INFARTO AGUDO DE MIOCARDIO POR DIABETES 22

Often, theories are complex and abstract, which leads to confusion when adapting and applying them in real-life contexts, especially in education. Gardner (2003) stated that educators misunderstood MI theory and its implications, which resulted in mixing other theories with MI theory. One way to make such a theory clear to implement is to con- duct studies within a specific context and to replicate these studies, which might result in clear recommendations for educators. Although the three-ring conception provides a broadened definition of giftedness based on three clusters, those clusters could be manifested by individuals within a specific field of study. In addition, the conception provides more than the generic theory but introduces a blueprint for educators to go from theory into practice. Along with the introduction of the theory, the enrichment triad model was introduced, specifying different levels of enrichment followed by the introduction of the RDIM model. This model provides a clear methodology for how to identify large groups of high potential students, termed a ‘talent pool’, and how to involve this group in different levels of enrichment. In addition, this model has been implemented for a long period in many schools in the United States under the Schoolwide Enrichment Model project (Renzulli & Reis, 2000).

We believe that the three-ring conception can be initially adopted in program- ming, whereby gifted student programmers can be identified based on their academic performance. At this stage, we do not have a comprehensive understanding of the programming characteristics that might be exhibited by the students; therefore, there might be another identification method that could be used in the programming con- text. Thus, investigating programming characteristics through gifted students allows for developing a theory for defining giftedness in programming. Based on that, the enrichment triad model and RDIM can be incorporated, allowing CS educators to

identify the talent pool to include the top 20% of students using multiple identifica- tion methods derived from the developed theory. Then, CS educators can design the three levels of enrichment in the enrichment triad model. In programming, the first level of enrichment, general exploratory, might include advanced data structures and algorithms that could extend student interests. The second level of enrichment, group training activities, might include small group seminars to focus on extending student problem solving, think aloud, and technical communication abilities. The third level of enrichment, individual and small group investigation of real problems, might focus on solving real-life problems by developing a large-scale project. The RDIM outlines the methods and principles for the transition process between the levels of enrichment. Thus, the ultimate goal of providing breadth and depth enrichment for a wide range of students can be achieved without restricting the benefit of a gifted education to a small number of students.

The literature suggests multiple characteristics that a good programmer should possess in both educational and professional contexts. The characteristic that has been most discussed in the literature is mathematics ability. Previous research has investi- gated mathematical abilities, such as cognitive abilities, which could involve problem solving, reasoning, and abstraction. Mathematical abilities have been an indicator of programming aptitude. Most PATs include mathematical questions to measure prob- lem solving, reasoning, and abstraction abilities. On the other hand, mathematics as a knowledge and pedagogy has been investigated to understand the role of mathematics in learning programming. Implications from previous research include designing math- ematics modules that relate to programming concepts or introducing programming concepts and languages based on formal mathematics theory. Moreover, CS educators have established a great deal of research investigating those characteristics for novice programmers to help students who struggle when learning programming. However, there has not been enough investigation into those characteristics for gifted students, whether they manifest specific characteristics or not. The literature indicates that mathematical knowledge is important in learning programming, but does this mean

that gifted student programmers should be gifted mathematicians? In addition, pre- vious studies have not provided significant statistical tests of the correlation between mathematics and programming. Thus, we aim to statistically investigate the correla- tion.

Some characteristics relate to cognitive ability, allowing the programmer to form a mental representation of a problem. Thus, mental representation can consist of prob- lem solving, reasoning, and abstraction ability. A good programmer should possess problem-solving ability, which allows the programmer to analyse, decompose, com- pose, and solve a problem. Different strategies have been proposed in the literature (Mason et al., 2010; Polya, 2004; Vickers, 2008). In relation to knowledge-organisation strategies, it has been suggested that good programmers have different strategies for organising their knowledge in programming (Ormerod, 1990; McKeithen et al., 1981; Hoc, 2014). Knowledge organisation might be manifested by an expert programmer organising and recalling multiple and specific chunks of information that are related to solving a specific problem. In addition, implementing advanced data structures and al- gorithms, such as red black trees, lists, divide and conquer, and graphs is an important characteristic that should be manifested by a highly competent programmer. However, we do not know yet whether gifted student programmers use any of the suggested strategies for solving problems and organising their knowledge or whether they tend to use different strategies. Abstraction ability is an important aspect of cognitive ability, and it has a significant effect on learning programming concepts, such as recursion and iteration. Therefore, a good programmer should possess the ability to abstract from the implementation of recursion to understand the whole picture. However, we are not sure whether gifted student programmers are able to understand recursion or not. Although measuring student abstraction ability is a challenging task, previous CSE research has established a way to incorporate SOLO taxonomy to analyse student code-writing ability, which can help to investigate student abstraction ability.

Different coding strategies have been suggested, including coding standards, debugging, and optimisation. In the educational context, coding strategies have been

taught to achieve best coding practices. However, in the software development industry, bespoke coding rules can be used that might be different from the educational context. Following these rules can be an important characteristic of a good programmer.

In relation to attitudes and personal traits derived from educational and IT professional contexts, learning style and personality might have an effect on the student learning process. In addition, the IT professional literature highlights the importance of communication and cooperation skills, which are considered important criteria for a good software developer. In addition, a software developer should be eager to learn new technologies and should be curious and creative.

The literature suggested important characteristics, such as mathematical abil- ities, abstraction, problem-solving strategies, attitudes, and personal traits. These characteristics have been derived from different contexts, including educational and professional, and they have a great deal of overlaps. In addition, CSE researchers pro- vides a great deal of effort in investigating the characteristics among novice students to provide them with support. However, there is a gap in the literature investigating the characteristics among gifted student programmers, as research in defining gifted- ness in programming and identifying gifted student programmers may not exist. These characteristics may or may not apply to CS educational contexts and gifted program- ming students may or may not exhibit specific characteristics. Therefore, our research questions have been developed to close that gap by investigating the characteristics of a gifted student within the context of programming.

CHAPTER

3.

Methodology

The key element of excellent research is a reliable and valid research methodology that can produce similar results if the research is conducted again. Of course, in social science research, different variables that are mostly human-based can affect research results. Thus, choosing a research methodology that produces valid results depends on two aspects. First, researcher beliefs, which indicate how researchers understand, interact with, and view the surrounding world, can result in several methods to conduct research using different methodologies. Second, research paradigms that correspond with research methods that serve the purpose of the research and answer the research questions are another characteristic.

This chapter is divided into five sections. The first discusses basic concepts of research philosophy paradigms. The second discusses various research methodologies that would be appropriate for this study. The discussion points out advantages and limitations of alternative methodologies along with justifications for selecting particular methodologies in this study. The third section describes the chosen data collection and analysis methods, and the fourth addresses the validity and reliability of these methodologies. Finally, ethical considerations are addressed.