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1. A Review of Competency‐Based Learning: Tools, Assessments, and Recommendations

   https://doi.org/10.1002/jee.20180  

   Henri, Maria ; Johnson, Michael D. ; Nepal, Bimal ( November 2017 , Journal of Engineering Education)

   Over the past decade, there has been a shift in science, technology, engineering and math education, especially in engineering, towards a competency‐based pedagogy. Competency‐based learning (CBL) is an outcome‐based, student‐centered form of instruction where students progress to more advanced work upon mastering the necessary prerequisite content and skills. Many articles have been published on the implementation of CBL in engineering higher education; however, the literature lacks a systematic review that summarizes prior work to inform both future research and practice.

   The purpose of this review is to integrate previous literature as well as identify gaps in competency‐based engineering higher education research. It summarizes the different approaches for implementing CBL, the effects of the pedagogy on student outcomes, tools to enhance its effectiveness, and assessment strategies. In addition, suggestions and recommendations for future research are provided.

   Engineering education articles were obtained from several EBSCO educational databases. The search was limited to articles published from 2005‐2015, and inclusion criteria consisted of peer‐reviewed journal articles that address the use of CBL in engineering higher education. Articles were then classified into several categories, summarized, and evaluated.

   Theoretical and applied perspectives are provided that address both the theoretical basis for the effectiveness of CBL and practical aspects of implementing successful CBL instruction in engineering education. There are gaps in the literature regarding how CBL programs should be structured and assessed. Future research directions include empirical quantitative evaluation of CBL's pedagogical effectiveness and the use of CBL for teaching professional skills.

    

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2. Assessing changes in global fire regimes

   https://doi.org/10.1186/s42408-023-00237-9  

   Sayedi, Sayedeh Sara ; Abbott, Benjamin W. ; Vannière, Boris ; Leys, Bérangère ; Colombaroli, Daniele ; Romera, Graciela Gil ; Słowiński, Michał ; Aleman, Julie C. ; Blarquez, Olivier ; Feurdean, Angelica ; et al ( February 2024 , Fire Ecology)

   The global human footprint has fundamentally altered wildfire regimes, creating serious consequences for human health, biodiversity, and climate. However, it remains difficult to project how long-term interactions among land use, management, and climate change will affect fire behavior, representing a key knowledge gap for sustainable management. We used expert assessment to combine opinions about past and future fire regimes from 99 wildfire researchers. We asked for quantitative and qualitative assessments of the frequency, type, and implications of fire regime change from the beginning of the Holocene through the year 2300.

   Respondents indicated some direct human influence on wildfire since at least ~ 12,000 years BP, though natural climate variability remained the dominant driver of fire regime change until around 5,000 years BP, for most study regions. Responses suggested a ten-fold increase in the frequency of fire regime change during the last 250 years compared with the rest of the Holocene, corresponding first with the intensification and extensification of land use and later with anthropogenic climate change. Looking to the future, fire regimes were predicted to intensify, with increases in frequency, severity, and size in all biomes except grassland ecosystems. Fire regimes showed different climate sensitivities across biomes, but the likelihood of fire regime change increased with higher warming scenarios for all biomes. Biodiversity, carbon storage, and other ecosystem services were predicted to decrease for most biomes under higher emission scenarios. We present recommendations for adaptation and mitigation under emerging fire regimes, while recognizing that management options are constrained under higher emission scenarios.

   The influence of humans on wildfire regimes has increased over the last two centuries. The perspective gained from past fires should be considered in land and fire management strategies, but novel fire behavior is likely given the unprecedented human disruption of plant communities, climate, and other factors. Future fire regimes are likely to degrade key ecosystem services, unless climate change is aggressively mitigated. Expert assessment complements empirical data and modeling, providing a broader perspective of fire science to inform decision making and future research priorities.

    

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3. Participatory Design in the Classroom: Exploring the Design of an Autonomous Vehicle Human-Machine Interface with a Visually Impaired Co-Designer

   Huff, E. ; Lucaites, K. ; Roberts, A ; Brinkley, J ( January 2020 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Self-driving vehicles are the latest innovation in improving personal mobility and road safety by removing arguably error-prone humans from driving-related tasks. Such advances can prove especially beneficial for people who are blind or have low vision who cannot legally operate conventional motor vehicles. Missing from the related literature, we argue, are studies that describe strategies for vehicle design for these persons. We present a case study of the participatory design of a prototype for a self-driving vehicle human-machine interface (HMI) for a graduate-level course on inclusive design and accessible technology. We reflect on the process of working alongside a co-designer, a person with a visual disability, to identify user needs, define design ideas, and produce a low-fidelity prototype for the HMI. This paper may benefit researchers interested in using a similar approach for designing accessible autonomous vehicle technology. INTRODUCTION The rise of autonomous vehicles (AVs) may prove to be one of the most significant innovations in personal mobility of the past century. Advances in automated vehicle technology and advanced driver assistance systems (ADAS) specifically, may have a significant impact on road safety and a reduction in vehicle accidents (Brinkley et al., 2017; Dearen, 2018). According to the Department of Transportation (DoT), automated vehicles could help reduce road accidents caused by human error by as much as 94% (SAE International, n.d.). In addition to reducing traffic accidents and saving lives and property, autonomous vehicles may also prove to be of significant value to persons who cannot otherwise operate conventional motor vehicles. AVs may provide the necessary mobility, for instance, to help create new employment opportunities for nearly 40 million Americans with disabilities (Claypool et al., 2017; Guiding Eyes for the Blind, 2019), for instance. Advocates for the visually impaired specifically have expressed how “transformative” this technology can be for those who are blind or have significant low vision (Winter, 2015); persons who cannot otherwise legally operate a motor vehicle. While autonomous vehicles have the potential to break down transportation 

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4. Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review

   https://doi.org/10.2196/25745  

   Kaelin, Vera C ; Valizadeh, Mina ; Salgado, Zurisadai ; Parde, Natalie ; Khetani, Mary A ( January 2021 , Journal of Medical Internet Research)

   Background In the last decade, there has been a rapid increase in research on the use of artificial intelligence (AI) to improve child and youth participation in daily life activities, which is a key rehabilitation outcome. However, existing reviews place variable focus on participation, are narrow in scope, and are restricted to select diagnoses, hindering interpretability regarding the existing scope of AI applications that target the participation of children and youth in a pediatric rehabilitation setting. Objective The aim of this scoping review is to examine how AI is integrated into pediatric rehabilitation interventions targeting the participation of children and youth with disabilities or other diagnosed health conditions in valued activities. Methods We conducted a comprehensive literature search using established Applied Health Sciences and Computer Science databases. Two independent researchers screened and selected the studies based on a systematic procedure. Inclusion criteria were as follows: participation was an explicit study aim or outcome or the targeted focus of the AI application; AI was applied as part of the provided and tested intervention; children or youth with a disability or other diagnosed health conditions were the focus of either the study or AI application or both; and the study was published in English. Data were mapped according to the types of AI, the mode of delivery, the type of personalization, and whether the intervention addressed individual goal-setting. Results The literature search identified 3029 documents, of which 94 met the inclusion criteria. Most of the included studies used multiple applications of AI with the highest prevalence of robotics (72/94, 77%) and human-machine interaction (51/94, 54%). Regarding mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%), and only 11% (10/94) were delivered remotely. Most interventions were tailored to groups of individuals (93/94, 99%). Only 1% (1/94) of interventions was tailored to patients’ individually reported participation needs, and only one intervention (1/94, 1%) described individual goal-setting as part of their therapy process or intervention planning. Conclusions There is an increasing amount of research on interventions using AI to target the participation of children and youth with disabilities or other diagnosed health conditions, supporting the potential of using AI in pediatric rehabilitation. On the basis of our results, 3 major gaps for further research and development were identified: a lack of remotely delivered participation-focused interventions using AI; a lack of individual goal-setting integrated in interventions; and a lack of interventions tailored to individually reported participation needs of children, youth, or families. 

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5. Augmented driver behavior models for high‐fidelity simulation study of crash detection algorithms

   https://doi.org/10.1049/itr2.12373  

   Jami, Ahura ; Razzaghpour, Mahdi ; Alnuweiri, Hussein ; Fallah, Yaser P. ( May 2023 , IET Intelligent Transport Systems)

   Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) requires a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time‐consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, a significant augmentation of the current models used in simulators for driver behavior is presented. In this paper, a simulation framework for a hybrid transportation system is presented that includes both human‐driven and automated vehicles. In addition, the human driving task is decomposed and a modular approach is offered to simulate a large‐scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human‐interpretable system that can be tuned to represent different classes of drivers. Additionally, a large driving dataset is analyzed to extract expressive parameters that would best describe different driving characteristics. Finally, a similarly dense traffic scenario is recreated within the simulator and a thorough analysis of various human‐specific and system‐specific factors is conducted, studying their effect on traffic network performance and safety.

    

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