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   West, R.; Leary, H. (Ed.)

   While most instructional design courses and much of the instructional design industry focus on ADDIE (Analysis, Design, Development, Implementation,Evaluation), approaches such as design thinking, human-centered design, and agile methods like SAM (Successive Approximation Model), have drawn attention.This chapter unpacks what we know about design thinking and presents a concise history of design thinking to situate it within the broader design research fi eld. Itthen traces its emergence in other fi elds. The chapter considers lessons for instructional designers and concludes by setting an agenda to address issues forscholarship, teaching, and practice. 

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   Interactive visualization interfaces enable users to efficiently explore, analyze, and make sense of their datasets. However, as data grows in size, it becomes increasingly challenging to build data interfaces that meet the interface designer’s desired latency expectations and resource constraints. Cloud DBMSs, while optimized for scalability, often fail to meet latency expectations, necessitating complex, bespoke query execution and optimization techniques for data interfaces. This involves manually navigating a huge optimization space that is sensitive to interface design and resource constraints, such as client vs server data and compute placement, choosing which computations are done offline vs online, and selecting from a large library of visualization-optimized data structures. This paper advocates for a Physical Visualization Design (PVD) tool that decouples interface design from system design to provide design independence. Given an interfaces underlying data flow, interactions with latency expectations, and resource constraints, PVD checks if the interface is feasible and, if so, proposes and instantiates a middleware architecture spanning the client, server, and cloud DBMS that meets the expectations. To this end, this paper presents Jade, the first prototype PVD tool that enables design independence. Jade proposes an intermediate representation called Diffplans to represent the data flows, develops cost estimation models that trade off between latency guarantees and plan feasibility, and implements an optimization framework to search for the middleware architecture that meets the guarantees. We evaluate Jade on six representative data interfaces as compared to Mosaic and Azure SQL database. We find Jade supports a wider range of interfaces, makes better use of available resources, and can meet a wider range of data, latency, and resource conditions. 

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4. Design Principles for CT Integrated Lesson Design and Implementation

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5. An Exploration of Course Design Heuristics Identified from Design Meetings, Design Artifacts, and Educator Interviews

   https://doi.org/10.18260/1-2--32060  

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   This research paper investigates differences between course design heuristics that have been identified from three distinct data sources: course design team meetings, educator interviews, and course design papers. The study of heuristics used by experts in a discipline can have several practical benefits. They can (1) be employed as tools to scaffold expert behavior among novices, (2) be translated into processes to make challenging tasks more efficient, and (3) provide deeper insights into the nature of a domain, task, or discipline. While the study of heuristics remains robust across domains, they have demonstrated differences in format and have been identified through a variety of data types. The purpose of this study is to unpack differences in heuristics independently identified through different data types in order to better understand the role these types of data can play in understanding of heuristics for course design, especially as related to engineering courses. We utilized thematic analysis to explore the patterns of differences between heuristics identified from the three settings in three related, but distinct studies. Datasets includes audio-recordings from a four-month team course redesign process, five approximately hour-long educator interviews, and 183 peer-reviewed course design papers. We identified four themes representing differences across the datasets: (1) differences in volume/frequency of heuristics, (2) differences in breadth, specificity, and conceptualizations evidenced by categories of heuristics, (3) individual heuristic specificity, and (4) locus of clarity in heuristic examples. These results inform a set of four considerations for selecting data sources for studies of heuristics within engineering course design and other domains. 

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