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Student engagement in the educational interface: understanding the mechanisms of student success
Open accessAbstract
Student success and retention continue to be of concern for higher education institutions. Wider participation, combined with lower completion rates for non-traditional students, highlights the need for new ways of understanding the student experience to ground policy and practice. This article provides this insight by drawing together a number of key constructs to refine a recent framework of student engagement. We argue that the transition metaphor, focusing on the first year, is limited because it depicts differences between students and institutions as both transient and temporal. Instead, we use a cultural lens to introduce the educational interface as a metaphor for the individual psychosocial space within which institutional and student factors combine and student engagement in learning occurs. Incorporating the interface into the existing framework of student engagement makes three contributions to our understanding of the student experience. First, the educational interface is a tangible way of representing the complex interactions between students and institutions, and how those interactions influence engagement. Second, the refined framework highlights four specific psychosocial constructs: self-efficacy, emotions, belonging and well-being, which, we contend, are critical mechanisms for mediating the interactions between student and institutional characteristics and student engagement and success. Finally, the refined framework helps to explain why some students with demographic characteristics associated with lower completion rates are retained and do go on to successfully complete their studies, while similar others do not. These three contributions, the interface, the key constructs within it being mediating mechanisms and their explanatory utility, provide focus for the design and implementation of curricula and co-curricular initiatives aimed at enhancing student success and retention, and importantly to evaluate the impact of these interventions.
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“as deeper reflection is consistently linked to higher self-efficacy and better outcomes”
From Enhancing Student Success through AI-Assisted Reflective Learning in Entrepreneurship Education: An Empirical Study (2026) · cites this paper as Kahu & Nelson, 2018
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“The increase in engagement and self-efficacy reinforces the link between reflective practice and student success”
From Enhancing Student Success through AI-Assisted Reflective Learning in Entrepreneurship Education: An Empirical Study (2026) · cites this paper as Kahu & Nelson, 2018
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Metadata
- Authors
- Kahu, E. R., & Nelson, K.
- Affiliations
- University of the Sunshine Coast (AU)
- Corresponding author
- Kahu, Ella R.
- Venue
- Higher Education Research & Development
- Publisher
- Informa UK Limited
- Published
- 2017-07-03
- Volume
- 37
- Issue
- 1
- Pages
- 58-71
- ISSN
- 0729-4360 (ISSN-L)
- 1469-8366
- OpenAlex
- W2729369333
- Cited by
- 1,108
- References
- 56
- Citations by year
- 2026: 272 · 2025: 146 · 2024: 153 · 2023: 144 · 2022: 128 · 2021: 117 · 2020: 66 · 2019: 59 · 2018: 22 · 2017: 1
- FWCI
- 144.12
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Higher Education Research & Development (publishedVersion)
- Massey Research Online (Massey University) (submittedVersion)
- University of Southern Queensland ePrints (University of Southern Queensland) (submittedVersion)
- Topics
- Social Sciences › Education › Higher Education Research Studies
- Social Sciences › Education › Higher Education Practises and Engagement
- Social Sciences › Education › Online and Blended Learning
- Keywords
- student engagement, educational interfaces, student success, higher education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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