Les déterminants technologiques de la persévérance des étudiants dans les cours à distance de niveau collégial : Les modalités de cours jouent-elles un rôle?
Bibliographic record
Abstract
L’objectif de cette étude est d’identifier et d’analyser les déterminants technologiques de la persévérance dans les cours à distance de niveau collégial (n=61), issus du modèle Unified theory of acceptance and use of technology (UTAUT). Les résultats des analyses par équations structurelles (Partial Least Square) indiquent que parmi ces derniers déterminants, seulement les conditions facilitantes ont un impact significatif et positif sur l’intention comportementale d’utiliser les technologies des cours à distance (R2=54%) définie comme l’intention de l’étudiant de réaliser ce comportement, qui a son tour a un effet significatif et positif sur la persévérance, définie par l’intention de finir le cours à distance auquel l’étudiant est inscrit (R2=14,2%) et par l’intention de s’inscrire dans le futur dans d’autres cours à distance (R2=65%). Les analyses des ANOVA font ressortir des différences significatives entre les groupes d’étudiants assignés à des modalités différentes de cours à distance sur tous les facteurs technologiques laissant présager que des analyses différenciées, selon la modalité de cours, devraient être envisagées dans le futur. This study aims to identify and analyze the technological determinants of persistence in college distance education courses (N=61), derived from the Unified theory of acceptance and use of technology (UTAUT) model. The results of the structural equation analyses (Partial Least Square) revealed that among these determinants, only facilitating conditions have a significant and positive impact on the behavioural intention to use distance learning technologies (R2 = 54%) defined as the student’s intention to display this behaviour. Moreover, behavioral intention to use distance learning technologies has a significant and positive effect on persistence, defined as the intention to finish the distance education course in which the student is enrolled (R2 = 14.2 %) and the intention to enroll in other distance education courses in the future (R2 = 65%). The ANOVA analyses revealed significant differences between the groups of students assigned to different courses delivery modes on all the technological factors, suggesting that differentiated analyses, depending on the course delivery mode, should be performed in the future.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".