The emerging field of SoTL is an inherently interdisciplinary endeavor, embracing a diverse range of research methods. It desires to be hospitable to a range of disciplinary differences in world views. However, the field lacks coherence in its conceptualization and communication.Ongoing debates in the community concern the use of theory, as well as definitional questions of what constitutes SoTL and the nature of its purpose. This article offers a framework for conceptualizing the field, which attempts to broadly delineate the available theories underlying and methodologies appropriate to studying teaching and learning, while intending to be hospitable to a broad range of diverse disciplines. Further, the framework illustrates the tacit links between learning theories and methodologies, serving as a guide to potential approaches to SoTL work. The framework is illustrated with example SoTL studies. It is hoped that the framework will help to broaden the types of questions being investigated in the field, ground those investigations in appropriate theories and methodologies, and build interdisciplinary communication and understanding in the "trading zone" that is SoTL.

Faculty members from five years of an annual Scholarship of Teaching and Learning (SoTL) development program were invited to participate in a study about the impact of SoTL on their teaching, scholarship, and career trajectory. During semi-structured interviews, many expressed feeling discomfort during their journey into SoTL. A qualitative analysis using the constant comparison method showed that this discomfort was sometimes due to contrasts between SoTL and their discipline's epistemology, as well as challenges to their identity as a teacher, researcher, and a colleague. We conclude with suggestions for how faculty development and multidisciplinary SoTL communities of practice can be planned and managed.

There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and deep learning with the activities. A mixed-methods design was used to explore students’ interactions with the online activities. This method integrated learning analytics data with responses from 63 survey, nine interview, and 16 focus group participants. We found that students used a combination of mobile devices to access the online learning activities across a variety of locations during opportunistic study sessions in order to fit study into their daily routines. The online activities were perceived positively, facilitating affective, cognitive, and behavioural engagement as well as stimulating deep learning. Activities that were authentic, promoted problem-solving, applied theory to real-life scenarios, and increased students’ feelings of being supported were perceived as most beneficial to learning. These findings have implications for the future design of online activities, where activities need to accommodate students’ need for flexibility as students’ study habits become more mobile.

Advanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

This chapter briefly describes the SoTL research development program and context at Mount Royal University, reports initial results from a study of the program's impact on participants’ teaching and scholarly activities, and situates the findings regarding individual impact, department‐level impact, institution‐level impact, and discipline‐level impact within the current literature and the Canadian context.