Education Systems Quality and the Concept of Wisdom

Dr Wayne Hellmuth & Ms Julie Owen

?1.??? Introduction

“It is difficult to conceptualise a situation where anything less than total quality is perceived as being appropriate or acceptable for the education of children” (West-Burnham, 1997, p. 17). Regardless of issues associated with applying appropriate quality management strategies, there still remains an imperative to work towards achieving systems quality within schools. A quality management system, within education, is defined as a collection of business processes focused on consistently providing services that meet student, teacher and parent expectations. The goal of quality education systems should be to optimise it, to ensure that education service quality is aligned with the institution’s purpose and strategic direction.

The quality management imperative is driven by the moral obligation of all education institutions to provide the best educational opportunities for children. Stemming from this imperative is the need for teachers to apply the best pedagogical practices and for leadership to develop the most conducive environment in supporting these practices. Aside from the moral obligations to the children, schools have become subject to constant changing public accountabilities and standards.

Berry (2002, p. 203) states that the implementation of any quality system in schools needs to be implemented from a system’s perspective ensuring ‘cyclical action learning’ and process improvement. Quality Management can help corporate systems and schools systematically bring about change as: “Its holistic approach accents system theory. Its tools provide vehicles for data analysis and decision-making. Its principles accent the importance of each person in the system to strive for continuous improvement”. Research shows that the leaders of quality award-winning schools are more likely to be familiar with, and have positive perceptions towards, quality management programs (Jauch, 2010).

Within education there are numerous cases where there have been attempts to improve quality or implement a quality improvement program. Many of these programs fail or make little difference to the outcome of the quality of education for students. Most notably, four years after the Australian Federal Government funding for 1:1 computer provision was distributed, the literacy and numeracy results – as measured by NAPLAN (National testing for literacy and numeracy) – had not improved (Allan, 2010). The main reason for these failures centres on the lack of understanding of the definition of ‘quality’, and a lack of understanding on how to implement effective quality management programs within an education system (Dimmock, 2013; Cheng, 1993).

McLaughlin (1990) notes that very few federally funded education reform projects have been successfully implemented; only 18% were deemed successful. Elmore (1995) states that there are few educational examples where the majority of teachers engage in teaching practices shaped by educational reform projects. A key reason for these failures is the central belief that improving the quality of one component of the ‘system’, will improve the overall quality. Regardless of these failures, many authors advocate the potential success of quality management within schools.

The objectives of Quality Management in education are to use the collective knowledge and skills of educators to identify, analyse, and implement strategies to improve education practices. It includes everything related to the student learning experience, including administrative processes, care services, teaching techniques, teaching content, examinations, leadership, and governance. All elements of the school system must be examined, as quality is dependent on the continual improvement of all elements within it (Weidner & Harris, 2008).

Many authors claim that the total quality management concept originated from the research and teachings of Deming (Juran, 1989; Feigenbaum, 1991; Martínez-Lorente, Dewhurst & Dale, 1998); however, it is evident that it evolved from ongoing ‘quality research’ and business improvement practices within the manufacturing industry in the 20th century (Ackoff, 1999). “Total quality management did not appear fully formed, but emerged in the 1980s as popular representation of fifty years of development of quality theory and practice in manufacturing industries” (Houston, 2007, p. 4).

Although Deming is not solely responsible for the development of total quality management, it is clear that he was responsible for its popular representation throughout the 1980’s. The basis of Deming’s ‘total quality management’ were the four steps: plan, do, check and action or later represented as (Define, Measure, Analyse, Improve & Control). These steps have become known as the Continuous Improvement Cycle (Deming, 1986).

The remainder of this article elaborates further on the development and use of data in the ‘Measure’ step of the continuous improvement cycle.

2.??? Measurement in Quality Management Practices

There is an old adage, attributed to Deming that you can’t ‘manage what you can’t measure’. Although, Deming a statistician who emphasised the importance of measurement, also provided a cautionary viewpoint to running an enterprise on data alone (see seven deadly diseases ).

In the world of Information Management, there are techniques for treating data that enriches our ways of measuring what is happening between constructs, or even what may happen in the future, between constructs. The DIKW pyramid, also known variously as the?DIKW hierarchy,?wisdom hierarchy,?knowledge hierarchy,?information hierarchy refers loosely to a class of models?for representing purported structural and/or functional relationships between data, information,?knowledge, and wisdom.

"Typically, information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge". The DIKW illustrates how the deep understanding of a subject will emerge when passing through the four qualitative stages:?D?– data,?I?– information,?K?– knowledge and?W?– wisdom.

??3.??? Knowledge Bases

Knowledge bases, also known as ontologies, are a?collection of interlinked descriptions of entities that put data into context and enable?data integration, analytics & sharing. Ontologies represent the backbone of the formal semantics of a knowledge graph. They can be seen as the data schema of the graph.?

3.1 Education Systems – Knowledge Base Example

Figure 1 below illustrates at layer 1, a representation of an ‘Education System’. All schools, no matter what their characteristics, will share this common ontology.

Figure 2 below shows the second layer of classes in the ‘Education System’ ontology. Each class in layer 2 can have further …. n sub-classes of objects. The full ‘Education System’ ontological model provides us with the knowledge of all objects in the education system and how they relate to each other. It is the understanding of this framework of semantic related objects that defines whether a person, within the system, is ‘wise’.

Ontologies ensure a shared understanding of the data and its meanings. Ontologies are represented through: (Note that figures 1 and 2 are simple representations of an education system and do not adhere to the formal ontological structure described below).

?Classes.?The entity description contains a classification of the entity with respect to a class hierarchy. For instance, when dealing with HR related information there could be classes?Person,?Organisation?and?Location. Persons and organisations can have a common superclass?HR. Location usually has numerous sub-classes, e.g.,?School,?Postcode,?Region, etc. The notion of class is borrowed by the object-oriented design, where each entity usually belongs to exactly one class.

Relationship types. The relationships between entities are formally tagged with types, which provide information about the nature of the relationship e.g., parent-child. Relationship types can also have formal definitions, e.g., that?parent-of?is inverse relation of?child-of, they both are special cases of?relative-of, which is a symmetric relationship.

Categories. An entity can be associated with categories, which describe some aspect of its semantics”.

Free text descriptions. Often a ‘human-friendly text’ description is provided to further clarify design intentions for the entity and improve search.

?4.??? Wisdom

Now that we have knowledge of the system, we ultimately want to know how to change system in order to best optimise it. To gain this wisdom, we require an understanding of what the future system might be like. This ‘wisdom’ requires statistical methods. Techniques such as SQL can only provide ‘what is’, whereas statistical methods provide the probability of what the relationship between objects might be, in future state scenarios.

Now that we have all of the entities defined in the ontology of an ‘Education System’ we can begin to use Bayesian logic to test the relationships between classes of objects that we have formally defined as part of the ‘Education System’.

Example

A leader in an education system, may want to know the following:

1.?? What effect does the investment in Internet Speeds, have on student engagement and literacy learning outcomes?

a.???? To do this we first, either use or define the entities in the existing class structure that is the “Education System’ ontology. A simple example is shown in Figure 3 below.

b.???? Secondly, we use Bayesian statistical methods to test the presented scenario.

5.??? Bayesian Logic

Bayes’ theorem (alternatively Bayes’ law or Bayes’ rule) has been called the most powerful rule of probability and statistics. It describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

?For example, if internet speed is related to literacy levels, then using Bayes’ theorem, internet speed can be used to more accurately assess the probability of a student’s literacy levels, compared to the assessment of the probability of literacy level gained without knowledge of the person’s access to internet technology. It is a powerful law of probability that brings in the concept of ‘subjectivity’ or ‘the degree of belief’ into statistical modelling.

In this internet example, we would expect that students who have access to higher internet bandwidths would have higher literacy levels. What if, however, this was not the case? We can use Bayes rule to determine, what other factors influence literacy levels given that a student has access to high internet speeds. Bayes’ rule is the only mechanism that can be used to gradually update the probability of an event as the evidence or data is gathered sequentially.

?Additionally, given the established education ‘systems ontology’ with objects grouped in class structures, we are able to group common objects and make comparisons between higher order classes. In the internet example forwarded, we provided one class of objects at a low level, however, we may be interested in a comparison of technology platforms i.e., one school may use a google platform and associated technologies, another school may use a Microsoft platform and associated technologies. Using the class structures in the ontology we are able to classify all technologies as Microsoft and Google and then we are able to use Bayesian logic to define whether schools using the Microsoft platform have better literacy rates then for those students using the Google Platform.

?6.??? Article Summary

This article has argued the importance of examining all aspects of a system in the overall pursuit of systems quality and systems improvement within education. This article has outlined an approach for the measurement of system’s quality using the concepts of information, knowledge and wisdom. The key concept of a knowledge base or systems ontology has been defined in the education context. Finally, Bayes theorem has been introduced as a preferred tool/method for developing wisdom about current and future optimisation states of an Education System.