Emotional Safety and Identity Expression Within Online Learning Environments in Higher Education: Insights from A Canadian College
Bibliographic record
Abstract
Assuring quality learning is increasingly important to higher education institutions (HEIs) in Canada, especially with continued e-marketplace, online enrolment growth, and programming internationalization. This thesis narrows the topic of quality assurance (QA) in learning to emotional safety and identity expression in online learning environments (OLEs). Creating and facilitating a safe OLE is imperative for many reasons, most notably because it can positively impact retention, learner satisfaction, and academic success. This thesis will argue that feeling safe within an OLE is a necessary condition for learners to express aspects of their identity, resulting in a perceived increase in grades. Identity expression is part of transformational learning and thus becomes important to teaching and learning. The conditions for expressing identity online, therefore, ought to be encouraged and enhanced, making the role of the instructor paramount in this aspect of quality. The study was conducted by gathering the thoughts and experiences of nine instructors and nine learners (n=18) using a single-site data gathering methodology. Through study findings, this thesis contributes to educational research in four ways. One, my theoretical framework is based on Illeris' (2007, 2014a, 2018a) learning and identity theory, which supports the emerging notion that identity is intrinsically connected to and centrally positioned within the overall learning process. Two, I gathered perspectives and experiences of both instructors and learners on this topic, which is uncharacteristic within educational research yet arguably critical when developing a comprehensive understanding of such topics and in the design and provision of HE supports and services. Three, this research study extends the sparsely researched area of emotional safety in conjunction with identity expression within HE OLEs and confirmed its importance and role in QA. Four, the findings support the importance of an emotionally safe OLE and such an OLE can positively impact learner grades and experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".