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Enregistrement W4400482669 · doi:10.55016/ojs/cpai.v4i2.74168

ESL Student Perspectives on Problems and Solutions for Academic Integrity

2021· article· en· W4400482669 sur OpenAlexaffabout
Jim C. Hu, Chen Zhang

Notice bibliographique

RevueCanadian Perspectives on Academic Integrity · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueAcademic integrity and plagiarism
Établissements canadiensThompson Rivers University
Organismes subventionnairesnon disponible
Mots-clésAcademic integrityResearch integrityMathematics educationEngineering ethicsPsychologyComputer scienceEngineering

Résumé

récupéré en direct d'OpenAlex

While technology has made information readily available to university students, many of them have no sound understanding of how to use the sources properly, especially ESL students (Löfström & Kupila, 2013). When they use others’ ideas, text, or work without crediting the sources, they may commit either intentional or involuntary plagiarism (Camara et al, 2017). When they reuse a submitted assignment for another course improperly, they may commit self-plagiarism (APA Style, 2019), However, rather than simply punishing students for plagiarism, the universities should educate and empower students, especially ESL students, to avoid plagiarism (Khoo, 2021). Previous research has found student plagiarism to arise for such reasons as language incompetence, first culture influence, and time pressure (Camara et al., 2017; Löfström & Kupila, 2013; Shi, 2004, 2006). However, there might be other challenges ESL students encounter that are not well understood. To counter plagiarism, programs such as Turnitin have been developed to detect copying but it would be more ideal if teachers understand student needs and strategies to address them. Unfortunately, only limited research has studied these issues (Camara et al., 2017; Hu, 2001; Löfström & Kupila, 2013; Shi, 2006). Thus, this presentation reports on a study examining student perspectives on academic integrity challenges and institutional solutions. The study employed semi-structured individual in-depth qualitative interviews (Creswell, 2007; Hu, 2009) with 20 ESL students taking Academic Writing at a western Canadian university in Winter 2021. The participants were selected based on EDI (equity, diversity, and inclusiveness) principles and represented 10 countries. Some participants had completed high school and others had finished undergraduate or graduate studies in part or whole. Each interview was conducted online via Blue Jeans, lasting about an hour, and each transcript underwent member checking. The data were analyzed qualitatively to determine recurrent themes. Preliminary findings suggest that the predominant challenge of the participants is their lack of experience using citations before studying at the Canadian university. The participants generally had written either no formal essays or only opinion-based essays with no source requirement. In some cases, although the participants used sources, they were not required to cite them. In others, although they cited sources, they were not required to follow strict conventions like APA style. Because of the lack of citation experience, the participants found APA 7th edition rules hard to follow in the beginning. Even after the course, many participants still found paraphrase challenging because ESL students typically have limited vocabulary and grammatical structures, which make it difficult to rephrase the source in their own words while keeping the original meaning. A less serious challenge is to create a reference list of various types of sources in APA 7. To help students with the challenges, style templates and models are valuable, but perhaps even more valuable are interactive workshops at semester start offering explanations and opportunities for hands-on practice. Thus, a combination of resources and workshops along with improved language competence are expected to empower ESL students in academic integrity. By attending the session, participants will understand ESL student challenges for academic integrity and strategies to help students. Furthermore, they will receive a list of internet resources.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,009
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,724
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,009
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,002
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0040,022
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,064
Tête enseignante GPT0,353
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission2
Résumé présentoui

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