Rapid Transition to Online Learning: Faculty Distance Training on LMS, Synchronous/Asynchronous Learning, and Computer-Assisted Assessment
Bibliographic record
Abstract
This study examines the swift shift to online learning prompted by the Covid-19 pandemic, assessing the effectiveness of a faculty distance training program based on the TMOC (Training for the Management of Online Courses) model. The training program aimed to equip faculty with essential skills for online learning, emphasizing a blend of asynchronous learning materials and synchronous support. Key components included training in the Learning Management System (LMS), video capture tools, synchronous learning platforms, and Computer-Assisted Assessment (CAA). The research surveyed a sample of faculty members (n=38), assessing their views towards the training they received. The findings highlight that faculty greatly valued personalized, real-time assistance, which proved instrumental in tackling immediate technical and pedagogical hurdles. High-quality asynchronous resources were also pivotal, offering flexibility and foundational knowledge. The training resulted in noticeable enhancements in faculty engagement and proficiency in online learning, particularly among those less familiar with digital educational methods. Qualitative feedback emphasized the significance of timely, customized support and collaborative assistance. The study underscores the imperative of holistic training programs that blend technical and pedagogical aspects to facilitate a seamless transition to online learning. These insights offer valuable guidance for institutions seeking to bolster their online education capabilities during emergency situations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".