ABSTRACT
Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored. © 2022 IEEE.
ABSTRACT
Social media are an integral part of the daily lives of today’s young generation. In addition to the positive impact on learning through these channels, there are also risks related to toxic content like “fake news” on various social media. Fake news aims to change opinions based on disinformation or misinformation supporting conspiracy theories, e.g., related to the pandemic. Fake news creators use various multimedia artifacts, including images taken from serious and valid news sources, to attract the audience’s attention. Tracking images in different contexts can give social media users important clues to distinguish fake news from credible information. We report on the development of a web-based learning environment that includes a “virtual learning companion” to help learners improve their understanding, awareness, and critical thinking concerning such social media threats. The learning environment mimics Instagram and includes toxic and non-toxic content in a controlled way. The companion is implemented as a browser plugin that communicates with students via chat. The companion poses knowledge activation questions and answers according to an underlying script. The companion offers other sources with the same image identified through Reverse Image Search (RIS). The goal is to help learners find the same image in different contexts with different textual descriptions and keywords. For this purpose, we added basic NLP mechanisms to extract keywords from these contexts, including keywords that signal persuasiveness. Currently, we evaluate the impact of this tool and the provided support in distinguishing fake or credible news. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.