Students' proficiency evaluation: a non-parametric multilevel latent variable model approach

Studies in Higher Education, vol. 50, pp. 1528–1555

Abstract

In higher education, students' assessment has a two-fold aim: (i) evaluate students' proficiency level concerning the topics of a specific course; (ii) identify students' weaknesses throughout the whole learning activity and, if any, relate them to a set of socio-demographic and psychological covariates/predictors. In this vein, this manuscript proposes a multilevel latent class model as an analytic strategy to detect homogeneous groups of students based on their abilities, operationalized according to the following dimensions: Knowledge, Applying knowledge, and Judgment. As a novelty, the proposed model associates each dimension with a first-level latent class variable, which contributes to the identification of a second-level latent class variable that summarizes students' abilities according to the whole learning activity. The presented empirical results are based on Statistics tests covering three different topics and survey instruments administered to students of an introductory Statistics course. The main results show that the model identifies distinct overall patterns of learning and differences according to ability dimensions and topics. Moreover, the study of the relationships between the second-level latent class variable and socio-demographic and psychological covariates helps to characterize and deeply understand the students' profiles, fostering the development of tailored recommendations.

Authors 5

1. Rosa Fabbricatore corresponding

   University of Naples Federico II

   Affiliation as printed

   University of Naples Federico II

   Department of Economics and Statistics, University of Naples Federico II, Naples, Italy

2. Zsuzsa Bakk Aachen Department of Methodology and Statistics

   Leiden University

   Affiliation as printed

   Leiden University

   Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands

3. Roberto Di Mari

   University of Catania

   Affiliation as printed

   University of Catania

   Department of Economics and Business, University of Catania, Catania, Italy

4. Mark de Rooij Aachen Department of Methodology and Statistics

   Leiden University

   Affiliation as printed

   Leiden University

   Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands

5. Francesco Palumbo

   University of Naples Federico II

   Affiliation as printed

   University of Naples Federico II

   Department of Political Sciences, University of Naples Federico II, Naples, Italy