0000000001129085

AUTHOR

Isabella Sulis

showing 2 related works from this author

Building up adjusted indicators of students' evaluation of university courses using generalized item response models

2012

This article advances a proposal for building up adjusted composite indicators of the quality of university courses from students’ assessments. The flexible framework of Generalized Item Response Models is adopted here for controlling the sources of heterogeneity in the data structure that make evaluations across courses not directly comparable. Specifically, it allows us to: jointly model students’ ratings to the set of items which define the quality of university courses; explicitly consider the dimensionality of the items composing the evaluation form; evaluate and remove the effect of potential confounding factors which may affect students’ evaluation; model the intra-cluster variabilit…

Statistics and ProbabilityStructure (mathematical logic)Computer sciencemedia_common.quotation_subjectadjusted indicators explanatory item response models multidimensional latent traits multilevel models evaluation of university courses potential confounding factorsRegression analysisData structureAffect (psychology)Multilevel dataComputingMilieux_COMPUTERSANDEDUCATIONEconometricsMathematics educationQuality (business)Settore SECS-S/05 - Statistica SocialeStatistics Probability and UncertaintySet (psychology)Settore SECS-S/01 - Statisticamedia_commonCurse of dimensionality
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Students Mobility: Assessing the Determinants of Attractiveness Across Competing Territorial Areas

2016

A central question for education authorities has become ‘‘which factors make a territory attractive for tertiary students?’’ Tertiary education is recognised as one of the most important assets for the development of a territory, thus students’ mobility becomes a brain drain issue whenever there are prevalent areas that attract students from other territories. In this paper, we try to identify the most important factors that could affect student mobility in Italy. In doing that we analyse students’ flows across competing territorial areas which supply tertiary education programs. We will consider a wide range of determinants related to the socio-economic characteristics of the areas as well…

AttractivenessEconomic growthSociology and Political ScienceHigher educationmedia_common.quotation_subject01 natural sciences010104 statistics & probabilityArts and Humanities (miscellaneous)Bradley–Terry model0502 economics and businessHuman geographyDevelopmental and Educational PsychologyRegional scienceEconomicsStudent mobility Bradley–Terry model University attractiveness League tablesQuality (business)050207 economics0101 mathematicsStudent mobility Bradley–Terry model University attractiveness League tablesmedia_commonbusiness.industry05 social sciencesGeneral Social SciencesVariety (cybernetics)Central governmentbusinessUniversity systemSocial Indicators Research
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