Creating Self-Awareness Of Learning That Occurs In Community

Learning that occurs in naturally forming communities can be more effective if those who engage in such groups are aware of it. Adult education practitioners who work with groups have an opportunity to assist group participants realize that learning occurs through engagement with issues of importance to them. Adults may consider learning to be knowledge acquisition, but the concept of social capital can be used to help them realize another level of learning. The purpose of this paper is to raise awareness among adult education practitioners about a potential role in furthering learning that occurs in naturally forming groups

Creating Social Capital Though The Deliberative Discussion: A Case Study Of Community Dialogue.

When citizens come together to inquire about issues that matter to the community, learning may occur in these temporary learning communities. Active engagement with issues of social and political importance may increase the adult’s sense of commitment to action and further the development of a community’s social capital. Using a social capital development framework, this case describes one community’s attempt to promote and encourage citizens to engage in deliberative discussion. The case also highlights one citizen’s struggle to link discourse with community action

Should We Trust our Judgments about the Proficiency of Motivational Interviewing Counselors?A Glimpse at the Impact of Low Inter-rater Reliability

Standardized rating systems are often used to evaluate the proficiency of Motivational Interviewing (MI) counselors. The published inter-rater reliability (degree of coder agreement)  in many studies using these instruments has varied a great deal; some studies report MI proficiency scores that have only fair inter-rater reliability, and others report scores with excellent reliability. How much can we to trust the scores with fair versus excellent reliability? Using a Monte Carlo statistical simulation, we compared the impact of fair (0.50) versus excellent (0.90) reliability on the error rates of falsely judging a given counselor as MI proficient or not proficient. We found that improving the inter-rater reliability of any given score from 0.5 to 0.9 would cause a marked reduction in proficiency judgment errors, a reduction that in some MI evaluation situations would be critical. We discuss some practical tradeoffs inherent in various MI evaluation situations, and offer suggestions for applying findings from formal MI research to problems faced by real-world MI evaluators, to help them minimize the MI proficiency judgment errors bearing the greatest cost

Automated rating of patient and physician emotion in primary care visits

OBJECTIVE: Train machine learning models that automatically predict emotional valence of patient and physician in primary care visits. METHODS: Using transcripts from 353 primary care office visits with 350 patients and 84 physicians (Cook, 2002 [1], Tai-Seale et al., 2015 [2]), we developed two machine learning models (a recurrent neural network with a hierarchical structure and a logistic regression classifier) to recognize the emotional valence (positive, negative, neutral) (Posner et al., 2005 [3]) of each utterance. We examined the agreement of human-generated ratings of emotional valence with machine learning model ratings of emotion. RESULTS: The agreement of emotion ratings from the recurrent neural network model with human ratings was comparable to that of human-human inter-rater agreement. The weighted-average of the correlation coefficients for the recurrent neural network model with human raters was 0.60, and the human rater agreement was also 0.60. CONCLUSIONS: The recurrent neural network model predicted the emotional valence of patients and physicians in primary care visits with similar reliability as human raters. PRACTICE IMPLICATIONS: As the first machine learning-based evaluation of emotion recognition in primary care visit conversations, our work provides valuable baselines for future applications that might help monitor patient emotional signals, supporting physicians in empathic communication, or examining the role of emotion in patient-centered care

Providing comprehensive and consistent access to astronomical observatory archive data: the NASA archive model

Since the turn of the millennium a constant concern of astronomical archives have begun providing data to the public through standardized protocols unifying data from disparate physical sources and wavebands across the electromagnetic spectrum into an astronomical virtual observatory (VO). In October 2014, NASA began support for the NASA Astronomical Virtual Observatories (NAVO) program to coordinate the efforts of NASA astronomy archives in providing data to users through implementation of protocols agreed within the International Virtual Observatory Alliance (IVOA). A major goal of the NAVO collaboration has been to step back from a piecemeal implementation of IVOA standards and define what the appropriate presence for the US and NASA astronomy archives in the VO should be. This includes evaluating what optional capabilities in the standards need to be supported, the specific versions of standards that should be used, and returning feedback to the IVOA, to support modifications as needed. We discuss a standard archive model developed by the NAVO for data archive presence in the virtual observatory built upon a consistent framework of standards defined by the IVOA. Our standard model provides for discovery of resources through the VO registries, access to observation and object data, downloads of image and spectral data and general access to archival datasets. It defines specific protocol versions, minimum capabilities, and all dependencies. The model will evolve as the capabilities of the virtual observatory and needs of the community change