IP: 37.9.47.196 On: Sat, 12 Oct 2019 16:55:54 Copyright: American Scientific Publishers Delivered by Ingenta RESEARCH ARTICLE Copyright © 2018 American Scientific Publishers All rights reserved Printed in the United States of America Journal of Computational and Theoretical Nanoscience Vol. 15, 1243–1246, 2018 Application of Multiple Dependent State Sampling in Improving Benchmarking for Students Learning Outcomes Nawal G. Alghamdi 1 and Khushnoor Khan 2 * 1 Department of Educational Psychology and Guidance, Institute of Educational Graduate Studies, King Abdulaziz University, Jeddah 21589, Saudi Arabia 2 Department of Statistics, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia Quality enhancement has become a buzz word in KSA with the expansion of the educational net- work throughout Kingdom of Saudia Arabia. Evaluation of educational programs acts as the lynch- pin for quality enhancement. For an objective analysis of educational evaluation, many statistical tools have been applied in recent years of which Shewhart control charts is considerably a new entry. These control charts and their variants provide the educational management with a clear picture of the process under study and pinpoint the latent areas which need adjustment or improve- ment. Current research proposes control charts using Multiple Dependent State (MDS) sampling techniques applied to a secondary data from Weber State University, which uses the Associate Constructor Level 1 exam as an assessment tool. For the application of the proposed technique, a code has been developed, and a comparison of the same with the traditional charts is carried out. The present study focuses on the application and interpretation of the proposed technique. The results show that the MDS is comparatively more effective than the traditional control charts in the assessment of the educational program for enhancing the benchmark which in the reference study was set at 70% (210) and the same has been discussed threadbare at the end. An easy approach has been adopted so that pedagogical staff engaged in educational assessment can more effec- tively, efficiently and readily adopt the technique. The results of the study will provide educational managers at all levels an objective oriented guide line for future policy decision making regarding the assessment of educational programs. Keywords: Quality Enhancement, Shewhart Charts, Multi Dependent State, Benchmark, Pedagogical Staff. 1. INTRODUCTION Saudi Arabia with its ever-expanding educational hori- zons has emerged as a beacon house of education in the Middle East Region. As in industry likewise in the edu- cational arena if the Kingdom has to sustain its educa- tional supremacy then evidence-based assessment must be undertaken. A continuous quality appraisal is essential in maintaining the sustainable advantage in the educational arena. Many statistical tools have been devised by statis- ticians for objective analysis of any educational programs. Control charts are one of these tools, though the control charts and their improved versions have been applied in the manufacturing industry for the last five decades, the * Author to whom correspondence should be addressed. application of these charts in the evaluation of educational programs have been rare. Quality is inversely proportion to variation which is an inherent attribute of any manufacturing or service render- ing process. There are two general sources of variation in a process or service—chance and assignable. Chance vari- ation is random and hence cannot be eliminated entirely. An assignable variation, on the other hand, is not ran- dom in nature it can be monitored and reduced by looking into the problem and tracing the cause of the variation. When assignable causes are recognized they are typically attributable to one of four situations: (a) change in the operator, (b) change in the input, (c) change in the equip- ment, (d) change in the process or methods. A Control chart is a graphical representation of data that serves as robust decision-making tool to unearth and filter out chance J. Comput. Theor. Nanosci. 2018, Vol. 15, No. 4 1546-1955/2018/15/1243/004 doi:10.1166/jctn.2018.7210 1243