Connecting family, school, gold mining community and primary school students’ reading achievements in Burkina Faso – A three-level hierarchical linear model analysis

Jean-Baptiste,

doi:10.1016/j.ijedudev.2021.102442

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…The HLM is a theory co-developed by Raudenbush and Bryk. It has been widely used in pedagogy and psychological research [30,54] and can address the effects of factors at various levels. Traditional multiple regression analysis can measure relationships between single-level variables, which reduces the effectiveness of multilevel data estimation, and the data for this study were nested.…”

Section: Econometric Model 331 Hlmmentioning

confidence: 99%

A Two-Level Hierarchical Linear Model Analysis of the Effect of Teacher Factors, Student Factors, and Facility Conditions on Students’ Cognitive Scores in Rural China

Wang

2022

Sustainability

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Based on the data from the China Education Panel Survey 2013–2014, this study uses a two-level hierarchical linear model to explore the impact of student factors, teacher factors, and facility conditions on students’ cognitive scores and the model’s heterogeneity. Additionally, under the Owen value method, the contribution of teacher and student factors to students’ cognitive scores is much greater than that of facility conditions, and teacher-related factors contribute more to scores than student-related ones. Therefore, teacher and student resources should be given priority in allocating resources to rural education systems, and high-quality teachers, who can have positive impacts on students’ cognitive scores, should be prioritized.

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Section: Econometric Model 331 Hlmmentioning

confidence: 99%

A Two-Level Hierarchical Linear Model Analysis of the Effect of Teacher Factors, Student Factors, and Facility Conditions on Students’ Cognitive Scores in Rural China

Wang

2022

Sustainability

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“…Although scholars have adopted different methods to study the factors affecting student performance and achievement [49][50][51], and to predict final performance [52,53]. There are few studies conducted on the impact of students' characteristics and family factors on their achievement [54,55].…”

Section: Plos Onementioning

confidence: 99%

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

2022

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This study proposes a Grey Wolf Optimization (GWO) variant named Elastic Grey Wolf Optimization algorithm (EGWO) with shrinking, resilient surrounding, and weighted candidate mechanisms. Then, the proposed EGWO is used to optimize the weights and biases of Multi-Layer Perception (MLP), and the EGWO-MLP model for predicting student achievement is thus obtained. The training and verification of the EGWO-MLP prediction model are conducted based on the thirty attributes from the University of California (UCI) Machine Learning Repository dataset’s student performance dataset, including family features and personal characteristics. For the Mathematics (Mat.) subject achievement prediction, the EGWO-MLP model outperforms one model’s prediction accuracy, and the standard deviation possesses the stable ability to predict student achievement. And for the Portuguese (Por.) subject, the EGWO-MLP outperforms three models’ Mathematics (Mat.) subject achievement prediction through the training process and takes first place through the testing process. The results show that the EGWO-MLP model has made fewer test errors, indicating that EGWO can effectively feedback weights and biases due to the strong exploration and local stagnation avoidance. And the EGWO-MLP model is feasible for predicting student achievement. The study can provide reference for improving school teaching programs and enhancing teachers’ teaching quality and students’ learning effect.

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Connecting family, school, gold mining community and primary school students’ reading achievements in Burkina Faso – A three-level hierarchical linear model analysis

Cited by 2 publications

References 54 publications

A Two-Level Hierarchical Linear Model Analysis of the Effect of Teacher Factors, Student Factors, and Facility Conditions on Students’ Cognitive Scores in Rural China

A Two-Level Hierarchical Linear Model Analysis of the Effect of Teacher Factors, Student Factors, and Facility Conditions on Students’ Cognitive Scores in Rural China

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

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