Bibliographic record
Abstract
A comprehensive, formal comparison of resources used to learn computer software has yet to be researched. Understanding the relative strengths and weakness of resources would provide useful guidance to teachers and students. The purpose of the current study was to explore the effectiveness of seven key resources: human assistance, the manual, the keyboard, the screen, the software (other than main menu), the software main menu, and software help. Thirty-six adults (18 male, 18 female), representing three computer ability levels (beginner, intermediate, and advanced), volunteered to think out loud while they learned, for a period of 55 minutes, the rudimentary steps (moving the cursor, using a menu, entering data) required to use a spreadsheet software package (Lotus 1-2-3). The main menu, the screen, and the manual were the most effective resources used. Human assistance produced short term gains in learning but was not significantly related to overall task performance. Searching the keyboard was frequently done, but was relatively ineffective for improving learning. Software help was the least effective learning resource. Individual differences in using resources were observed with respect to ability level and gender. Résumé : L’objet de la présente étude consistait à évaluer un cours de perfectionnement professionnel en ligne pour les enseignants qualifiés dans le domaine des technologies de l’information et de la communication et à étudier les facteurs qui ont de l’influence sur le perfectionnement professionnel en ligne. L’étude a tenu compte de méthodes quantitatives et qualitatives, notamment un sondage, un groupe de discussion et une entrevue réalisée alors que le cours était donné et environ neuf mois après la fin du cours. Les données indiquent que la prestation en ligne du cours sur le perfectionnement professionnel sur les technologies de l’information et de la communication pour les enseignants qualifiés s’est avérée une réussite. Toutefois, il a été difficile d’initier une communauté d’apprentissage au milieu de l’apprentissage en ligne. Les enseignants participant ont éprouvé de grandes difficultés à mettre en pratique dans leur enseignement ce qu’ils avaient appris. L’étude suggère que les prochaines séances de perfectionnement professionnel en ligne sur les technologies de l’information et de la communication devront comprendre des séances en personne et devront être offertes à plus d’un enseignant par école. Le perfectionnement professionnel qui vise des changements devrait être considéré comme un processus continu et appuyé alors que l’école change.
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.
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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".