The Effects of Virtual Lab Delivery on Motivation, Learning, and Academic Success
Bibliographic record
Abstract
In March 2020, a significant amount of education moved online because of the COVID-19 pandemic restrictions. Due to these restrictions, Durham College offered an alternative online synchronous option (virtual lab delivery) in September 2021 and students were given an option to select their preferred lab option for one course, Fitness Assessment 1. While online delivery has advantages (accessibility, convenience, and reduced costs), student success requires elevated levels of motivation and the capacity to self-direct learning. It is important to ensure the quality of online synchronous delivery in post-secondary education and investigate how a virtual lab option (online synchronous delivery) impacts self-directed learning (SDL), motivation, self-regulated learning (SRL), and academic success. Students were recruited from the virtual lab group (n = 13) and the in-person lab group (n = 10) and completed questionnaires at baseline (week 1) and following the course (week 14). The effect of SRL and motivation was measured by the Motivated Strategies for Learning Questionnaire (MSLQ) and the effects of SDL was measured by the Self-directed Learning Readiness Scale (SDLRS). This study also investigated the impact of delivery method on academic success (as measured by final calculated grade). Both delivery groups scored >150 on the SDLRS at both timepoints indicating a high readiness for self-directed learning. There was a significant effect of delivery method for the overall MSLQ score (p = 0.009) as well as the MSLQ learning domain (p = 0.005) with higher scores achieved by the virtual learning group. There was a trend towards higher final calculated grade for the in-person delivery group (84.3%) as compared to the virtual group (80.1%). These findings suggest that the students who selected the virtual learning option possessed a greater capacity to self-regulate their learning process using metacognitive and behavioral strategies. This has implications for post-secondary education design and implementation as certain predispositions may make some students more likely to engage in self-directed learning and students may require differential educational approaches based on these differences.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".