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The COVID-19 pandemic forced universities to shift their in-person lab courses to remote delivery, which presented several challenges for instructors and students. This article analyzed 33 peer-reviewed research articles to identify the various approaches taken by universities to migrate lab courses to remote platforms, as well as the difficulties encountered during the transition. The review revealed that technology and internet issues, workload constraints, academic integrity, and the overall educational experience were among the challenges faced. The authenticity and completeness of online labs were inferior to in-person labs, resulting in mixed opinions on the effectiveness of online labs. Students found labs that incorporated video recordings and simulations on a synchronous platform to be the most engaging. However, home labs provided limited hands-on experience, depending on the circumstances. Further research is required to investigate the cognitive, physical, and temporal demands posed by these technologies to develop a more compelling online lab experience.

 

Augmented reality (AR) has the potential to enhance the learning experience of students by providing collaborative, interactive, and immersive environments. This paper reports a systematic literature review focused on examining the research studies on the use of AR in higher education from January 2018 to October 2022, specifically in the context of collaborative learning. The initial search resulted in a total of 2537 studies, of which 20 were analyzed for final review. The main findings suggest that learning using AR-enabled collaborative learning benefits students’ overall knowledge gain, improves task performance, reduces task errors, and provides a positive collaboration experience in higher education settings. This article concludes by discussing the implications of these findings and their use as guidelines by educators, designers, and researchers for developing effective collaborative AR learning content.

 

The use of AI-enabled recommender systems in construction activities has the potential to improve worker performance and reduce errors; however, the accuracy of such systems in providing effective suggestions is dependent on the quality of their training data. A within-subjects experimental study was conducted using a simulated recommender system for installation tasks to investigate the effect of system reliability and construction task complexity on worker trust, workload, and performance. Results indicate that overall trust in the AI agent was higher for the highly reliable condition but remained consistent across various levels of task complexity. The workload was found to be higher for low reliability and complex conditions, and the effect of reliability on performance was influenced by task complexity. These findings offer insights for designing recommender systems to support construction workers in completing procedural tasks.

 

A social media phenomenon that has received limited research attention is the advent and propagation of viral online challenges. Several of these challenges entail self-harming behavior, which, combined with their viral nature, poses physical and psychological risks for both participants and viewers. The objective of this study is to identify the nature of what people post about the social media challenges that vary in their level of risk. To do so, we conducted a qualitative analysis of three viral social media challenges, the Blue Whale, Tide Pod, and Ice Bucket challenges, based on 180 YouTube videos, 3,607 comments on those YouTube videos, and 450 Twitter posts. We identified common themes across the YouTube videos, comments, and Twitter posts: (1) promoting education and awareness, (2) criticizing the participants, (3) providing detailed information about the participants, (4) giving viewers a tutorial on how to participate, and (5) attempting to understand this seemingly senseless online behavior. We used social norm theory to discuss what leads people to post about the challenges and how posts intended to raise awareness about harmful challenges could potentially create a contagion effect by spreading knowledge about them, thereby increasing participation. Finally, we proposed design implications that could potentially minimize the risks and propagation of harmful social media challenges. 

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. This process is performed by a trained windstorm risk engineer who physically goes to a facility to assess the wind vulnerabilities associated with it. This process is highly subjective, and the accuracy of findings depends on the experience and skillsets of the engineer. Although using sensors and automation enabled systems help engineers gather data, their ability to make sense of this information is vital. Further, their Situation Awareness (SA) can be affected by the use of such systems. Using a between-subjects experimental design, this study explored the use of various context-based visualization strategies to support the SA requirements and performance of windstorm risk engineers. The independent variable included in this study is the type of context-based visualizations used (with 3 levels: no visual aids, checklist based and predictive display based visual aids). We measured SA using SAGAT and performance using a questionnaire. SA and performance were found to be higher for the predictive display and checklist based conditions. The findings from this study will inform the design of context-based decision aids to support the SA of risk engineers.