Augmented Reality in Design Education: Landscape Architecture Studies as AR Experience

The rapid and ongoing development of digital technologies continues to create new opportunities for education. Over the last decade this has enabled the establishment of blended learning approaches and online education. More recently, Augmented Reality (AR) has emerged as a unique technology that can transform learning experiences across diverse disciplines. This article outlines the development of an AR prototype, Master of Time, which was created to educate first year students and non‐designers on the foundational principles of landscape architecture. This study examines the learning potential and benefits of AR technology with a focus on creating new practices in digital storytelling across situated experiences. In outlining project outcomes, the authors propose a series of critical design principles, strategies and methodologies for educators to apply when developing AR learning experiences across disciplines. Included within this is a framework for transdisciplinary and co‐design collaboration, which is essential for educators working in the forefront of learning technologies.     

The Frontier of Web-based Instruction

In spite of advances in course design software and experience with online offerings, the question of how web-based education affects teaching and learning remains largely unanswered, and the terrain of online learning remains largely unmapped. In this paper, through the stories of 10 faculties of education and one computer science instructor in Ontario, we map out a small piece of the frontier of web-based course delivery and plot a course for future exploration. We argue that most existing offerings take the form of a Lone Ranger or Greenhorn approach but that there is a need to move toward a strategic institutional Wagon Train approach that consolidates and integrates support for online teaching and learning. We argue, as well, that failures in online instruction cannot simply be attributed to improper instruction or inappropriate course design. Rather, at least part of the problem locates in the ways in which conversation is structured in the online environment.     

Methods of Instruction and Learning Outcomes: A Theoretical Analysis of Two Approaches in an Introductory Information Technology Course*

In introductory information technology (IT) courses, communicating technical concepts so that they can be comprehended by all students, technical and nontechnical, has been a concern. Another challenge in such courses is to teach the real‐world applicability of technical concepts. In this conceptual article, we focus on a relatively unexplored issue in IT education—which instructional method is more effective in improving the learning outcomes of all students taking introductory IT courses. In doing so, we consider two instructional methods, lecture and multimedia case studies, and argue that either of these instructional methods, adopted singly, will be perceived by students as less effective in accomplishing learning outcomes than adopting a combination of the two instructional methods. Our arguments both augment existing knowledge about the differential influence of lecture and multimedia case studies on students’ learning outcomes and questions the wisdom of adopting either of these methods singly in introductory IT courses. We derive insights from the literature and anecdotal evidence, presented as four propositions, which illustrate the relationship between the two instructional methods and the specific learning outcomes students perceive they affect.     

A comparison of accelerometry analysis methods for physical activity in older adult women and associations with health outcomes over time

ABSTRACT

This study compared five different methods for analyzing accelerometer-measured physical activity (PA) in older adults and assessed the relationship between changes in PA and changes in physical function and depressive symptoms for each method. Older adult females (N = 144, M_age = 83.3 ± 6.4yrs) wore hip accelerometers for six days and completed measures of physical function and depressive symptoms at baseline and six months. Accelerometry data were processed by five methods to estimate PA: 1041 vertical axis cut-point, 15-second vector magnitude (VM) cut-point, 1-second VM algorithm (Activity Index (AI)), machine learned walking algorithm, and individualized cut-point derived from a 400-meter walk. Generalized estimating equations compared PA minutes across methods and showed significant differences between some methods but not others; methods estimated 6-month changes in PA ranging from 4 minutes to over 20 minutes. Linear mixed models for each method tested associations between changes in PA and health. All methods, except the individualized cut-point, had a significant relationship between change in PA and improved physical function and depressive symptoms. This study is among the first to compare accelerometry processing methods and their relationship to health. It is important to recognize the differences in PA estimates and relationship to health outcomes based on data processing method.

Abbreviation: Machine Learning (ML); Short Physical Performance Battery (SPPB); Center of Epidemiologic Studies Depression Scale (CES-D); Physical Activity (PA); Activity Index (AI); Activities of Daily Living (ADL)