The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Self-assessment in the process of self-regulated learning and its relationship with academic achievement
Abstract
Despite the theoretical argument and empirical evidence regarding the impact of self- assessment on academic achievement and self-regulated learning (SRL), the mechanism for this impact is understudied. The present study aimed to investigate the characteristics of self-assessment practices at different SRL phases and its relationship with academic achievement. Using a course assignment as the learning task, sixty-three students enrolled in a one-year master programme in a teacher education institute responded to an instrument assessing their self-assessment practices (including four self-assessment actions) at the SRL Preparatory, Performance and Appraisal phases of the task. Their final scores of the assignment were also collected. The results showed that self-assessment is a fundamental skill for SRL and occurs at each SRL phase with different patterns. Autoregressive relationships were found for all self-assessment actions between different SRL phases. Self-reflection at Performance phase was found to influence feedback seeking at Appraisal phase. Self-directed feedback seeking through monitoring at Performance phase was the strongest and positive predictor of academic achievement; and achievement had negative impact on all self-assessment actions at Appraisal phase. This study may assist educators and researchers to better understand the complexity of self-assessment in relation to learning process.
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“It is widely reported in many reviews and empirical studies that self-assessment can benefit academic achievement.”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [46]
In the citing paper
“It can be achieved through the activities such as selfgrading and self-regulated learning [45,46].”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [46]
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Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education
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Metadata
- Authors
- Yan, Z.
- Affiliations
- Education University of Hong Kong (HK)
- Corresponding author
- Yan, Zi
- ORCID
- Yan, Zi
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2019-06-26
- Volume
- 45
- Issue
- 2
- Pages
- 224-238
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W2955338425
- Cited by
- 217
- References
- 44
- Citations by year
- 2026: 22 · 2025: 38 · 2024: 39 · 2023: 49 · 2022: 39 · 2021: 23 · 2020: 6 · 2019: 1
- FWCI
- 46.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Evaluation of Teaching Practices
- Keywords
- self-assessment, student self-regulation, academic performance, self-reflection, feedback seeking, self-monitoring, pre-service teachers, course assignments
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