Parameter estimation of the Linear Phase Correction model by hierarchical linear models

Noy, Dominic; Menezes, Raquel

doi:10.1016/j.jmp.2018.03.008

Cited by 2 publications

(2 citation statements)

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“…The research structure spans two analytical levels (school level and teacher level). Therefore, the research design also adopts hierarchical linear analysis [20]. Hence, this research adopts the hierarchical linear model (HLM) as the analysis method and puts the adjustment variables of the school situation in the second tier of the school level.…”

Section: Leadershipmentioning

confidence: 99%

Stimulating the Influence of Teaching Effectiveness and Students’ Learning Motivation by Using the Hierarchical Linear Model

2022

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In the context of school learning and teaching, teachers are leaders who stimulate students’ learning outcomes through leadership styles. The teacher efficacy of this study is discussed by using multiple teaching and class management strategies. Because the situation of each school is different, the contingency theory is used to design the research structure. This study changed the original leadership style to the new leadership style; effectiveness to teacher effectiveness; and context to school context, and set them as moderators. Since the unit of analysis is teachers, it is difficult to find factors such as differences in regression coefficients in the school context. Therefore, the HLM hierarchical linear analysis is used, and the school context is set as the second level, and then statistical analysis is performed. Teachers’ leadership styles have significant differences in multiple teaching strategies for teacher effectiveness. Moral leadership and charismatic leadership have a moderating effect on the multiple teaching strategies of teacher effectiveness. The multiple teaching strategies of teacher effectiveness have a significant effect in middle school situations with lower moral leadership. On the other hand, charismatic leadership is more pronounced in middle school situations than in high school situations. Teachers’ leadership styles have a significant impact on classroom management of teacher effectiveness. Under the situational control of schools, moral leadership, charismatic leadership, and supportive leadership have moderating effects on classroom management of teacher effectiveness. When the classroom management of teacher effectiveness is in the middle school situation, low moral leadership is more significant; in the high school situation, charismatic leadership will reduce the effectiveness of the class management; in the moderate or high situation, the higher the support of the leader, the greater the effect of class management.

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Section: Leadershipmentioning

confidence: 99%

Stimulating the Influence of Teaching Effectiveness and Students’ Learning Motivation by Using the Hierarchical Linear Model

2022

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“…Here, A represents diagonal matrix M × M as shown in (13), B denotes M × N matrix as shown in (14), and e is for the error rate. Moreover, coefficients, namely A and B, are unknown for the linear model [50]. The values of A and B are estimated by using a PSO algorithm, and the parameters of PSO are selected depending on the problem type and input features.…”

Section: A New Deep Learning Framework Using Deep Auto-encoders and A...mentioning

confidence: 99%

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

Karim¹,

Kaya²,

Güzel

et al. 2020

Sensors

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This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

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Parameter estimation of the Linear Phase Correction model by hierarchical linear models

Cited by 2 publications

References 31 publications

Stimulating the Influence of Teaching Effectiveness and Students’ Learning Motivation by Using the Hierarchical Linear Model

Stimulating the Influence of Teaching Effectiveness and Students’ Learning Motivation by Using the Hierarchical Linear Model

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

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