constructive-alignment
Filtering by topic constructive-alignment(2)Clear all filters
- PaperAssessment & Evaluation in Higher Education4 Jun 2026
When ‘good teaching’ isn’t enough: learning environments that affect student feedback literacy
Caroline Xin Liu, Lily M. Zeng
Assessment for understanding and clear goals significantly predict student feedback literacy, while good teaching and teacher feedback do not, according to a mixed-methods study of Mainland Chinese undergraduates in Hong Kong. The findings emphasize that constructive alignment in programme-level learning environments is critical for cultivating feedback literacy and improving learning outcomes.
Original abstract
Despite the growing interest in student feedback literacy for student learning, how it is shaped within programme-level learning environments and its influence on learning outcomes remain underexplored. Even fewer studies have focused on how non-local students may experience this despite the key factors that were reported to have an impact on student feedback literacy would be different in their case. Using mixed methods, this project investigates the relationship among programme-level learning environments, feedback literacy, and learning outcomes. Study 1 analysed quantitative data from 547 Mainland Chinese undergraduates in Hong Kong. Structural equation modelling revealed that assessment for understanding and clear goals and standards significantly predicted feedback literacy, which mediated the effects of learning environments on learning outcomes, whereas good teaching and teacher feedback were not significantly associated with student feedback literacy. Study 2, based on interviews with fifteen students, indicated that aligned assessment designs and transparent standards were the key factors that enhanced student feedback literacy, explaining why teaching-related aspects may be less directly involved in creating affordances. The findings advance understanding of feedback literacy as influenced by the learning environments in the higher education context. The study highlights the critical role of constructive alignment in cultivating sustainable feedback literacy and improving student learning outcomes.
- PaperAssessment & Evaluation in Higher Education29 May 2026
Between delegation and responsibility: an exploratory case study of graduate educators’ conceptualizations of AI-supported assessment using the AI assessment scale
Armağan Ateşkan
Graduate educators conceptualize AI-supported assessment not as technical classification but as ethical boundary-setting about delegating evaluative responsibility. Constructive alignment was strongest when AI was embedded in design, yet designing without AI tools increased awareness of AI dependence and confidence in unassisted design, suggesting AI assessment literacy may require experiential constraint.
Original abstract
This study investigates how graduate educators conceptualize and apply the AI Assessment Scale (AIAS) within their assessment design practice. Drawing on an exploratory qualitative case study design, the study analyzed eight AI-integrated lesson plans produced by in-service teachers in a technology elective course, supplemented by semi-structured interviews with four participants. Findings suggest that AIAS level selection is experienced not as a technical classification but as an ethical boundary-setting practice, a judgment about delegating evaluative responsibility between human and AI agents. Participants demonstrated variably developed AI assessment literacy: procedural ethics (integrity, authorship) and experiential ethics (learner agency) were more fully articulated than structural ethics (algorithmic bias, data governance). Constructive alignment was strongest when AI was constitutively embedded in the design; conversely, AI integration could shift assessment criteria from content-driven toward procedurally driven evaluation, a drift originating at the design-imagining stage before any tool was deployed. Notably, designing without AI tools heightened participants’ awareness of habitual AI dependence and, in several cases, increased confidence in unassisted design, suggesting that AI assessment literacy may require experiential constraint as well as conceptual instruction. Implications are discussed for teacher education, AIAS professional development, and AI assessment literacy.