Planning, Living, and Adding to our Plates: K-3 educators' Experiences of Curricula in Virtual Learning Environments (VLE)
Bibliographic record
Abstract
Virtual Learning Environments (VLE) have interested scholars since the late 20th century, with much of the research focusing on secondary and post-secondary instructors and learners (Brown, 2010; Dabbagh, 2007; Fuchs, 2020; Proserpio & Gioia, 2007). With the onset of VLEs in elementary education due to the COVID-19 pandemic and the continuation of synchronous virtual learning thereafter, VLEs have become commonplace in K-3 contexts across Ontario. Yet, as we contend with the ubiquitous nature of technology in early elementary, a paucity of literature exists regarding teachers’ and early childhood educators' (ECE) experiences of planned and lived curricula (Aperribai et al., 2020; Ferdig et al., 2020; Muldong et al., 2021) in VLEs. Adopting a narrative methodological approach, I reflected on themes unearthed through narrative interviews with five educators (Clandinin, 2006; Clandinin & Connelly, 1988, 1996, 2000). Early narrative analysis suggested four narrative threads: time, parent relationships, classroom community, teacher presence and engagement, and technological barriers. Each thread explored how teachers navigated the entanglements of planned and lived curricular experiences in VLEs. Further, the threads exposed critical elements to be considered in future VLE policy and curricular reform in the K-3 context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".