Does Infused ESOL teacher preparation program hold promise toward narrowing the English Learner achievement gap? A Multilevel analysis of the One-Plus Model

By

👆   ABSTRACT


This study examined the effectiveness of the pre-service teacher (PST) candidates who participated in an infused ESOL One-Plus model teacher preparation program (TPP) in a large public university located in the Southeastern United States. PST’ effectiveness was measured using the value-added model of teacher evaluation by means of reported pre- & posttest score through teacher work samples (TWS). The data included K-12 students’ achievement scores on tests designed and administered by One-Plus PSTs before and after teaching a unit in the content area and language arts courses during their internship. The procedure involved measuring students’ existing/baseline knowledge of the unit using the pretest scores later compared to their posttest scores to assess the learning gains and achievement gap among students of various demographic characteristics including ELs. In addition, the study measured the interactional effects of student-level variables on achievement gap and how teacher level variables moderated such gaps. The findings suggested that the students had variable degrees of prior knowledge in all the subject areas based on their gender, socio-economic status, English learner status, and the class size. The results showed increased learning gains among all groups, smaller achievement gap between students, and comparatively higher learning gains among historically low-performing K-12 students.

Key Words: ESOL Infusion One Plus, English learners, Achievement gap, Value-added measure, HLM, Teacher preparation

Some important tables:

Table 1

Unconditional Model Results for Pre-test and Pre-Post Gain.

 

Pre-test

Pre-Post Gain

Effect

Estimate

SE

df

t-value

Estimate

SE

df

t-value

Intercept

47.18

1.14

235

41.41

 

 

235

 

Note. df = degrees of freedom; SE = standard error


Table 2

Model Comparison Table ICC, AIC, and BIC statistics

Model

Pre-test

Pre-Post Gain

 

ICC

AIC

BIC

ICC

AIC

BIC

Unconditional

0.47927

47661.7

47707.3

0.43325

47494.1

47539.8

All Level-1 Predictors, with Fixed Slopes

.49309

47399.8

 

47536.7

.43376

47496.1

47633

All Level 1 predictors with Random Slopes

.55635

46610.8

 

47036.7

.54044

47344.3

47892

Mixed with Level-1 predictors

.55617

46613.5

 

46811.2

.45697

47325

47598.8

Final Model

.5568

46605.8

46818.8

.45599

47316.4

47529.4

Note. ICC = Intra Class Correlation Coefficient; AIC = Akaike Information Criteria; BIC = Bayesian Information Criteria

Table 3

Solution for Fixed Effects Output for Pre-test Random Intercept, Fixed Slope Model, and Random Intercept, Random Slope with Predictors (Final) model

 

Random Intercept, Fixed Slope

Final Modal

Effect

Estimate

SE

df

t-value

Estimate

SE

df

t-value

Intercept

47.18

1.14

235

41.50***

47.19

1.14

235

41.51***

STUDENT

0.161

0.016

5227

9.62***

0.72

0.09

235

8.12***

GENDER

1.42

0.47

5227

3**

1.15

0.47

4755

2.48*

FRLUNCH

-4.52

0.6

5227

-7.58***

-3.87

0.72

4755

-5.37***

ELL

-7.21

0.83

5227

-8.62***

-5.05

0.83

4755

-6.06***

Random For GENDER

 

 

 

 

 

 

PROGRAM

-

-

-

-

7.14

2.76

4755

2.58*

GRD_LEVEL

-

-

-

-

-6.63

2.81

4755

-2.36*

 

Table 4

Solution for Random Effect for Pre-test Random Intercept, Fixed Slope Model, Random Intercept, and Random Slope with Predictors (Final) model.

Random Intercept, Fixed Slope

Final Modal

Random Effect

VC

df

χ2

 

VC

df

χ2

Intercept

289.20

235

5294.21**

 

292.51

10

532.93**

STUDENT

-

-

-

 

1.37

10

31.21**

GRADE

-

-

-

 

6.84

10

15.48

 

👇Want more? Contact Me.

Comments

Post a Comment

Popular posts from this blog

Education Matters: Understanding Nepal’s Education (Publication Date: June 19, 2023, Ratopati-English, Link at the End)

By
Welcome to the inaugural article of “Education Matters”, a thought-provoking series that sheds light on the pressing issues within Nepal’s educational system while fostering a spirit of positive transformation. In this first installment, I provide an overview of the current state of education in Nepal, examining the general problems faced by the system. I also delve into the experiences of individuals who have pursued education abroad, exploring their struggles in utilizing their Nepalese education and credentials effectively. As I embark on this enlightening journey, I invite you to join me in uncovering the concerns within Nepal’s education system and inspiring meaningful change. Together, we can ensure that education truly matters, empowering every Nepalese child with the quality education they deserve.   Education as a Fundamental Right The right to education, including quality education, is enshrined in both international and national frameworks. Article 26 of the Universal Declar
Read more

Multiple Correspondence Analysis (MCA) in Educational Data

By
Image
  AUTHOR AFFILIATION Nirmal Ghimire, Ph.D.   K-16 Literacy Center at University of Texas at Tyler PUBLISHED May 19, 2023 Introduction Multiple Correspondence Analysis (MCA) is a multivariate statistical technique that is used to analyze the relationships between categorical variables. It is a generalization of correspondence analysis (CA), which is used to analyze the relationships between two categorical variables. MCA can be used to explore the associations between multiple categorical variables simultaneously. MCA works by creating a map of the categorical variables. The map is created by calculating the distances between the different categories of the variables. The closer two categories are on the map, the more similar they are. The further apart two categories are on the map, the less similar they are. MCA can be used to explore a variety of research questions. For example, MCA can be used to: Explore the relationships between different demographic variables, such as age, gender
Read more

charting Concept and Computation: Maps for the Deep Learning Frontier

By
Image
  Kelleher, J. D. (2019). Deep Learning (1st ed.) . The MIT Press Buy the book here General Overview of the Book Introduces deep learning, its applications, and how it enables data-driven decision making Explains key machine learning concepts like datasets, algorithms, functions, overfitting vs underfitting Describes how neural networks work and how they implement functions  Traces history of neural networks through three key eras: threshold logic units, multilayer perceptrons/backpropagation, and deep learning Covers specialized neural network architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) Explains processes for learning functions from data using backpropagation and gradient descent algorithms Discusses future directions for deep learning like bigger datasets, new models, hardware improvements  Examines concept of representational learning in hidden neural network layers Considers challenges around interpretability and explainability of d
Read more