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

👆   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

 

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