Using ConceptGrid as an easy authoring technique to check natural language responses

ConceptGrid provides a template-style approach to check natural language responses by students using a model-tracing style intelligent tutoring system. The tutor-author creates, using a web-based authoring system, a latticestyle structure that contains the set of required concepts that need to be in a student response. The author can also create just-in-time feedback based on the concepts present or absent in the student\u27s response. ConceptGrid is integrated within the xPST authoring tool and was tested in two experiments, both of which show the efficacy of the technique to check student answers. The first study tested the tutor\u27s effectiveness overall in the domain of statistics. The second study investigated ConceptGrid\u27s use by non-programmers and non-cognitive scientists. ConceptGrid extends existing capabilities for authoring of intelligent tutors by using this template-based approach for checking sentence-length natural language input

BCHS 2524- Overview of Minority Health and Health Disparities in the US

Understanding health disparities involves a critical analysis of historical, political, economic, social, cultural, and environmental conditions that have produced an inequitable health status for racial and ethnic minorities in the United States. While we also recognize that disparities exist along socio-economic status, gender, sexual orientation and other factors, this class will focus on disparities in racial and ethnic minority communities. Issues of gender, SES and other factors will be examined as they intersect race and ethnicity, and further influence disparities in health. Minority health and health disparities have gained considerable attention from the recent publication of Healthy People 2010 Report, which lists as its two goals: 1) improve the quality of life for all citizens, and 2) eliminate health disparities. The purpose of this class is to introduce basic issues that underlie health disparities. We will gain a better understanding of the relationships of social and environmental phenomena and the health of minority communities. This course will include current literature and foster discussions that will examine health disparities, explore social and environmental determinants of those disparities, critically review measurement issues, and determine public health’s response to these disparities. Students should seek to critically reflect on their personal and professional roles in eliminating health disparities. By the end of the course, students will be able to

Overview of Minority Health and Health Disparities in the US

Understanding health disparities involves a critical analysis of historical, political, economic, social, cultural, and environmental conditions that have produced an inequitable health status for racial and ethnic minorities in the United States. While we also recognize that disparities exist along socio-economic status, gender, sexual orientation and other factors, this class will focus on disparities in racial and ethnic minority communities. Issues of gender, SES and other factors will be examined as they intersect race and ethnicity, and further influence disparities in health. Minority health and health disparities have gained considerable attention from the recent publication of Healthy People 2010 Report, which lists as its two goals: 1) improve the quality of life for all citizens, and 2) eliminate health disparities. The purpose of this class is to introduce basic issues that underlie health disparities. We will gain a better understanding of the relationships of social and environmental phenomena and the health of minority communities. This course will include current literature and foster discussions that will examine health disparities, explore social and environmental determinants of those disparities, critically review measurement issues, and determine public health’s response to these disparities. Students should seek to critically reflect on their personal and professional roles in eliminating health disparities. By the end of the course, students will be able to

A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?

Background Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools. Discussion We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption. It is important that reviews produced using automation tools are considered non-inferior or superior to current practice. However, this standard will likely not be sufficient to lead to widespread adoption. As with many technologies, it is important that reviewers see “others” in the review community using automation tools. Adoption will also be slow if the automation tools are not compatible with workflows and tasks currently used to produce reviews. Many automation tools being developed for systematic reviews mimic classification problems. Therefore, the evidence that these automation tools are non-inferior or superior can be presented using methods similar to diagnostic test evaluations, i.e., precision and recall compared to a human reviewer. However, the assessment of automation tools does present unique challenges for investigators and systematic reviewers, including the need to clarify which metrics are of interest to the systematic review community and the unique documentation challenges for reproducible software experiments. Conclusion We discuss adoption barriers with the goal of providing tool developers with guidance as to how to design and report such evaluations and for end users to assess their validity. Further, we discuss approaches to formatting and announcing publicly available datasets suitable for assessment of automation technologies and tools. Making these resources available will increase trust that tools are non-inferior or superior to current practice. Finally, we identify that, even with evidence that automation tools are non-inferior or superior to current practice, substantial set-up challenges remain for main stream integration of automation into the systematic review process