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

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.

KEYWORDS:

• Latent variable models
• multilevel latent class model
• educational assessment
• learning statistics
• self-learning platforms
• formative and motivational feedback

Data availability statement

Data are available upon request to the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 STEM is the abbreviation for Science, Technology, Engineering, and Mathematics

2 https://www.nfqnetwork.ie/_fileupload/Image/Bologna_web.pdf

3 https://europass.europa.eu/system/files/2020-05/EQF-Archives-EN.pdf

4 Many of the questions were developed during the ‘Adaptive LEArning in Statistics’ (ALEAS) Erasmus+ project (KA+2018-1-IT02-KA203-048519).

Additional information

Funding

Rosa Fabbricatore acknowledges the financial support provided by the European Union - NextGenerationEU, in the framework of the GRINS - Growing Resilient, INclusive and Sustainable project (GRINS PE00000018 - CUP E63C22002140007). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them.