Bibliographic record
Abstract
In a marketized environment, ESL providers, in common with other postcompul- sory educational institutions, canvass student satisfaction with their services. While the predominant method is likely to be based on tick-box questionnaires using Likert scales that measure degrees of satisfaction, qualitative methodology is an option when rich data is desired. The well-established Critical Incident Tech- nique (CIT) is particularly useful in this regard as an exploratory methodology with potential to increase knowledge about previously unknown phenomena. A pilot study with a small sample of ESL students was set up to explore ESL student satisfaction using CIT. The data obtained were analyzed within the framework of Johnston’s (1995) quality factors, then further categorized in terms of satisfying, dissatisfying, or neutral factors. The findings provided some tentative indications of differentiation among ESL quality factors as perceived by ESL students. In- sights were obtained regarding procedural, analytical, and student response issues in the use of CIT in conjunction with satisfaction data. The outcomes supported the view that information obtained through CIT could assist ESL managers and teachers in developing and enhancing quality factors that more accurately reflect student expectations of the service. Dans un environnement commercialisé, les fournisseurs en ALS, tout comme les autres institutions postsecondaires, sondent les étudiants pour connaitre leur niveau de satisfaction avec les services qu’ils offrent. Alors que la méthode la plus couramment employée repose sur des questionnaires à choix multiples avec des échelles de Likert pour mesurer les niveaux de satisfaction, une méthodolo- gie qualitative est à envisager quand l’on désire des données approfondies. La méthode des incidents critiques (CIT) est bien établie et particulièrement utile à cet égard, étant une méthodologie d’exploration avec le potentiel d’accroitre les connaissances de phénomènes inconnus auparavant. On a mis sur pied une étude pilote avec un petit échantillon d’étudiants en ALS pour explorer, par la CIT, leur niveau de satisfaction. Les données ont été analysées dans le cadre des facteurs de qualité de Johnston (1995) et ensuite catégorisées en fonction de facteurs satisfai- sants, insatisfaisants et neutres. Les résultats fournissent des indices de différen- tiation parmi les facteurs de qualité en ALS, tels que perçus par les étudiants. De nouvelles idées sont ressorties par rapport à l’emploi de la CIT et les enjeux liés à la procédure, l’analyse et les réponses des étudiants dans le contexte de données relatives à la satisfaction. La conclusion en est que les informations découlant de la CIT pourraient appuyer les administrateurs et les enseignants dans le déve- loppement et l’amélioration des facteurs de qualité de sorte à mieux répondre aux attentes des étudiants en matière des services.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".