Evaluating a swine biosecurity website as an education and outreach tool and identifying best practices for end-user engagement: A learning analytics approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".