Tapping into Floor Staff: Using the knowledge of floor staff to conduct formative evaluations of exhibits in a Canadian science centre

Most science centres in Canada employ science-educated floor staff to motivate visitorsto have fun while enhancing the educational reach of the exhibits. Although bright andsensitive to visitors’ needs, floor staff are rarely consulted in the planning,implementation, and modification phases of an exhibit. Instead, many developmentteams rely on costly third-party evaluations or skip the front-end and formativeevaluations all together, leading to costly errors that could have been avoided. This studywill seek to reveal a correlation between floor staff’s perception of visitors’ interactionswith an exhibit and visitors’ actual experiences. If a correlation exists, a recommendationcould be made to encourage planning teams to include floor staff in the formative andsummative evaluations of an exhibit. This is especially relevant to science centres withlimited budgets and for whom a divide exists between floor staff and management.In this study, a formative evaluation of one exhibit was conducted, measuring both floorstaff’s perceptions of the visitor experience and visitors’ own perceptions of the exhibit.Floor staff were then trained on visitor evaluation methods. A week later, floor staff andvisitors were surveyed a second time on a different exhibit to determine whether anincrease in accuracy existed.The training session increased the specificity of the motivation and comprehensionresponses and the enthusiasm of the staff, but not their ability to predict observedbehaviours with respect to ergonomics, learning indicators, holding power, and successrates. The results revealed that although floor staff underestimated visitors’ success ratesat the exhibits, staff accurately predicted visitors’ behaviours with respect to holdingpower, ergonomics, learning indicators, motivation and comprehension, both before andafter the staff training.

Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).