Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients

Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria

doi:10.1007/s00180-021-01107-1

Cited by 6 publications

(7 citation statements)

References 52 publications

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“…To this end, in parametric multilevel models the variance partition coefficient measures the percentage variability explained by the data highest level structure. Its extension to semiparametric multilevel models have also been proposed (Goldstein, Browne and Rasbash (2002), Rights and Sterba (2016), Masci et al (2021)), and we are working to adapt it to the case of multinomial semiparametric mixed-effects models.…”

Section: Concluding Remarks and Future Perspectivesmentioning

confidence: 99%

Semiparametric multinomial mixed-effects models: A university students profiling tool

2022

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Many applicative studies deal with multinomial responses and hierarchical data. Performing clustering at the highest level of grouping, in multilevel multinomial regression, is also often of interest. In this study we analyse Politecnico di Milano data with the aim of profiling students, modelling their probabilities of belonging to different categories and considering their nested structure within engineering degree programmes. In particular, we are interested in clustering degree programmes standing on their effects on different types of student career. To this end, we propose an EM algorithm for implementing semiparametric mixed-effects models dealing with a multinomial response. The novel semiparametric approach assumes the random effects to follow a multivariate discrete distribution with an a priori unknown number of support points, that is, allowed to differ across response categories. The advantage of this modelling is twofold: the discrete distribution on random effects allows, first, to express the marginal density as a weighted sum, avoiding numerical problems in the integration step, typical of the parametric approach, and, second, to identify a latent structure at the highest level of the hierarchy where groups are clustered into subpopulations.

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Section: Concluding Remarks and Future Perspectivesmentioning

confidence: 99%

Semiparametric multinomial mixed-effects models: A university students profiling tool

2022

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“…This means that most of the students in the allotted time cannot perform the exercises that their predecessors successfully solved. In order to identify the criteria that affect the quality of school learning and by which the difference between classroom learning and online learning can be established, the authors have studied a number of publications [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The authors of the articles [4,5] worked to highlight the parameters that influence the learning process. C. Masci et al [4] examined different parameters influencing the teaching process in the framework of the school value-added concept. The researchers studied the effect of a particular class or school in Italy on the achievements of students in reading and mathematics.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the above publications [2][3][4][5][6][7][8][9][10][11], we established a criteria vector that affects completing homework.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

et al. 2021

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This article deals with the multicriteria programming model to optimize the time of completing home assignments by school students in both in-class and online forms of teaching. To develop a solution, we defined 12 criteria influencing the school exercises’ effectiveness. In this amount, five criteria describe exercises themselves and seven others the conditions at which the exercises are completed. We used these criteria to design a neural network, which output influences target function and the search for optimal values with three optimization techniques: backtracking search optimization algorithm (BSA), particle swarm optimization algorithm (PSO), and genetic algorithm (GA). We propose to represent the findings for the optimal time to complete homework as a Pareto set.

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Inferential Tools for Assessing Dependence Across Response Categories in Multinomial Models with Discrete Random Effects

Masci,

Ieva,

Paganoni

2024

J Classif

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We propose a discrete random effects multinomial regression model to deal with estimation and inference issues in the case of categorical and hierarchical data. Random effects are assumed to follow a discrete distribution with an a priori unknown number of support points. For a K-categories response, the modelling identifies a latent structure at the highest level of grouping, where groups are clustered into subpopulations. This model does not assume the independence across random effects relative to different response categories, and this provides an improvement from the multinomial semi-parametric multilevel model previously proposed in the literature. Since the category-specific random effects arise from the same subjects, the independence assumption is seldom verified in real data. To evaluate the improvements provided by the proposed model, we reproduce simulation and case studies of the literature, highlighting the strength of the method in properly modelling the real data structure and the advantages that taking into account the data dependence structure offers.

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Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients

Cited by 6 publications

References 52 publications

Semiparametric multinomial mixed-effects models: A university students profiling tool

Semiparametric multinomial mixed-effects models: A university students profiling tool

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

Inferential Tools for Assessing Dependence Across Response Categories in Multinomial Models with Discrete Random Effects

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