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Enregistrement W3131195351 · doi:10.48336/wd9e-f011

Perceptions and expectations of the technological proficiency levels of university business school graduates: representations of graduates and employers.

2021· dissertation· en· W3131195351 sur OpenAlexaffabout
Muhammad Khurram

Notice bibliographique

RevueMemorial University Research Repository (Memorial University) · 2021
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueHigher Education and Employability
Établissements canadiensMemorial University of Newfoundland
Organismes subventionnairesnon disponible
Mots-clésPerceptionWork (physics)Medical educationValue (mathematics)Computer literacyPsychologyMathematics educationEngineeringComputer scienceMedicine

Résumé

récupéré en direct d'OpenAlex

The present study explores business school graduates’ experiences in acquiring computing skills, as well as employers’ experiences with the computer proficiency of recent business school graduates. Following on the work of Gibbs, Steel & Kuiper (2011) this study examines the experiences of business graduates from Memorial University of Newfoundland, Canada and local employers who hire these graduates. A qualitative research design was employed, and semi-structured interviews were conducted with eighteen (18) participants: twelve (12) business graduates and six (6) employers. Results were divided into three major themes: (1) graduate perceptions of their acquisition of computing skills within and outside their post-secondary program; (2) perceptions of the roles and responsibilities of business schools in the acquisition of computing skills, and; (3) employers’ perspectives on specific aspects of graduate computing skills. The findings show that graduates were positive in their appraisal of the value of computing skills in general, and have high levels of confidence about their ICT skills; however, the acquisition of such skills was found to be primarily learned informally, self-taught, or learned during work terms. Some graduates had little or no formal computing training and most participants had no more than a vague awareness of the scope and breadth of computing skills needed in a professional work environment. Thus, there appears to be some misalignment between workplace computer skill requirements, and program objectives. Employers perceive an appropriate balance between the computer proficiency of business graduates and the skills they need for the workplace. The data relating to skill deficits suggest that they are more prevalent in the areas of writing and communication – including grammar and spelling, and business writing. These findings raise questions about a potential gap or a weakness in the current approach to university education for business students. Although there is wide recognition that the primary aim of university business degree programs falls outside of technical training, there is clearly room for a more standardized approach to the teaching and assessment of computer skills. The findings suggest the need for better coordination between business schools and industry employers to better align the needs and expectations of employers with the goals and objectives of business programs, Recommendations are provided for greater collaboration between business graduates’ competencies, employer expectations and the ability of business schools to help standardize and assess computer skills and language proficiencies of their graduates. Finally, more research is necessary to study and help establish effective preparation, training, and intervention strategies for business school students with respect to ever-evolving technological changes and new business applications.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesÉtudes des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,459
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,004
Études des sciences et des technologies0,0020,004
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
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 Opus0,054
Tête enseignante GPT0,335
Écart entre enseignants0,281 · 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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Même revueMemorial University Research Repository (Memorial University)Même sujetHigher Education and EmployabilityTravaux en français237 207