Exploring the Impact of Knowledge and Social Environment on Influenza Prevention and Transmission in Midwestern United States High School Students

We used data from a convenience sample of 410 Midwestern United States students from six secondary schools to develop parsimonious models for explaining and predicting precautions and illness related to influenza. Scores for knowledge and perceptions were obtained using two-parameter Item Response Theory (IRT) models. Relationships between outcome variables and predictors were verified using Pearson and Spearman correlations, and nested [student within school] fixed effects multinomial logistic regression models were specified from these using Akaike’s Information Criterion (AIC). Neural network models were then formulated as classifiers using 10-fold cross validation to predict precautions and illness. Perceived barriers against taking precautions lowered compliance with the CDC recommended preventative practices of vaccination, hand washing quality, and respiratory etiquette. Perceived complications from influenza illness improved social distancing. Knowledge of the influenza illness was a significant predictor for hand washing frequency and respiratory etiquette. Ethnicity and gender had varying effects on precautions and illness severity, as did school-level effects: enrollment size, proficiency on the state’s biology end-of-course examination, and use of free or reduced lunch. Neural networks were able to predict illness, hand hygiene, and respiratory etiquette with moderate success. Models presented may prove useful for future development of strategies aimed at mitigation of influenza in high school youths. As more data becomes available, health professionals and educators will have the opportunity to test and refine these models

Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events

Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of thetweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion. Results: Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F1-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories. Conclusions: This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats

Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia

The widespread use of smartphones and sensors has made physiology, environment, and public health notifications amenable to continuous monitoring. Personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context, converting relevant medical knowledge into actionable information for better and timely decisions. We apply these principles in the healthcare domain of dementia. Specifically, in this study we validate one of our sensor platforms to ascertain whether it will be suitable for detecting physiological changes that may help us detect changes in people with dementia. This study shows our preliminary data collection results from six healthy participants using the commercially available Hexoskin vest. The results show strong promise to derive actionable information using a combination of physiological observations from passive sensors present in the vest. The derived actionable information can help doctors determine physiological changes associated with dementia, and alert patients and caregivers to seek timely clinical assistance to improve their quality of life

Measuring Claim-Evidence-Reasoning Using Scenario-based Assessments Grounded in Real-world Issues

Improving students’ use of argumentation is front and center in the increasing emphasis on scientific practice in K-12 Science and STEM programs. We explore the construct validity of scenario-based assessments of claim-evidence-reasoning (CER) and the structure of the CER construct with respect to a learning progression framework. We also seek to understand how middle school students progress. Establishing the purpose of an argument is a competency that a majority of middle school students meet, whereas quantitative reasoning is the most difficult, and the Rasch model indicates that the competencies form a unidimensional hierarchy of skills. We also find no evidence of differential item functioning between different scenarios, suggesting that multiple scenarios can be utilized in the context of a multi-level assessment framework for measuring the impacts of learning experiences on students’ argumentation

The Impact of Study Strategies on Knowledge Growth and Summative Exam Performance in the First Year of Medical School

Although the distinction between deep and surface processing strategies, their potential to differentially impact learning, and data supporting the superiority of deep processing strategies on summative exam scores are well supported by the literature, more work is needed to understand: (1) how medical students combine study strategies into learning practices, and (2) the effectiveness of these learning practices in facilitating knowledge gains as measured by standardized test scores

Learning Biology through Innovative Curricula: A Comparison of Game- and Nongame-Based Approaches

This study explored student learning in the context of innovative biotechnology curricula and the effects of gaming as a central element of the learning experience. The quasi-experimentally designed study compared learning outcomes between two curricular approaches: one built around a computer-based game and the other built around a narrative case. The research questions addressed student learning of basic biological principles, development of interest in learning science, and how a game-based approach compared to a nongame-based approach in terms of supporting learning. The study employed a pre-post design with 1,888 high school students nested within the classes of 36 biology teachers. Results indicated that students participating in both approaches demonstrated statistically and practically significant gains on both proximal and distal assessments of biological content knowledge. Neither group demonstrated gains in science interest. The curriculum by time interaction was not statistically different, indicating that students in both groups showed similar results. Implications for game-based science learning and future research include building better awareness of technological and professional development challenges associated with implementing educational games, the need for new strategies for understanding the impacts of games for learning, and the need for cost-benefit analyses in the planning of game-based educational approaches

Learning Biology through Innovative Curricula: A Comparison of Game- and Nongame-Based Approaches

This study explored student learning in the context of innovative biotechnology curricula and the effects of gaming as a central element of the learning experience. The quasi-experimentally designed study compared learning outcomes between two curricular approaches: one built around a computer-based game and the other built around a narrative case. The research questions addressed student learning of basic biological principles, development of interest in learning science, and how a game-based approach compared to a nongame-based approach in terms of supporting learning. The study employed a pre-post design with 1,888 high school students nested within the classes of 36 biology teachers. Results indicated that students participating in both approaches demonstrated statistically and practically significant gains on both proximal and distal assessments of biological content knowledge. Neither group demonstrated gains in science interest. The curriculum by time interaction was not statistically different, indicating that students in both groups showed similar results. Implications for game-based science learning and future research include building better awareness of technological and professional development challenges associated with implementing educational games, the need for new strategies for understanding the impacts of games for learning, and the need for cost-benefit analyses in the planning of game-based educational approaches

Learning Biology through Innovative Curricula: A Comparison of Game- and Nongame-Based Approaches

This study explored student learning in the context of innovative biotechnology curricula and the effects of gaming as a central element of the learning experience. The quasi-experimentally designed study compared learning outcomes between two curricular approaches: one built around a computer-based game and the other built around a narrative case. The research questions addressed student learning of basic biological principles, development of interest in learning science, and how a game-based approach compared to a nongame-based approach in terms of supporting learning. The study employed a pre-post design with 1,888 high school students nested within the classes of 36 biology teachers. Results indicated that students participating in both approaches demonstrated statistically and practically significant gains on both proximal and distal assessments of biological content knowledge. Neither group demonstrated gains in science interest. The curriculum by time interaction was not statistically different, indicating that students in both groups showed similar results. Implications for game-based science learning and future research include building better awareness of technological and professional development challenges associated with implementing educational games, the need for new strategies for understanding the impacts of games for learning, and the need for cost-benefit analyses in the planning of game-based educational approaches

Predicting Early Indicators of Cognitive Decline From Verbal Utterances

Dementia is a group of irreversible, chronic, and progressive neurodegenerative disorders resulting in impaired memory, communication, and thought processes. In recent years, clinical research advances in brain aging have focused on the earliest clinically detectable stage of incipient dementia, commonly known as mild cognitive impairment (MCI). Currently, these disorders are diagnosed using a manual analysis of neuropsychological examinations. We measure the feasibility of using the linguistic characteristics of verbal utterances elicited during neuropsychological exams of elderly subjects to distinguish between elderly control groups, people with MCI, people diagnosed with possible Alzheimer\u27s disease (AD), and probable AD. We investigated the performance of both theory-driven psycholinguistic features and data-driven contextual language embeddings in identifying different clinically diagnosed groups. Our experiments show that a combination of contextual and psycholinguistic features extracted by a Support Vector Machine improved distinguishing the verbal utterances of elderly controls, people with MCI, possible AD, and probable AD. This is the first work to identify four clinical diagnosis groups of dementia in a highly imbalanced dataset. Our work shows that machine learning algorithms built on contextual and psycholinguistic features can learn the linguistic biomarkers from verbal utterances and assist clinical diagnosis of different stages and types of dementia, even with limited data

Linking Science and Literacy Through Multimodal Text Sets: Student Perspectives

We present findings of a mixed methods study examining the perceptions of students’ (with and without disabilities) understanding and engagement with multimodal STEM text sets. Exit slip and survey data were used to identify areas for improvement in the development of the multimodal STEM text sets for middle school students. Data were collected from 434 middle school students, 86 of whom had a disability, from six teachers’ classrooms in Spring 2021. Significant differences in perceptions of understanding of argumentation were reported between students with and without disabilities. However, ratings of the lessons and the quality of learning, as well as interest in the topic, were statistically similar. Topic modeling on the responses showed that all students expressed similar learning experiences and confusions whether or not they were students with disabilities. This supports prior work which suggests that multi-modal text sets are an effective way to motivate students with a wide range of interests and abilities to engage with scientific literacy practices