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1. Impact of Youth and Adult Informal Science Educators on Youth Learning at Exhibits

   https://doi.org/10.1080/10645578.2021.1930467  

   McGuire, L. ; Hoffman, A. J. ; Mulvey, K. L. ; Winterbottom, M. ; Balkwill, F. ; Burns, K. P. ; Chatton, M. ; Drews, M. ; Eaves, N. ; Fields, G. E. ; et al ( July 2021 , Visitor Studies)

   The impact of educators in informal science learning sites (ISLS) remains understudied from the perspective of youth visitors. Less is known about whether engagement with educators differs based on the age and gender of both visitor and educator. Here, visitors (5–17 years old) to six ISLS in the United States and United Kingdom (n¼488, female n¼244) were surveyed following an interaction with either a youth (14–18 -years old) or adult educator (19þ years old). For participants who reported lower interest in the exhibit, more educator engagement was related to greater self-reported learning. Younger children and adolescents reported more engagement with an adult educator, whereas engagement in middle childhood did not differ based on educator age. Participants in middle childhood showed a trend toward answering more conceptual knowledge questions correctly following an interaction with a youth educator. Together, these findings emphasize the promise of tailoring educator experiences to visitor demographics. 

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2. Getting Meta: A Multimodal Approach for Detecting Unsafe Conversations within Instagram Direct Messages of Youth

   https://doi.org/10.1145/3579608  

   Ali, Shiza ; Razi, Afsaneh ; Kim, Seunghyun ; Alsoubai, Ashwaq ; Ling, Chen ; De Choudhury, Munmun ; Wisniewski, Pamela J. ; Stringhini, Gianluca ( April 2023 , Proceedings of the ACM on Human-Computer Interaction)

   Instagram, one of the most popular social media platforms among youth, has recently come under scrutiny for potentially being harmful to the safety and well-being of our younger generations. Automated approaches for risk detection may be one way to help mitigate some of these risks if such algorithms are both accurate and contextual to the types of online harms youth face on social media platforms. However, the imminent switch by Instagram to end-to-end encryption for private conversations will limit the type of data that will be available to the platform to detect and mitigate such risks. In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth. 

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3. The Usual, Racialized, Suspects: The Consequence of Police Contacts with Black and White Youth on Adult Arrest

   https://doi.org/10.1093/socpro/spaa042  

   McGlynn-Wright, Anne ; Crutchfield, Robert D ; Skinner, Martie L ; Haggerty, Kevin P ( October 2020 , Social Problems)

   null (Ed.)

   Abstract Research on race and policing indicates that Black Americans experience a greater frequency of police contacts, discretionary stops, and police harassment when stops occur. Yet, studies examining the long-term consequences of police contact with young people have not examined whether criminal justice consequences of police contact differ by race. We address this issue by examining whether police encounters with children and adolescents predict arrest in young adulthood and if these effects are the same for Black and White individuals. The paper uses longitudinal survey data from 331 Black and White respondents enrolled in the Seattle Public School District as eighth graders in 2001 and 2002. Our findings indicate that police encounters in childhood increase the risk of arrest in young adulthood for Black but not White respondents. Black respondents who experience contact with the police by the eighth grade have eleven times greater odds of being arrested when they are 20 years old than their White counterparts. 

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4. Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities

   https://doi.org/10.1007/s41666-023-00149-y  

   Kaelin, Vera C. ; Boyd, Andrew D. ; Werler, Martha M. ; Parde, Natalie ; Khetani, Mary A. ( October 2023 , Journal of Healthcare Informatics Research)

   Abstract Customizing participation-focused pediatric rehabilitation interventions is an important but also complex and potentially resource intensive process, which may benefit from automated and simplified steps. This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11–17 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, naïve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY). 

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5. Adventure Recreation Camp: An Informal Education Methodology Used to Educate Youth

   https://doi.org/10.18666/JPRA-2023-11581  

   Burns, Robert C. ; Janowicz, Lauren ; Moreira, Jasmine Cardozo ; Pierskalla, Chad ; Andrew, Ross ; Smaldone, Dave ( May 2023 , Journal of Park and Recreation Administration)

   This study focuses on the informal learning methods that take place in an 8-day structured summer camp. The Appalachian GeoSTEMCamp infused outdoor adventure recreation with geology using an informal-formal learning methodology. The camp was designed to instruct middle school/high school students on earth and environmental sciences in a way that is challenging and interesting, promoting critical and creative thinking. The data adds to a growing body of literature suggesting informal education has an established place in STEM education.

    

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