PubMed Health⌕ Search

PubMed · 12535110

Techniques used by "expert" and "non-expert" tutors to facilitate problem-based learning tutorials in an undergraduate medical curriculum.

Abstract

BACKGROUND: There is inconclusive debate within the literature as to whether the best problem-based learning (PBL) tutors are subject experts or not. The debate hinges on whether knowledgeable tutors are tempted to intervene too often in PBL discussions compared to non-expert tutors, and whether the latter may not be able to sufficiently challenge the students' level of understanding. PURPOSE: To describe approaches used by tutors in PBL tutorials and to identify differences between tutors from medical and non-medical backgrounds. METHODS: The research reported in this paper was undertaken during the academic session 1999-2000 at the University of Liverpool Faculty of Medicine. A qualitative exploratory case study method was used and two PBL groups were observed. One of these groups had a medically qualified tutor and the other had a tutor from a humanities background. The focus of the observation was the discourse between tutor and students, which was analysed using a framework drawn from linguistics. Results were fed back to both the tutors and the students to check their perceptions of the interactions. RESULTS: Analysis of the tutorial group interaction revealed that tutors from both backgrounds used similar techniques to raise students' awareness, facilitate the group process and direct students' learning. Differences were noted between the two tutors: the medical tutor set out to raise students' awareness by using questioning techniques herself, whereas the non-medical tutor expected students to question each other. The non-medical tutor was observed to facilitate the group process more often than the medical tutor. CONCLUSIONS: Qualitative analysis of spoken discourse in PBL tutorials provides valuable insights into the processes involved in PBL, thereby generating material which is useful for both training of and giving feedback to PBL tutors.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrea Gilkison. 2003. Techniques used by "expert" and "non-expert" tutors to facilitate problem-based learning tutorials in an undergraduate medical curriculum.. https://doi.org/10.1046/j.1365-2923.2003.01406.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A boosting approach to flexible semiparametric mixed models.

In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semi- and non-parametric regression also the mixed model has been expanded to allow for additive predictors. The common approach uses the representation of additive models as mixed models. An alternative approach that is proposed in the present paper is likelihood based boosting. Boosting originates in the machine learning community where it has been proposed as a technique to improve classification procedures by combining estimates with reweighted observations. Likelihood based boosting is a general method which may be seen as an extension of L2 boost. In additive mixed models the advantage of boosting techniques in the form of componentwise boosting is that it is suitable for high dimensional settings where many explanatory variables are present. It allows to fit additive models for many covariates with implicit selection of relevant variables and automatic selection of smoothing parameters. Moreover, boosting techniques may be used to incorporate the subject-specific variation of smooth influence functions by specifying 'random slopes' on smooth effects. This results in flexible semiparametric mixed models which are appropriate in cases where a simple random intercept is unable to capture the variation of effects across subjects.

Cohort Studies↗

Estimation of attributable number of deaths and standard errors from simple and complex sampled cohorts.

Estimates of the attributable number of deaths (AD) from all causes can be obtained by first estimating population attributable risk (AR) adjusted for confounding covariates, and then multiplying the AR by the number of deaths determined from vital mortality statistics that occurred in the population for a specific time period. Proportional hazard regression estimates of adjusted relative hazards obtained from mortality follow-up data from a cohort is combined with a joint distribution of risk factor and confounders to compute an adjusted AR. Two estimators of adjusted AR are examined. These estimators differ according to which reference population is used to obtain the joint distribution of risk factor and confounders. Two types of reference populations were considered: (i) the population represented by the baseline cohort and (ii) a population that is external to the cohort. Methods used in survey sampling are applied to obtain estimates of the variance of the AD estimator. These variances can be applied to data that range from simple random samples to multistage stratified cluster samples, which are used in national household surveys. The variance estimation of AD is illustrated in an analysis of excess deaths due to having a non-ideal body mass index using the second National Health and Examination Survey (NHANES) Mortality Study and the 1999-2002 NHANES. These methods can also be used to estimate the attributable number of cause-specific deaths and their standard errors when the time period for the accrual of deaths is short.

Cohort Studies↗

Longitudinal variable selection by cross-validation in the case of many covariates.

Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (marginal models, mixed effects models, etc.), far fewer options exist for the closely related issue of variable selection. In addition, longitudinal data typically derive from medical or other large-scale studies where often large numbers of potential explanatory variables and hence even larger numbers of candidate models must be considered. Cross-validation is a popular method for variable selection based on the predictive ability of the model. Here, we propose a cross-validation Markov chain Monte Carlo procedure as a general variable selection tool which avoids the need to visit all candidate models. Inclusion of a 'one-standard error' rule provides users with a collection of good models as is often desired. We demonstrate the effectiveness of our procedure both in a simulation setting and in a real application.

Cohort Studies↗