Remote learning during COVID-19 and transformative learning theory: tendency towards Quadruple Helix Model for future post-secondary education in Indigenous contexts
Bibliographic record
Abstract
Abstract This paper aims to examine UCN (University College of the North) students’ remote learning experience during the COVID-19 pandemic to provide reference for future remote education in Indigenous contexts. Survey data are used for empirical analysis of the five themes: socio-demographic contexts; social activities, stress, and well-being; academic performance; course delivery; and student support services. Transformative learning theory and Quadruple Helix Model are used as a framework to explore the breadth and depth of such five themes. As the descriptive study shows, the majority of UCN students are over 25 years old and study in their first and second year with major challenges such as Internet connectivity, private space, and interruption. Mean values reveal that the remote learning performance is determined by concerns about COVID-19 infection, mental and physical health, isolation and lack of socio-cultural activities, students’ self-preparedness and motivation, and support services. The regression analysis shows that students’ concerns about COVID-19 infection interference with course completion are directly affected by their worries about themselves or someone in their families who could be exposed to COVID-19, their mental health, and blended course delivery. Therefore, students’ remote learning performance and their well-being will be enhanced if we take into consideration improving social distancing, educational technology, and course delivery with community-university culturally responsive collaboration. The research findings and the reviewed literature attest that transformative learning theory fits UCN’s remote learning practices to meet educational goals through the university-community collaboration, which is supported by the Quadruple Helix model. As a result, such remote learning practices engage students, particularly Indigenous students, and the practices will help upgrade universities with similar attributes globally into Mode 3 university, contributing to community economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".