No page numbers were available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTwo studies (n = 2) targeted AI-supported tools for writing assessment and feedback analytics.
“two studies (n = 2) targeted AI-supported tools for writing assessment and feedback analytics (Tang et al., 2024; Li et al., 2024).”
From The Use of Artificial Intelligence Tools in English Academic Writing among University Students: A Scoping Review (2025) · cites this paper as Tang et al., 2024
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Nazaretsky, T., Mejia‐Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2026) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70153 · Cited by 26 · see citing works
Asking generative artificial intelligence the right questions improves writing performance
Cheng, Y., Fan, Y., Li, X., Chen, G., Gašević, D., & Swiecki, Z. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100374 · Cited by 21 · see citing works
Self-citationInnovations in Online Learning Analytics: A Review of Recent Research and Emerging Trends
Wang, Y., Lai, Y., & Huang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3493621 · Cited by 24 · see citing works
Banihashem, S. K., Gašević, D., & Noroozi, O. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70056 · Cited by 29 · see citing works
Self-citationThe potential of deep learning in improving K‐12 students' writing skills: A systematic review
Bal, M., & Öztürk, E. (2025) · British Educational Research Journal · doi:10.1002/berj.4120 · Cited by 14 · see citing works
FLoRA: An advanced AI-powered engine to facilitate hybrid human-AI regulated learning
Li, X., Li, T., Yan, L., Li, Y., Zhao, L., Raković, M., Molenaar, I., Gašević, D., & Fan, Y. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105527 · Cited by 16 · see citing works
“Secondly, integrating Bloom’s taxonomy into FLoRA ’s writing analytics demonstrated its capacity to improve the accuracy of students’ self-assessment and metacognitive monitoring (Tang et al., 2024).”
Self-citationBalancing syntactic complexity and clarity: the role of AI in enhancing academic writing proficiency
Bahari, A. (2025) · Saudi Journal of Language Studies · doi:10.1108/sjls-10-2024-0062 · Cited by 19 · see citing works
Teachers’ perceptions of value-sensitive AI in education: A case study of AI tutor
Shen, Y., Le, H., Wang, Z., & Wang, Q. (2025) · Teaching and Teacher Education · doi:10.1016/j.tate.2025.105253 · Cited by 10 · see citing works
Self-citationBo, Z., Pek, L. S., Jian, L., Cong, W., & Nan, L. T. (2025) · Language Teaching Research Quarterly · doi:10.32038/ltrq.2025.53.05 · Cited by 6 · see citing works
Li, M., Wang, X.-X., Chen, Y., & Cukurova, M. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105538 · Cited by 8 · see citing works
Evaluating the Effectiveness of AI ‐Driven Approaches on EFL Learners' Expository Writing Skills
Bahari, A. (2026) · TESOL Journal · doi:10.1002/tesj.70099 · Cited by 8 · see citing works
Zhou, W., Xu, X., & Luo, X. (2026) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-026-01090-5 · Cited by 1 · see citing works
“However, no significant group differences emerged in tool utility or perceived writing difficulty, echoing Tang et al. ( 2024 ), who argued that tools cannot fundamentally change students’ perceptions of writing anxiety.”
Hamsiah, A., Bakri, M., Muhammadiah, M., Rizal, A., Darwis, A., & Malik, A. J. R. (2026) · SAGE Open · doi:10.1177/21582440261423575 · Cited by 1 · see citing works
“The fourth model is reflective revision, defined as students’ ability to self-assess and make iterative improvements to their writing (Chung et al., 2021; Delante, 2017; Peeters & Sexton, 2019; L. Tang et al., 2024).”
Embang, J. E. G., Tan, Y. H. V., Hooi, S. C., Wong, A. H., & Wong, L. (2026) · Advances in Health Sciences Education · doi:10.1007/s10459-026-10547-7 · Cited by 1 · see citing works
Abdellatif, M. S., Alshehri, M. A., Lamouchi, A., Rahmath, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1809174
“Multi-source assessment ecosystems combining self-assessment, peer evaluation, AI analytics, and instructor judgment provide triangulated evidence of competence (Elshall and Badir, 2025; Francis et al., 2025; Tang et al., 2024; Zhang and Zhang, 2022).”
From Periphery to Center: The Emotional Sequence of Cognitive Revision in L2 Writing Assessment
Ke, Y., Zhou, X., & Wang, G. (2024) · Psychological Reports · doi:10.1177/00332941241282567
“These factors serve as crucial mediators in language cognition within language processing contexts (Lara-Lynn et al., 2023; Roeser et al., 2019; Tang et al., 2024).”
Dai, Z. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1829268 · Cited by 2 · see citing works
Human or GenAI Support? Conditions Impacting Students' Strategy Choices in an Essay Revision Task
Iqbal, S., Jovanović, J., Fan, Y., Raković, M., Li, X., Chen, G., & Gašević, D. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70300
Self-citationAliyeva, N. (2026) · Springer eBooks · doi:10.1007/978-981-95-9563-1_4
The role of AI for self-regulated learning: a systematic literature review
Jadhav, V. S., & Singh, S. (2026) · Journal of Computers in Education · doi:10.1007/s40692-026-00406-0
Alnashiri, H., Raković, M., Nawaz, S., Li, X., Fernandez-Nieto, G. M., Qian, K., Lämsä, J., Lim, L., Bannert, M., Järvelä, S., et al. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70331
Self-citation
- Authors
- Tang, L., Shen, K., Le, H., Shen, Y., Tan, S., Zhao, Y., Juelich, T., Li, X., Gašević, D., & Fan, Y.
- Affiliations
- Peking University (CN)
- Research Center for Data Hub and Security Zhejiang Lab Zhejiang China
- University of Chinese Academy of Sciences (CN)
- Monash University (AU)
- Corresponding author
- Fan, Yizhou
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2024-07-11
- Volume
- 40
- Issue
- 6
- Pages
- 2822-2839
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W4400598586
- Cited by
- 25
- References
- 106
- Citations by year
- 2026: 12 · 2025: 10 · 2024: 3
- FWCI
- 31.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Computer Assisted Learning (publishedVersion, cc-by-nc)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc)
- HAL (submittedVersion)
- Funders
- Peking University (7310101005)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- self-assessment, formative feedback, writing revision, self-assessment accuracy, data visualization, machine learning, reflective writing, writing skills
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