Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods.

This article shares results from the research project “Experiential Learning Assessment in Post-Secondary Education.” The purpose of this research was to develop a further understanding of experiential learning assessment (EL) through an exploratory approach with university faculty and students. This article shares findings from the first part of a two-part research project examining the faculty experience of assessment in EL. A second research study is underway that provides the student perspective. The first phase of the research engaged University of Saskatchewan instructors with experience in experiential learning. Nine participants completed an online survey and six discussed their experiences and understanding in a focus group or an interview. Findings revealed that EL students were far more engaged with their learning than those in traditional courses. Participants applied their experience to expand upon current definitions and characteristics of EL assessment. They also shared differences with EL instruction and assessment as compared to traditional courses. Common themes identified by participants included a need for questioning and processes to integrate more EL assessment strategies into teaching and learning. Participants shared advice for those considering EL approaches in their own teachings. As EL continues to be a key area of growth for many universities and post-secondary institutions, this study contributes to the body of knowledge and appeals to faculty, designers, and others tasked with implementing effective EL.