The Role of Trainer Behavior in End User Software Training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the factors that differentiate effective from ineffective end user software training is an under-researched topic in MIS research. Only a few studies have investigated the characteristics of effective training, and the cognitive and social processes through which they influence learning. Of these, none has focused on the role of the trainer and his or her influence on training effectiveness. Thus, the purpose of this research is to identify the behaviors that characterize effective trainers, and examine these behaviors in the context of the learning process. Fifty-three items were identified through interviews with trainers as characterizing effective trainer behavior. These items were organized using card sorting and factor analysis. Six primary categories of behavior emerged: knowledge, communication, course design, sympathy, training techniques, and class management. The prototypicality of the behaviors was also assessed, through a survey of 68 trainers. The results of the study are useful in a number of ways. First, the study provides a basis for training feedback instruments that can be used in applied settings. Second, the results provide a foundation for including trainer behavior into existing training models in a more comprehensive fashion than has been undertaken to date.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it