13 research outputs found

    A System Dynamics Evaluation Model: Implementation of Health Information Exchange for Public Health Reporting

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    The objective of this study is to evaluate complex dynamics involved in implementing electronic health information exchange for public health reporting at a state health department, and to identify policy implications to inform similar implementations. Qualitative data were collected over eight months from seven experts at NYS DOH who implemented web services and protocols for querying, receipt, and validation of electronic data supplied by regional health information organizations. Extensive project documentation also was collected. During group meetings experts described the implementation process and created reference modes and causal diagrams that the evaluation team used to build a preliminary model. System dynamics modeling techniques were applied iteratively to build causal loop diagrams representing the implementation. Validation of the diagrams was done iteratively by individual experts followed by group review online, and through confirmatory review of documents a! nd artifacts. Three casual loop diagrams captured well-recognized system dynamics: Sliding Goals, Project Rework, and Maturity of Resources. The findings were associated with specific policies that address funding, leadership, ensuring expertise, planning for rework, communication, and timeline management. This evaluation illustrates the value of a qualitative approach to system dynamics modeling. As a tool for strategic thought about complicated and intense processes, qualitative models can be produced with fewer resources than a full simulation, yet still provide insights that are timely and relevant. System dynamics techniques clarified endogenous and exogenous factors at play in a highly complex technology implementation, which may inform other states engaged in implementing HIE supported by federal HITECH legislatio

    Learning in an Introductory Physics MOOC: All Cohorts Learn Equally, Including an On-Campus Class

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    We studied student learning in the MOOC 8.MReV Mechanics ReView, run on the edX.org open source platform. We studied learning in two ways. We administered 13 conceptual questions both before and after instruction, analyzing the results using standard techniques for pre- and posttesting. We also analyzed each week’s homework and test questions in the MOOC, including the pre- and posttests, using item response theory (IRT). This determined both an average ability and a relative improvement in ability over the course. The pre- and posttesting showed substantial learning: The students had a normalized gain slightly higher than typical values for a traditional course, but significantly lower than typical values for courses using interactive engagement pedagogy. Importantly, both the normalized gain and the IRT analysis of pre- and posttests showed that learning was the same for different cohorts selected on various criteria: level of education, preparation in math and physics, and overall ability in the course. We found a small positive correlation between relative improvement and prior educational attainment. We also compared homework performance of MIT freshmen taking a reformed on-campus course with the 8.MReV students, finding them to be considerably less skillful than the 8.MReV students.Google (Firm) (Faculty Award)Massachusetts Institute of TechnologyNational Science Foundation (U.S.

    Learning in an Introductory Physics MOOC: All Cohorts Learn Equally, Including an On-Campus Class

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    We studied student learning in the MOOC 8.MReV Mechanics ReView, run on the edX.org open source platform. We studied learning in two ways. We administered 13 conceptual questions both before and after instruction, analyzing the results using standard techniques for pre- and posttesting. We also analyzed each week’s homework and test questions in the MOOC, including the pre- and posttests, using item response theory (IRT). This determined both an average ability and a relative improvement in ability over the course. The pre- and posttesting showed substantial learning: The students had a normalized gain slightly higher than typical values for a traditional course, but significantly lower than typical values for courses using interactive engagement pedagogy. Importantly, both the normalized gain and the IRT analysis of pre- and posttests showed that learning was the same for different cohorts selected on various criteria: level of education, preparation in math and physics, and overall ability in the course. We found a small positive correlation between relative improvement and prior educational attainment. We also compared homework performance of MIT freshmen taking a reformed on-campus course with the 8.MReV students, finding them to be considerably less skillful than the 8.MReV students
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