Evaluating a swine biosecurity website as an education and outreach tool and identifying best practices for end-user engagement: A learning analytics approach
Notice bibliographique
Résumé
INTRODUCTION: Implementing biosecurity measures on commercial and small-scale swine farms is an ongoing effort to prevent the introduction and spread of infectious diseases. Educating and training swine producers on effective on-farm biosecurity practices is imperative. This study aims to assess a swine biosecurity website as an outreach tool and identify best practices for end-user engagement by tracking and analyzing data on user demographics, engagement, and interaction. METHODS: User data for a swine biosecurity website were recorded between 5th July 2022 and 31st December 2023 using Google Analytics. A direct interaction between RStudio software and Google Analytics facilitated data export and analysis on user demographics and website traffic. A multivariable negative binomial regression model assessed associations between website event counts (outcome) and predictors representing the type of devices used to access the website and how the website was found. A multivariable linear regression model evaluated associations between the previously described predictor variables and the duration for which the users engaged with the website (outcome). The number of users and event counts in each state was illustrated in choropleth maps, and the Local Moran's I method was used to identify states with a high number of users and event counts to evaluate the website's outreach across the United States of America (US) and worldwide. RESULTS: Google Analytics reported 768 users with an aggregated event count of 9643. Users were from 78 countries, of which the most users were from the US (708), the Philippines (202), and Canada (49). The website users were distributed across all age categories. The "biosecurity checklist" and "biosecurity protocol of entering the swine farm" were the most downloaded infographics. The website engagement (total events and engagement duration) was significantly higher if users accessed the website on desktop computers compared to mobile phones and tablets, and was higher for users accessing the website through direct links, and search engines. In the US, local clusters of high website users were identified in leading swine production states, including Iowa, Minnesota, Illinois, Nebraska, Indiana, and Missouri. CONCLUSION: The study findings support the utility of a web-based learning environment, which can provide swine biosecurity education and resources to a broad audience. The website traffic data obtained through Google Analytics helped examine the website users' behavioral patterns, preferences, and engagement tendencies, which can be used to enhance the website in the future. The website tracking and analytical methods presented in this study can be applied to other educational websites.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».