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1. Students’ Civic Engagement Self-Efficacy Varies Across Socioscientific Issues Contexts

   https://doi.org/10.3389/feduc.2021.628784  

   Dauer, Jenny M.; Sorensen, Amanda E.; Wilson, Jena (May 2021, Frontiers in Education)

   In STEM learning focused on science literacy, socioscientific issues instruction is often proposed as a way to bolster students’ civic engagement, however few studies in science education have explicitly examined this connection. We define civic engagement as the work of influencing legitimately public matters using means within the existing political structure. In this work we investigate students’ feelings of self-efficacy for this type of civic engagement in the context of four socioscientific issues (prairie dog conservation, food insecurity, biofuels and water conservation). This study was in the context of a large enrollment introductory science college course, where students used a structured decision-making process to examine alternative policy solutions to complex socioscientific issues. We qualitatively examined students’ response about their perception of the importance of the issue, their self-efficacy in exploring actions they could take to impact the issue, and the effectiveness of those actions. We found that students’ ideas about impact and effectiveness varied across the four different issues contexts due to students’ sense of the issues’ importance and scale. Generally, students’ ideas about actions they could take to impact the issue were narrow and rarely included political actions like voting. We also found post instruction increases in students’ civic engagement attitudes and skills related to social justice, interpersonal and problem-solving skills and political awareness. Finally, we suggest that socioscientific instruction must have an explicit connection to policy-level decisions and reveal how individual actions can influence the societal system. Our course using a structured decision-making process in the context of socioscientific issues is one model to help students make these connections. 

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2. Knowledge Annotation for Intelligent Textbooks

   https://doi.org/10.1007/s10758-021-09544-z  

   Wang, Mengdi; Chau, Hung; Thaker, Khushboo; Brusilovsky, Peter; He, Daqing (July 2021, Technology knowledge and learning)

   With the increased popularity of electronic textbooks, there is a growing interest in developing a new generation of “intelligent textbooks,” which have the ability to guide readers according to their learning goals and current knowledge. Intelligent textbooks extend regular textbooks by integrating machine-manipulable knowledge, and the most popular type of integrated knowledge is a list of relevant concepts mentioned in the textbooks. With these concepts, multiple intelligent operations, such as content linking, content recommendation, or student modeling, can be performed. However, existing automatic keyphrase extraction methods, even supervised ones, cannot deliver sufficient accuracy to be practically useful in this task. Manual annotation by experts has been demonstrated to be a preferred approach for producing high-quality labeled data for training supervised models. However, most researchers in the education domain still consider the concept annotation process as an ad-hoc activity rather than a carefully executed task, which can result in low-quality annotated data. Using the annotation of concepts for the Introduction to Information Retrieval textbook as a case study, this paper presents a knowledge engineering method to obtain reliable concept annotations. As demonstrated by the data we collected, the inter-annotator agreement gradually increased along with our procedure, and the concept annotations we produced led to better results in document linking and student modeling tasks. The contributions of our work include a validated knowledge engineering procedure, a codebook for technical concept annotation, and a set of concept annotations for the target textbook, which could be used as a gold standard in further intelligent textbook research. 

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3. Do actions reduce perceived risk? A longitudinal analysis of the relationship between risk perception and actions in response to forest disturbance in Colorado

   https://doi.org/10.1111/risa.14023  

   Qin, Hua; Vickery, Jamie; Brenkert‐Smith, Hannah; Bekee, Barituka; Prasetyo, Yanu (September 2022, Risk Analysis)

   As residents living in hazard-prone areas face on-going environmental threats, the actions they take to mitigate such risks are likely motivated by various factors. Whereas risk perception has been considered a key determinant of related behavioral responses, little is known about how risk mitigation actions influence subsequent perceived risk. In other words, do actions to prevent or mitigate risk reduce risk perception? This longitudinal study considers the dynamic relationships between risk perception and risk-mitigating behavior in the context of forest disturbance in north-central Colorado. Based on panel survey data collected in 2007 and 2018, the results provide a first look at changes in perceived forest risks as they relate to individual and community actions in response to an extensive mountain pine beetle outbreak. Analysis revealed that the perception of direct forest risks (forest fire and falling trees) increased, whereas indirect forest risk perception (concern on broader threats to local community) decreased across the two study phases. Higher individual or community activeness (level of actions) was associated with subsequent reductions in perceived forest fire risk, smaller increases in direct risk perception, and larger decreases in indirect risk perception. These findings contribute insights into the complex risk reappraisal process in forest hazard contexts, with direct implications for risk communication and management strategies. 

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4. After Violation But Before Sanction: Understanding Volunteer Moderators' Profiling Processes Toward Violators in Live Streaming Communities

   https://doi.org/10.1145/3479554  

   Cai, Jie; Wohn, Donghee Yvette (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Content moderation is an essential part of online community health and governance. While much of extant research is centered on what happens to the content, moderation also involves the management of violators. This study focuses on how moderators (mods) make decisions about their actions after the violation takes place but before the sanction by examining how they "profile" the violators. Through observations and interviews with volunteer mods on Twitch, we found that mods engage in a complex process of collaborative evidence collection and profile violators into different categories to decide the type and extent of punishment. Mods consider violators' characteristics as well as behavioral history and violation context before taking moderation action. The main purpose of the profiling was to avoid excessive punishment and aim to integrate violators more into the community. We discuss the contributions of profiling to moderation practice and suggest design mechanisms to facilitate mods' profiling processes. 

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5. Data Scavenger Hunt: Actively Authoring Data and Interpreting Movement

   Quiterio, Ashley; Worsley, Marcelo (February 2025, Data Science for Everyone)

   The showcase will display a programmable sensor in action for understanding a person’s relationship to embodied data collection practices. The activity design is called a Data Scavenger Hunt, where participants record data and search through the data to identify different actions within automated graphs. The sensor can be adapted to record many variables from acceleration to light level. Our activity is designed for 8th to 12th graders in out-of-school contexts, so that they explore their physical movement as it relates to data and subsequent visualizations of that data. With this embodied data, there is an inherent relationship to the data, and we explore how that relationship creates opportunities for learning about agency in data collection processes. As such, we highlight the process of active data production rather than passive data collection in our activity. In initial testing, we see how learners are navigating discrepancies between their experiences and the data. 

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