About Visual Acuity and Type Design: A Protocol

In this book chapter, we provide guidelines and best practices to instructional designers working in higher education settings on how to use learning analytics to support and inform design decisions. We start by defining learning analytics and frame such a definition from a practitioner point of view. Then, we share best practices on how to use learning analytics to support and inform design decisions in designing courses in higher education. We share examples of learning analytics—through screenshots—gathered from our instructional design experience in higher education and comment on what implications such examples have on design decisions. We conclude by sharing a list of commonly available tools that support gathering learning analytics that instructional designers can put at their disposal throughout the design process, and in conducting needs analyses and formative/summative evaluations.

<div class="htmlview paragraph">A small SINDA/FLUINT logic routine was developed to improve upon standard spacecraft-to-instrument thermal model interface methodology for steady state analysis. Rather than the standard approach of providing backloads and/or conductive limits with uniform spacecraft temperatures, this methodology enables the instrument thermal engineer to make more informed design decisions by providing more information regarding the source and magnitude of the sink temperatures and backloads. The instrument thermal engineer can use the model information provided from the spacecraft thermal engineer to make more informed design decisions in subsequent analysis, and can be less dependent on the spacecraft thermal engineer.</div>

This research investigates the integration of building energy modeling (BEM) within collaborative construction projects to inform design decisions for achieving high-energy performance goals. The study aims to understand current practices, benefits, and challenges associated with this integration. Using an ethnographic case study approach focused on two high-energy performance social housing projects with integrated project delivery and integrated design processes, data were collected through direct observations, document analysis, and interviews with project team members. Design process modeling was utilized to dissect current practices, followed by a hybrid inductive and deductive thematic analysis to find challenges related to energy performance design in collaborative projects. Findings from this research revealed that BEM experts often operate in isolation, with late integration of energy models into design decisions. Compliance-centric BEM usage and challenges related to interoperability of design and BEM tools further compound the issue of seamless collaboration. However, the study highlights that early collaboration among project stakeholders emerges as a pivotal factor in informed design decisions, bridging the gap between energy modeling and design. This research provides valuable insights for practitioners seeking to optimize BEM in their design process, and offers support to policymakers aiming to enhance the role of BEM in projects.

The use of models, frameworks, and toolkits in learning design serve to support teaching practitioners in producing well-structured learning designs for students.However, existing learning design tools inherently lack an objective metric system which is able to measure the degree to which an educational design is well-formed according to either the principles of constructive alignment or more generally design practices that students find satisfactory in practice.Such a metric system, that could integrate measures of educational theory and practice, would enable teaching practitioners to make more informed design decisions such as which profile of activities/assessments to use for a particular set of learning outcomes.This paper presents the first computational intelligence tool that measures educational design quality in a way that is underpinned by both the theoretical principles of constructive alignment and how it is used in practice.Furthermore, the alignment metrics computed are calibrated by student satisfaction scores to promote those structures that are preferred in practice rather than from a theoretical standpoint thus offering more pragmatic and realistic design solutions.

<div class="section abstract"><div class="htmlview paragraph">System-level design decisions in Formula SAE (FSAE) vehicles drive all downstream subsystem designs, yet these decisions are often based on historical precedent or anecdotal evidence rather than rigorous analysis. This work presents a simulation-driven methodology to support data-informed decisions early in the design process, specifically examining how overall vehicle parameters—such as engine power, vehicle mass, aerodynamic drag and lift, wheelbase, and track width—influence performance in a representative FSAE endurance scenario.</div><div class="htmlview paragraph">Two types of lap-time simulation tools were used in this study: OpenLAP, a point-mass simulator, and ChassisSim, a transient 3D vehicle dynamics simulator that incorporates suspension geometry, yaw response, weight transfer, and steering effects. Initial simulations with OpenLAP were used to rapidly identify trends and guide early design decisions, while ChassisSim was used for detailed sensitivity analyses and to validate system-level trade-offs in a more realistic dynamic context.</div><div class="htmlview paragraph">Sensitivity results from ChassisSim revealed that vehicle mass and tire grip have the strongest influence on lap time, followed by geometric and aerodynamic parameters. While increases in engine power do contribute to faster lap times, the results indicate that performance gains are more effectively achieved by reducing vehicle mass and optimizing grip. These findings suggest that lightweight, high power-to-weight ratio powertrains that minimally compromise tire grip should be favored over maximizing engine output alone.</div><div class="htmlview paragraph">This work provides FSAE teams with a replicable framework for powertrain and chassis-level optimization through simulation. The approach not only enables teams to make more informed design decisions but also helps quantify trade-offs that directly affect dynamic performance, contributing to more competitive and efficient vehicle architectures</div></div>

The need to explicitly document design decisions has been emphasized both in research and in industry. To address design concerns, software architects and developers implicitly capture design decisions in tools such as issue management systems. These design decisions are not explicitly labeled and are not integrated with the architecture knowledge management tools. Automatically extracting design decisions will aid architectural knowledge management tools to learn from the past decisions and to guide architects while making decisions in similar context. In this paper, we propose a two-phase supervised machine learning based approach to first, automatically detect design decisions from issues and second, to automatically classify the identified design decisions into different decision categories. We have manually analyzed and labeled more than 1,500 issues from two large open source repositories and have used this dataset for generating the machine learning models. We have made the dataset publicly available that will serve as a starting point for researchers to further reference and investigate the design decision detection and classification problem. Our evaluation shows that by using linear support vector machines, we can detect design decisions with 91.29% accuracy and classify them with an accuracy of 82.79%. This provides a quantitative basis for learning from past design decisions to support stakeholders in making better and informed design decisions.

In early design stages, system architects mostly rely on estimations to make design decisions. These are based on the available information at hand and their experience. Modeling and simulation is almost exclusively applied in more detailed stages of design. In this paper we present an approach aimed at making better informed design decisions, early in the design process. Our approach focuses on giving insights in early design through simulations and models that are usually only provided in more detailed design stages. To do so, we propose a framework and address three conflicts that arise when connecting techniques from early and detailed design stages. These are dealing with uncertainty, accommodating multidisciplinary views and accounting for more divergent design space exploration strategies. The approach has been applied to a medical imaging system, to analyze a possible latency reduction. The goal of this case study was to gain realistic insight in system latency using a highly abstracted system model and a generic simulation model. Insights gained with these models confirmed that a new design reduces system latency and deals better with large variations in latency. The underlying structure of the approach has proven itself to be feasible. Further research is necessary to determine whether the approach can cover a broader range of applications and to evaluate how the full approach can be implemented. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the University of Southern California.

Using operational simulations during the early design phase has been largely neglected within the aerospace industry. This paper suggests that an operational simulation should be used twofold by designers during the design process to improve a product. First, it presents how an operational simulation can be used to react to customer specifications. Second, its active use as a design decision support tool is portrayed. Results are found by means of two case studies recreating the operational life of a Search-and-Rescue Unmanned Air Vehicle developed in parallel at the University of Southampton. The simulation's ability to act as a decision support tool is explored by conducting a fuel weight optimization. Reactive capabilities are explored by calculating the surplus value of using UAVs. This exemplifies the derivation of product specifications as the simulation reveals the value and hence usefulness of supplied customer specifications. It is shown that operational simulations benefit designers and overall product value by analysing product specifications and guiding designers to more informed design decisions.

ABSTRACTThis research maps various Integrated Design Processes (IDPs) with Danish conventional silo Design Practice as the reference point. The intention was to identify generic elements that are common among IDPs. The mapping was based on a literature study of a number of IDP guidelines. Eight IDP guides from the last two decades were selected for mapping. The Danish Description of Services functions as a typical representation of a conventional silo Design Practice (CSDP) and as a ‘scale’ against which to map the selected IDP guides. The results indicate a limited consensus on what constitutes an IDP but a possible consensus core that is shared by them all. One commonality is that technical knowledge must inform design decisions, and not simply be used to validate them, but on the other hand, it should not drive them. Another main trait is the interdisciplinary character of these processes, where several professions must be a part of the process from the beginning. The study also found that all IDP guides have a ‘black box problem’, where the desired inputs and outputs of the process are known but no explanation is given regarding the mechanisms of how the integrated design decisions are to be made or how to facilitate this decision-making in an interdisciplinary design team. These findings can explain the slow adoption of IDPs in the building industry and they can be used to improve IDPs and increase their implementation in integrated building design.

Customers for acoustic products need manufacturers and professionals to provide performance specifications to allow informed design decisions. Acoustic professionals rely upon standard tests and special labs to quantify products. Data published by the ASTM for E90 shows that an STC rating for a single construction can have a variability range of up to 7 points. Being logarithmic, this equates to a sound pressure range of more than 2×, larger than 100%. The log nature of many acoustical performance metrics hides this from most. This large variability causes challenges for the architectural acoustics community. The primary negative impact: customers do not trust the acoustical ratings manufacturers provide. This is not an issue of the manufacturers providing inaccurate information, it is an issue of under-reported and incomplete understanding of the uncertainty in the metrics themselves. For perspective, NRC measured the Young’s Modulus of OSB material to have a mean of 6.8 × 109 and a standard deviation of 1.5 × 108 (Ref: NRC IR-766, Table 22). This yields a measurement error of 3.8%. It is simple to account for this in the design process. If the metric has an uncertainty as large as the acoustic metrics, the standard paradigm for accounting for error not only breaks down, it incentivizes bad actors: cherry picking, lab shopping, etc. More open acknowledgement of the measurement error in architectural acoustic metrics would increase the confidence customers have in them and would allow designers to make more informed decisions.

Advances in computer technology over recent years now mean building simulation can be used in the design process and even in the construction and daily operation of most buildings. The techniques are sophisticated and require a good deal of expertise, so relatively few designers, builders and practitioners understand the full potential of the field even though simulation can inform design decisions, enable performance analysis and diagnostic studies. This book should provide these readers with an overview of building simulation and its current advancements, and a grasp of current limitations and future directions.To begin, the book introduces recent trends in building simulation and outlines its historic development. The book then takes the reader on a journey into three major areas of investigations: simulation with uncertainty, combined air and heat flow in whole buildings, in particular the applications of Computational Fluid Dynamics (CFD) to the built environment, and the introduction of new paradigms for the effective use of building simulation including issues of integration and potentially very significant ways for users to interact and to engage in immersed simulation.Leading experts in the field both in the US and Europe have written the chapters. The book provides a graduate-level student textbook as well as a guide to advanced methods for architects, engineers and other construction professionals.

As an architectural response to the changing climate, a wind-driven design approach is developed. By adopting the digital design tools, the wind can be integrated into the design process, from the large scale of urban planning, through the form-finding of buildings, as well as designing the texture and roughness of building surfaces. In its more extreme forms, it is even more notable, how the wind flow is shaped and influenced by architecture. Former docks in Stockholm serve as a case study site to test architectural interventions that alter the wind flow on the site. This wind-driven design approach enables the evaluation of the wind performance of various design options, which informs design decisions. Grasshopper, the graphical algorithmic plug-in for Rhinoceros, creates one working environment, where the geometry is parametrically designed, and subsequently analysed in the virtual wind tunnel through the Swift extension for Grasshopper. The Computational Fluid Dynamics analysis results retroactively help decide which of the design options performs the best in the given wind conditions.

As free and low-cost Web analytics tools become more sophisticated, libraries’ approach to user analysis can become more nuanced and precise. Tracking appropriate metrics with a well-formulated analytics program can inform design decisions, demonstrate the degree to which those decisions have succeeded, and thereby inform the next iteration in the design process. The Health Sciences Libraries of the University of Minnesota have been using Google Analytics as their primary analytics solution since 2005, and as Google has continued to add functionality and flexibility to the platform, the Health Sciences Libraries has capitalized on the opportunities made available. In this article the author outlines the Health Sciences Libraries strategy for using Google Analytics and describes several of the more novel methods they have developed, providing a roadmap for others seeking to strengthen their understanding of the behavior of users on their library's Web sites.

Cultural and technological evolution have profoundly transformed architectural simulation and visualisation, revolutionising how architects communicate and execute projects. The integration of digital tools, in particular, has advanced building behaviour prediction and decision-making capabilities during the design process, playing a pivotal role in promoting benefits for sustainable building design. However, inherent uncertainties within daylight simulation and visualisation can significantly impact the precision and credibility of the obtained results. The objective of this literature review is to examine challenges related to accuracy and validation complexities when employing digital tools in daylighting studies.However, in assessing the quality and accuracy of models, it’s crucial to consider their suitability for the intended purpose at various stages of design or research. This necessitates an initial overview of how digital tools are currently applied in daylighting studies, which helps to understand the goals of these models and ensure they are aligned with their intended applications. To address this, a systematic search conducted on the Web of Science (WoS) database yielded 1,441 studies, of which 1,102 were found relevant and employed digital tools for daylight analysis up to March 2023. This thorough analysis identified five primary thematic areas in the use of digital tools for daylighting research: Optimisation (49%), Calculation methods (17%), Assessment (13%), Accuracy (10%), and Decision-making (8%), collectively compromising 97% of the analysed articles.We subsequently concentrated on two pivotal areas, ’Calculation Methods’ and ’Accuracy’, to undertake a qualitative analysis of the discussed challenges related to accuracy linked to three main aspects: Input data, Computational process, and Output. The discussion further delves into the significance of the feedback step within this process, highlighting the complexities of validation and reviewing pertinent examples from the analysed studies.In conclusion, for those involved in education, research, and practice within the realm of (day)lighting analysis, grasping the nuances of these challenges is crucial to making more informed design decisions, particularly in Assessment and Optimisation areas, which represent a significant 62% of daylighting studies. As the field of architecture progresses, tackling these challenges becomes essential to maintaining the precision and trustworthiness of digital tools in daylighting studies, thereby contributing to the advancement of sustainable building design.

The better integration of the knowledge and expertise from different disciplines into urban design and the creation of more interdisciplinary and collaborative work processes to accommodate this have been under discussion in related research for decades. Nevertheless, many barriers preventing a seamless collaborative work flow still persist. In this paper we present an experiment taking place under real-world conditions, which outlines an alternative way for more efficient collaboration by focusing on the design process rather than the result and thus providing additional insights for all parties involved. A parametric design approach was chosen to help mediate between the areas of expertise involved supporting the smooth transition of data, the mutual translation of design feedback and better informed design decisions as an outcome. The case study presented in this paper exemplifies the application of the approach in a design project on masterplan scale integrating inputs from urban design, economics and mobility experts; and shows the opportunity for transforming the formerly segregated design process into a platform for transparent negotiations.