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Record W2193187970 · doi:10.18806/tesl.v32i2.1210

Using Critical Incidents to Understand ESL Student Satisfaction

2015· article· en· W2193187970 on OpenAlexvenueno aff
John Walker

Bibliographic record

VenueTESL Canada Journal · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleQuality (philosophy)PsychologyExploratory researchPedagogySocial psychologySociologySocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.126
GPT teacher head0.345
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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