On-the-job e-learning: Workers' attitudes and perceptions
Bibliographic record
Abstract
The use of e-learning for on-the-job training has grown exponentially in the last decade due to it being accepted by people in charge of businesses. Few papers have explored virtual training from the workers’ standpoint, that is, the perception they have about the different training methodologies (face-to-face vs. virtual) and the attitudes they have towards on-the-job learning. Training, in this context, is an investment for both the two participating agents: businesses and workers. It seems logical that knowing the perceptions and attitudes shown by the targets of the training is, at least, as important as knowing the advantages for the companies.<br /><br />To analyse workers’ perceptions and attitudes we conducted an online survey of 2,000 employees of the leading European savings bank, CaixaBank (<a title="http://www.caixabank.com/index_en.htm" href="http://www.caixabank.com/index_en.html">http://www.caixabank.com/index_en.html</a>), on training habits, perceptions, motivations, and disincentives of undertaking face-to-face or online instruction. <br /><br />The results reveal that workers perceive e-learning as a more flexible and up-to-date training methodology. On the other hand, face-to-face training continues to be perceived as a more motivating methodology compared to virtuality and with better explanations from the course trainers. As regards motivations given by the workers when it comes to training, there are three main groups of attitudes: those which are more affective and social, those which reveal poor adaptability or fear of the new training requirements, and, finally, those linked to the knowledge society.<br /><br />Such results state that while the benefits of distance methodology can be clearly identified from the company’s point of view (i.e., as a flexible and efficient methodology to develop the employees’ skills and knowledge), from the employees’ standpoint, the advantages of virtual training are not so clear and depend to a great extent on their attitude towards the use of virtuality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".