Singapore's No Child Left Behind Quality Assurance Program

Quality Control & Assurance

Introduction

In identifying the strategic goals of improving student achievement, the school environment, partnership of the community and school staff effectiveness, the “no-child-left-behind” initiative launched by the Ministry of Education (MoE) in Singapore has necessitated the aggregate collection of disparate data from hundreds of primary, secondary and tertiary institutions across the country. The quality of the data obtained from these myriad sources will determine the effectiveness of the initiative and hence demands a rigorous approach by the government of Singapore in collaboration with educational institutions, students, parents, academics and administrators towards policymaking and developing a sound technical architecture which can support the all encompassing strategic goals.

Issues

The “no-child-left-behind” initiative, while beneficial to society at large for gaining access to a wide body of data relating to the quality of education in the country’s academic institutions, also creates a strong incentive for these institutions to attempt to project a better standing of their student and faculty pools through manipulation of data in their information systems and other nefarious means. The MoE can refer in detail the issues plaguing the adoption of a similar policy in the USA, which resulted in institutions adopting dubious and reprehensible tactics to create an artificial illusion of

performance –

(a) Exaggerating test scores by lowering examination standards

(b) Selectively excluding students of certain classes, economic backgrounds and races

(c) Discriminating between students on the basis of their historical academic performance

Compounding the issue of the collection of q...

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...dge of experts can be combined to define rule checking algorithms in engines that can flag exceptions when applied against the data collected from institutions. For example, if it is known that the pass percentage of students from a certain district in Singapore is 70% for mid-semester examinations, a sudden spurt in the pass percentage figures between consecutive semesters should alert authorities to the possible supply of spurious data by some institutions in the latest semester.

Qualitative data such as the feedback from students about instructors can be aggregated using text mining tools to draw summarizing inferences.

Although the above framework considerably increases data quality and largely automates its assurance, it is recommended that periodic manual audits by government bodies be enforced on institutions to serve as an additional layer of supervision.