Proposing Revised KHDA Model of School Improvement: Identification of Factors for Sustainable Performance of Dubai Private Schools

Abstract

This paper investigatesthe school’s organizational environment and identify the missing links in the current Knowledge and Human Development Authority (KHDA) framework. It is an identification of the new factors which can contribute toward better employees’ performance and students’ achievement. A high score on these factors justifies the high standards of educational service and the low score indicates the presence of conditions disabling the school improvement efforts of schools. Initial investigation on the KHDA school’s data has indicated the inconsistent performance pattern among Dubai private schools. The presence of at least five distinct performance patterns has encouraged in conducting a study to identify the reasons which promote such inconsistent performance behavior. Three cultural factors—the sub-culture of collective leadership, sub-culture of creativity and innovation, and the sub-culture of the learning organization—were identified from the past literature, and the proposed model was examined by applying structural equation modeling techniques. Therefore, two research hypotheses that the performance of employees in good schools is better than the employees working in the struggling schools, and that the difference of the performance is due to the difference in the success factors of organizational culture in schools, were established. Findings have confirmed that school culture plays a vital role in the success of KHDA efforts of school improvement. Good Quality Schools (GQS) are successful because they can provide an environment to its employees which enables them to perform effectively, whereas the employees in Poor Quality Schools (PQS) are struggling due to the unfavorable organizational culture and work environment. The study provides valuable information to struggling schools on how to come out of the vicious circle of poor performance quality. It also highlights the importance of the preexamination of the cultural conditions in the schools before applying any systemic school improvement framework. It is recommended that extending advise and support to underperforming schools for promoting conducive cultural conditions in the school environment will help them to obtain better performance results on the KHDA inspection framework.

Appendices

Appendix 1

Variables in the proposed model

Construct (LV)	MV and their definition	Items	No. of items
Habits and practices constituting the sub-culture of Collective Leadership (CL)	Measures of shared vision prevail among employees (SV)	cl_svi	3
	Measures of staff commitment to achieve shared vision (SC)	cl_scg	3
	Measures of collaboration among employees (CO)	cl_col	3
Habits and practices constituting the sub-culture of Creativity and Innovation (CR)	Measures of presence of common challenge among employees (CW)	cr_pcj	3
	Measures of staff motivation to face the challenge (MC)	cr_mtc	3
	Measures of autonomy or freedom employees enjoy in their work (FJ)	cr_ffj,	3
	Measures of degree to which resources are made available to exercise innovative ideas in work (SR)	cr_asr	3
Habits and practices constituting the sub-culture of Learning Organization (LO)	Measures of leadership commitment encourage learning (LC)	lo_lel	3
	Measures of degree to which research and development practices are prevailing among employees (RD)	lo_rnd	3
	Measures of degree to which knowledge management practices are prevailing among employees (KM)	lo_kmg	3
Behavior of employees influenced by their performance (PR)	Measures of influence of performance on job satisfaction (JS)	pr_sat	3
	Measures of influence of performance on the organizational commitment (OC)	pr_com	3
	Measures of influence of performance on the work motivation (WM)	pr_mot	3
3 LVs	13 MVs		39 items

Appendix 2

Scale	No. of items	Mean	Variance	Cronbach’s alpha		Reliability
				GQS	PQS
Performance scale (PR)	9	34.88	15.598	0.782	0.764	Acceptable (good)
Collective leadership scale (CL)	9	30.15	27.084	0.871	0.899	Acceptable (very good)
Creativity and innovation scale (CR)	12	47.96	29.319	0.890	0.888	Acceptable (very good)
Learning organization scale (LO)	9	32.51	27.829	0.870	0.896	Acceptable (very good)

Appendix 3

See Tables 3, 4, 5 and 6.

Table 3 Descriptive statistics of composite variable “performance” (GQS and PQS)

Descriptive Analysis of the Sub-culture of Collective Leadership (CL) Scale

Table 4 Descriptive statistics of composite variable “collective leadership” (GQS and PQS)

Descriptive Analysis of the Sub-culture of Creativity and Innovation (CR) Scale

Table 5 Descriptive statistics of composite variable “creativity and innovation” (GQS and PQS)

Descriptive Analysis of the Sub-culture of Learning Organization (LO) Scale

Table 6 Descriptive statistics of composite variable “learning organization” (GQS and PQS)

Appendix 4

Exploratory Factor Analysis on the Sample of GQS

The following table shows the KMO and Bartlett’s test measuring the sample adequacy for the multivariate analysis on the sample of GQS.

See Tables 7, 8, 9, 10, 11, 12, 13 and 14.

Table 7 KMO and Bartlett’s Test (GQS)

Table 8 Total variance explained (GQS)

Table 9 Pattern matrix (GQS)

Table 10 Factor correlation matrix (GQS)

Table 11 KMO and Bartlett’s test (PQS)

Table 12 Total variance explained (PQS)

Table 13 Pattern matrix (PQS)

Table 14 Factor correlation matrix (PQS)

Appendix 5

CFA Fit Indices

See Tables 15, 16, 17, 18, 19, 20, 21, 22 and 23.

Table 15 Model adequacy indices for scale–item CFA on sample of GQS

Table 16 Model adequacy indices for scale–item CFA on sample of PQS

Validity and Reliability Analysis

Convergent validity on the samples of GQS and PQS

First, we examine the factor loadings on the sample of GQS.

Table 17 Regression Weights (GQS)

Table 18 Regression weights (PQS)

Table 19 Calculating AVE (GQS)

Table 20 Calculating AVE (PQS)

Discriminant validity on sample of GQS and PQS

The following table provides comparisons between AVE and corresponding MSV in the samples of GQS and PQS. The findings show that MSV is less than AVE in all variables and it is true for both samples.

Table 21 Discriminant validity (GQS)

Composite Reliability for the sample of GQS and PQS

Table 22 Composite reliability (GQS)

Table 23 Composite reliability (PQS)

From the above discussion, we can conclude that the proposed measurement model is qualifying all criteria of the validity, reliability, and goodness of fit for both samples GQS and PQS. Therefore, we can now run the structured path model.

Appendix 6

Structural Equation Modeling (SEM)

Measurement model

See Tables 24, 25 and 26.

Table 24 Parameter summary (AMOS Output)

Table 25 Model fit indices (GQS)

Table 26 Model fit indices (PQS)

Appendix 7

The Structured Model

See Tables 27, 28, 29 and 30.

Table 27 Paths in the initial model

Table 28 Parameter summary (initial path model)

Table 29 Goodness of fit indices of initial path model (GQS and PQS)

Table 30 Regression estimates of initial path model (GQS and PQS)

Appendix 8

The alternate proposed Model (final form of the structured Model)

Examination of Alternate Proposed Model on Sample of GQS

See Tables 31, 32, 33, 34, 35 and 36.

Table 31 Paths in the alternate model

Table 32 Parameter summary (alternate model)

Table 33 Goodness of fit indices of alternate model (GQS)

Table 34 Regression estimates of alternate model (GQS)

Examination of Alternate Proposed Model on Sample of PQS

The model adequacy was examined with the help of various goodness of fit indices. The following table shows the goodness of fit indices for the above structured model.

Table 35 Goodness of fit indices of alternate model (PQS)

The following table shows that all regression estimates are significant and their p-values are less than the threshold of 0.05.

Table 36 Regression estimates of alternate model (PQS)

According to the indices shown in the above tables, the model verifies the adequacy of the model for the sample data on PQS.

Appendix 9

Multigroup Analysis (Examination of Conformity on Two Samples Multigroup Analysis in the Measurement Model

See Tables 37, 38, 39 and 40.

Table 37 Goodness of fit indices for multigroup analysis (measurement model)

Table 38 Nested model comparisons (assuming model unconstrained to be correct)—measurement model

Multigroup Analysis in the Structured Model

Table 39 Goodness of fit indices for group analysis (structured model)

Table 40 Nested model comparisons (assuming model unconstrained to be correct)—structured model

Appendix 10

Mediation in the Proposed Alternate Model

Case-1: CR as the mediator

See Tables 41, 42, 43, 44, 45 and 46.

Table 41 Regression estimates of direct path between CL and PR

Table 42 Regression estimates of all direct and indirect paths between CL and PR

Case-2: LO as the mediator

Table 43 Regression estimates of direct path between CR and PR

Table 44 Regression estimates of all direct and indirect paths between CR and PR

Direct and Indirect Effects in the Proposed Alternate Model

Table 45 Direct and indirect effects on PR (GQS)

Table 46 Direct and indirect effects on PR (PQS)

Appendix 11

Testing for Mean Difference in Variables for Two Population

Model variables (manifest/latent)		Mean values			Levene’s test for equality of variances
Var.	Type	GQS	PQS	Diff	F	sig.	t	df	Sig. (2-tailed)
PR	L	34.88	19.72	15.16	0.344	0.558	47.528	598	0.000
pr_sat	M	11.77	6.40	5.37	0.006	0.936	40.471	598	0.000
pr_com	M	11.42	6.79	4.63	0.760	0.384	34.970	598	0.000
pr_mot	M	11.69	6.52	5.17	0.060	0.807	41.428	598	0.000
CL	L	30.15	24.31	5.84	1.686	0.195	12.968	598	0.000
cl_svi	M	9.76	8.20	1.56	1.251	0.264	9.714	598	0.000
cl_scg	M	10.29	8.05	2.24	0.075	0.784	12.704	598	0.000
cl_col	M	10.10	8.06	2.04	2.134	0.145	12.188	598	0.000
CR	L	47.96	25.61	22.35	1.884	0.170	48.425	598	0.000
cr_psj	M	12.12	6.33	5.79	3.483	0.063	43.328	598	0.000
cr_mtc	M	12.24	6.21	6.03	2.330	0.127	43.116	598	0.000
cr_ffj	M	12.06	6.31	5.75	1.210	0.272	45.207	598	0.000
cr_asr	M	11.54	6.76	4.78	1.203	0.273	35.057	598	0.000
LO	L	32.51	21.18	11.33	0.006	0.937	26.268	598	0.000
lo_lol	M	11.07	6.88	4.19	0.079	0.779	23.861	598	0.000
lo_rnd	M	10.86	7.06	3.8	0.003	0.958	26.611	598	0.000
lo_kmg	M	10.57	7.24	3.33	0.426	0.514	20.775	598	0.000