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Record W7161999566 · doi:10.82308/30547

The effect of the Macdonald farm-to-school summer program on children's agricultural knowledge

2018· dissertation· en· W7161999566 on OpenAlexaboutno aff
Naomi Yocheved Shalit

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureAgricultural educationDemographicsPopulationPeriod (music)Study abroad

Abstract

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As the world population continues to rise there is an increased movement of people to urban areas and a greater disconnect from rural life. Children living in urban centers may lack the opportunities to learn about agriculture, which affects their daily lives. Studies on elementary aged children's knowledge and understanding of agriculture demonstrate that children have a low level of agriculture literacy. Interestingly though, many of these same studies show that a great deal of children's information on agriculture is acquired outside of school. Consequently, many education researchers have advocated for the incorporation of informal (out-of-school) learning opportunities in agriculture into the science curriculum. In Canada, there is a growing trend of agriculture education programs at the elementary and higher education levels. However, most of the studies on the impact of these types of programs have been conducted on American (US) and European programs. It was, therefore, decided to evaluate a Canadian program: children's learning from the Farm-to-School summer program located at the McGill University Macdonald Campus Farm.The study period consisted of four 5-day sessions during August, 2016. During this period two thematic programs were offered: Plate-to-Farm and Global Food Security. Both programs were offered in both languages, with one week in English and one week in French. Children and their parents from all four summer program sessions were invited to participate in the study. Five research questions asked were: 1 - Does participation in the 5-day Farm-to-School Program improve children's agricultural knowledge? 2 - Do family demographics impact children's knowledge of agriculture (age, maternal language, ethnicity, gender, etc.) 3 - Do children's agricultural background (previous Farm-to-School experience and family agricultural background) have an impact on their agricultural knowledge? 4 - What are the parents' perceptions on how the summer program improved (or not) their children's agricultural knowledge? 5-What are the parents' perceptions on how the summer program influenced (or not) their children's agricultural behaviours? Children's knowledge was evaluated using a pre-and post-test design. Participants were separated into two age groups (6-8 years old and 9-12 years old), and administered a pre- and post-test using a clicker-based response system. Participants' parents provided demographic information, and completed a post-program survey on perceptions. All data was analyzed using SAS version 9.4.Results for the first three questions, using generalized linear mixed-model (GLIMMX) analyses showed no significant difference between the overall pre- and post-test scores. However, English-speaking children were found to have significantly higher scores compared to French-speaking, bilingual and children who spoke other languages (p<0.1). In addition, 9-12-year-olds scored significantly higher than the 6-8-year-old for pre-and post-test scores (p= 0.0562 and p=0.0628, respectively). Perhaps not surprisingly, previous Farm-to-School summer program experience was also found to have a significant effect on children's test score (p=0.012). For the last two research questions, generalized linear model analyses were conducted via the Likert-scale, using demographic and background data. The results of this study demonstrate that children's demographic and background profile significantly impact their knowledge and understanding of agriculture. As well, the demographic and background data affected parents' perceptions of their children's learning and behaviour changes. These results should be useful for future planning of the Farm-to-School summer program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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