Group items tagged

Here, we show that practicing retrieval produces greater gains in meaningful learning than elaborative studying with concept mapping. The advantage of retrieval practice generalized across texts identical to those commonly found in science education. The advantage of retrieval practice was observed with test questions that assessed comprehension and required students to make inferences. The advantage of retrieval practice occurred even when the criterial test involved creating concept maps. Our findings support the theory that retrieval practice enhances learning by retrieval-specific mechanisms rather than by elaborative study processes. Retrieval practice is an effective tool to promote conceptual learning about science.

"This demonstrates that medical students using mind maps can successfully retrieve information in the short term, and does not put them at a disadvantage compared to SNT students."

Clinical Document Collection: A collection of 7673 initial visit notes was obtained from the Columbia University Medical Center Milstein Hospitalist Service. This includes all resident and attending initial visit notes and initial consult notes for inpatient admissions of all types from late 2006 through early 2007. They are not filtered and should therefore be representative of all patients admitted to the Hospitalist Service. All notes from the Service are entered through semi-structured entry templates in a system called eNote11. PMH was entered into a coded field in eNote templates, but as free text within that field. The advantage for this analysis was that these lists were in the doctor's own words without any limits on structure or content imposed by the information system. The notes were stored using the Clinical Document Architecture (CDA) XML schema. This allowed for a simple XSL transformation to filter protected health information (PHI) and convert sections of interest to text. A small Java application was written to perform this XSLT on each document and do basic preprocessing to prepare the text for natural language processing analysis. Data Preparation: The corpus was then parsed with the MedLEE natural language processor12 to obtain the semantic structure and UMLS codes of concepts represented in these notes. MedLEE output was generated as XML and a Java postprocessor was used to validate the XML output. Each note section was divided into a text section with numbered phrase tags around identifiable phrases and a structured element containing references describing the tagged phrases. Reference tags were named with the phrase's semantic type. MedLEE assigned a UMLS code to the phrase whenever it could map the clinical information detected to known UMLS concepts. MedLEE results were then merged into one large XML file to facilitate querying across all documents with XQuery.

A long list with links to mindmapping software