Flipped classroom for academic and career advising: an innovative technique for medical student advising
Name:
AMEP-162504-flipped-classroom- ...
Size:
215.6Kb
Format:
PDF
Description:
Final Published version
Affiliation
Univ Arizona, Coll Med, Dept Emergency MedUniv Arizona, Coll Med
Univ Arizona, Coll Med, Dept Psychiat
Univ Arizona, Coll Med, Dept Family & Community Med
Univ Arizona, Coll Med, Dept Surg
Univ Arizona, Coll Med, Dept Med
Issue Date
2018Metadata
Show full item recordPublisher
DOVE MEDICAL PRESS LTDCitation
Amini, R., Laughlin, B. S., Smith, K. W., Siwik, V. P., Adamas-Rappaport, W. J., & Fantry, G. T. (2018). “Flipped classroom” for academic and career advising: an innovative technique for medical student advising. Advances in medical education and practice, 9, 371.Rights
© 2018 Amini et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) LicenseCollection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Introduction: Career advising for medical students can be challenging for both the student and the adviser. Our objective was to design, implement, and evaluate a "flipped classroom" style advising session. Methods: We performed a single-center cross-sectional study at an academic medical center, where a novel flipped classroom style student advising model was implemented and evaluated. In this model, students were provided a document to review and fill out prior to their one-on-one advising session. Results: Ninety-four percent (95% CI, 88%-100%) of the medical students surveyed felt that the advising session was more effective as a result of the outline provided and completed before the session and that the pre-advising document helped them gain a better understanding of the content to be discussed at the session. Conclusion: Utilization of the flipped classroom style advising document was an engaging advising technique that was well received by students at our institution.Note
Open access journal.UA Open Access Publishing Fund.
ISSN
1179-7258PubMed ID
29785150Version
Final published versionAdditional Links
https://www.dovepress.com/flipped-classroom-for-academic-and-career-advising-an-innovative-techn-peer-reviewed-article-AMEPScopus Count
Collections
Related items
Showing items related by title, author, creator and subject.
-
A POSITION - ANALYSIS OF THE FOREIGN STUDENT ADVISER AS REFLECTED BY FOREIGN STUDENT ADVISERS AND ADMINISTRATORS(The University of Arizona., 1967)
-
Parameter Advising for Multiple Sequence Alignment(The University of Arizona., 2016)The problem of aligning multiple protein sequences is essential to many biological analyses, but most standard formulations of the problem are NP-complete. Due to both the difficulty of the problem and its practical importance, there are many heuristic multiple sequence aligners that a researcher has at their disposal. A basic issue that frequently arises is that each of these alignment tools has a multitude of parameters that must be set, and which greatly affect the quality of the alignment produced. Most users rely on the default parameter setting that comes with the aligner, which is optimal on average, but can produce a low-quality alignment for the given inputs. This dissertation develops an approach called parameter advising to find a parameter setting that produces a high-quality alignment for each given input. A parameter advisor aligns the input sequences for each choice in a collection of parameter settings, and then selects the best alignment from the resulting alignments produced. A parameter advisor has two major components: (i) an advisor set of parameter choices that are given to the aligner, and (ii) an accuracy estimator that is used to rank alignments produced by the aligner. Alignment accuracy is measured with respect to a known reference alignment, in practice a reference alignment is not available, and we can only estimate accuracy. We develop a new accuracy estimator that we call called Facet (short for "feature-based accuracy estimator") that computes an accuracy estimate as a linear combination of efficiently-computable feature functions, whose coefficients are learned by solving a large scale linear programming problem. We also develop an efficient approximation algorithm for finding an advisor set of a given cardinality for a fixed estimator, whose cardinality should ideally small, as the aligner is invoked for each parameter choice in the set. Using Facet for parameter advising boosts advising accuracy by almost 20% beyond using a single default parameter choice for the hardest-to-align benchmarks. This dissertation further applies parameter advising in two ways: (i) to ensemble alignment, which uses the advising process on a collection of aligners to choose both the aligner and its parameter settings, and (ii) to adaptive local realignment, which can align different regions of the input sequences with distinct parameter choices to conform to mutation rates as they vary across the lengths of the sequences.
-
Learning parameter-advising sets for multiple sequence alignment(IEEE COMPUTER SOC, 2017)While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for the alignment scoring function (such as the choice of gap penalties and substitution scores), most users rely on the single default parameter setting provided by the aligner. A different parameter setting, however, might yield a much higher-quality alignment for the specific set of input sequences. The problem of picking a good choice of parameter values for specific input sequences is called parameter advising. A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that provides an estimate of the accuracy of the alignment computed by the aligner using a parameter choice. The parameter advisor picks the parameter choice from the set whose resulting alignment has highest estimated accuracy. We consider for the first time the problem of learning the optimal set of parameter choices for a parameter advisor that uses a given accuracy estimator. The optimal set is one that maximizes the expected true accuracy of the resulting parameter advisor, averaged over a collection of training data. While we prove that learning an optimal set for an advisor is NP-complete, we show there is a natural approximation algorithm for this problem, and prove a tight bound on its approximation ratio. Experiments with an implementation of this approximation algorithm on biological benchmarks, using various accuracy estimators from the literature, show it finds sets for advisors that are surprisingly close to optimal. Furthermore, the resulting parameter advisors are significantly more accurate in practice than simply aligning with a single default parameter choice.
