This study presents initial work to validate a scale designed to measure scientists’ outcome expectations in relation to public engagement. A 20-item survey was administered to a sample of 341 scientists. Graded response models were used to assess the quality of the items. Results suggest that six items provided the strongest measure of outcome expectations, with classically adequate reliability across a wide range of scientists and scores. The findings are presented in relation to the short-term outcomes of public engagement for scientists and the need for validated scales that allow for the continued study of science communication efforts.
In order to evaluate the acceptability and potential impact of the Passion-Driven Statistics curriculum, this article describes background characteristics, and course experiences and outcomes of students enrolled in the multidisciplinary, introductory, projectbased course in liberal arts colleges, large state universities, regional college/universities, and community colleges. We found that the course could be successfully delivered across these diverse educational settings. After controlling for educational setting and pre-survey responses to individual outcome measures, consistent predictors of positive course outcomes included student's initial interest in conducting research, their higher likelihood of enrolling in a statistics course if it were not required, finding the project-based course less challenging, and finding the research project more rewarding than other students. Regional college/university, and community college students reported working significantly harder in the course and finding the course more challenging than students taking the course at liberal arts colleges or state universities. Students from liberal arts colleges generally reported more positive course experiences than students from other educational settings. However, when compared to students from both liberal arts colleges and large state universities, those from regional colleges/universities reported being more likely to have learned more in the project-based course than in other college courses they had taken. Taken together, the project-based course was successfully delivered across diverse post-secondary educational settings and provides a promising model for getting students hooked on the power and excitement of applied statistics.
(ABSTRACT)With the rise of big data, it is becoming increasingly important to educate students about data analytics. In particular, students without a strong mathematical background usually have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this thesis, we present an embodied approach for visual analytics designed to teach students exploring alternative 2D projections of high dimensional data points using weighted multidimensional scaling. We proposed a novel concept, Be the Data, and its application to explore the possibilities of using human's embodied resources to learn from high dimensional data. In our system, each student embodies a data point and the position of students in a physical space represents a 2D projection of the high-dimensional data.Students physically moves in a room with respect to others to interact with alternative projections and receive visual feedback. We conducted educational workshops with students inexperienced in relevant data analytical methods. Our findings indicate that the students were able to learn about high-dimensional data and data analysis process despite their low level of knowledge about the complex analytical methods. Similarly, we applied Be the Data to social meetings. We used the same techniques to analyze and display social-cluster related information to facilitate social interactions in real time.
Participants information 323.2 A quantitative summary of students' understanding about the key concepts (i.e., variable, relative distance, dimension reduction, data exploration), interests, and confidence towards learning high-dimensional data before and after the workshop. DR stands for dimension reduction. Column 3 and 4 are observed proportion of correct answers for the pre and post surveys. Column 5 and 6 are the expected difference and the credible interval for the difference in proportions. Column 7 are the p-values from a two-tailed two sample t-test. The * in column 6 and 7 flags questions when there is important difference in pre and post. 373.3 A summary of students' embodied interaction during the activity 48 4.1 A portion of the high-dimensional dataset that describes participants in one social meeting.
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