Natal Context (Westville Campus) 51 4.3 Selection of Research Participants 52 4.4 Confidentiality Issue 52 4.5 Thinking Styles of First Year IT Students 52. This chapter therefore discusses the need for research on such thinking styles in the IT industry.
The need for Thinking Styles in IT
The envisaged contribution of this study to IT
Problem Statement
This study tests this approach with reference to students and professionals in the field of Information Systems and Technology. Therefore, this study focuses on the miscommunication and misunderstandings between individuals, in determining the way in which one collects, acquires and shares information and how the entire field of knowledge acquisition, creation, sharing and management can be better informed by an investigative systems approach .
Layout of the Research
The final chapter concludes the research by answering the research questions in chapter two, section 2.12, and provides some recommendations and guidelines from the study.
Conclusion
Introduction
Characteristics of the Five Inquiring Systems
T h e Five thinking styles
Consequently, these researchers have created a test called the I n Q, which, when taken, will assess a preference for different thinking styles. The five thinking styles include the synthetic, the idealist, the pragmatist, the analyst, and the realist.
Definitions of thinking styles
- T h e Synthesist
- T h e Pragmatist
- T h e Analyst
- T h e Realist
They also discovered that synthesizers are interested in conflict and also like change – often for its own sake. They are also likely to be most interested in formulating strategies and tactics to get things done, and they often enjoy being liked, approved of, or at least accepted.
Multiple thinking styles
- Idealist-Analyst (I-A)
- Analyst-Realist (A-R)
- Synthesist-Idealist (S-I)
- Idealist-Realist (I-R)
- Pragmatist-Realist (P-R)
- Idealist-Pragmatist (I-P)
- Analyst-Pragmatist (A-P)
- Analyst-Synthesist (A-S)
- Synthesist-Pragmatist (S-P)
- Synthesist-Realist (S-R)
- Three Way Thinkers
- Flat Profile
People who have a strong preference for three of the five styles tend to be more creative. These people tend to be unpredictable, less intense and less recognizable than people with a strong preference for other styles.
Differences between thinking styles
The synthetic-realist is extremely rare due to the fact that the syntheticist and the realist are at opposite ends of the spectrum of thinking. Both the synthesist and the realist tend to become easily impatient, especially with excessive analysis and rambling discussions.
T h e Usefulness of the Inquiring Systems Approach
Neither the Synthesist nor the Idealist approach is best when the problem is one that is well-formulated, structured, and can be calculated or put into mathematical terms. DeLisi (2002) finds that the questionnaire gives an indication of how people process information; something that IT professionals can easily relate to.
Strengths and Weaknesses of different Thinking Styles
An idealist may procrastinate with too many choices and strive too hard for perfect solutions (Harrison and Bramson, 1984). The realist's weakness is that he may rush to oversimplified solutions and try too hard for consensus (Harrison and Bramson, 1984).
Previous applications of the Inquiring Mode Questionnaire
Their solutions tend to be somewhat riskier than Synthesist's, but are more innovative with a better payoff. Some of the disadvantages of being a pragmatist are that he may rush too quickly to payoffs and may try to do it too expediently (Harrison and Bramson, 1984).
T h e impact of thinking styles in academia
Zhang (2002b) states that thinking styles contribute to the academic achievement of IT students beyond what can be explained by abilities. It also found that thinking styles were related to IT academic achievement and had implications for teacher training.
H o w the thinking styles of IT practitioners influence their different approaches to Knowledge Management
- Knowledge and Information
- Definitions of Knowledge Management
- Systems Approach to Knowledge Management
- T h e People D i m e n s i o n to Knowledge M a n a g e m e n t
- U s e of I T in Knowledge Management
Lombo therefore argues that Knowledge Management predates the invention of writing systems approximately 5000 years ago. In effect, these people become key components of the Knowledge Management System; the systems cannot function without it (King, 2000). Davis (1998) argues that Knowledge Management requires fundamental change in the way most organizations do business.
MetaKnowledge Management and Knowledge Management
According to Furlong (2003), managing the IT infrastructure for Knowledge Management is a critical success factor for an organization. Wiig (1998) supports this argument by stating that the understanding of the human cognitive function is important in knowledge management. Therefore, it is important to better professional understanding of cognitive aspects of how knowledge understanding, mental models and associations influence decision-making and the performance of knowledge-intensive work when deciding how to perform knowledge management.
Characteristics of IT practitioners
He further points out that people and their work behavior are central to the effective enterprise. Therefore, it is important to gain a better professional understanding of the cognitive aspects of how knowledge understanding, mental models and associations influence decision-making and the performance of knowledge-intensive work when deciding how to perform knowledge management. 1998) argue that communication skills are critical for determining needs and allocating resources. General management – understanding both the business and the company's markets; Organizational development capabilities and a broad background in various facets of the business are essential to the success of the company.
Research Questions
Conclusion
Introduction
Aim of the research
T h e purpose of the study
Study setting
Unit of Analysis
Quantitative and Qualitative Data
Measuring Instrument
Selection of elicitation instrument
Sampling Technique
The procedure used for selecting a sample of I T first level students included entering the names of the students into Microsoft Excel and thereby selecting the appropriate sample by randomly generating numbers statistically. The samples for the IT practitioners were conveniently selected from the IT academics at the University of KwaZulu Natal (Westville Campus) IT department. These samples include a sufficient number of elements from the population so that a study of the sample and an understanding of the characteristics or characteristics of the sampled persons will be possible to generalize the characteristics or characteristics to the population elements (Sekaran.
Data Collection Method
The same procedure was used to select samples from other populations, mainly students studying Information Systems and Technology at second and honors levels. The thinking styles of Information Systems and Technology students were measured using the I n Q questionnaire and then these scores determined their thinking styles. The thinking styles of Information Systems and Technology practitioners were measured using the same questionnaire.
Data capturing and analysis
- Variance
- T h e Standard Deviation
- Pearson Correlation Coefficient
- Significant M e a n Differences A m o n g Multiple Groups: A N O V A
The variance is also calculated to analyze the standard deviation of each of the factors in the questionnaire, that is, the way in which the scores of thinking styles are spread among the respondents. The standard deviation is usually preferred to the variance because it is expressed in the same units as the original measurements (Huysamen, 1990). The standard deviation has been used to analyze the factors in the questionnaire, i.e. the measure of dispersion of thinking style scores between the respondents.
Bias In Research
It is useful when trying to determine whether there is a significant relationship between two variables (Huysamen, 1998). Pearson's correlation coefficient has been used to establish the relationship between IT students' thinking style and their exam grade. Where the t-test would indicate whether there is a significant mean difference in a dependent variable between two groups, an analysis of variance (ANOVA) helps examine the significant mean differences between more than two groups on an interval or ratio scaled dependent variable ( Sekaran, 2000:406).
Limitations
IT positions should also be explored, as this directly affects the thinking style of IT professionals. Although there is a link between knowledge management, knowledge creation and thinking styles, it should be noted that the questionnaire does not allow direct comparison with the content of the exam field. The anonymity of the questionnaire also made it difficult to correlate exam scores with people's results, making critical assumptions difficult.
Summary and conclusions
It also provides some information about the choice of participants and the issue of confidentiality.
Introduction
Information Systems and Technology in the University of KwaZulu-Natal (Westville Campus) context
Choice of research participants
T h e question of confidentiality
Thinking Styles of First Year IT Students
T h e Results
It is important to note that IT people need to think in more than one thinking style. The difference in range also indicates that the scores for each respondent in each thinking style were quite close to each other. Three of the thinking styles show a positive relationship between the thinking style and students' exam scores.
Discussion of the Results
The idealist, on the other hand, has a low negative correlation because he delays having too many choices and strives too hard for perfect solutions. The realist produces a low positive correlation because he rushes to oversimplified solutions and strives too hard for consensus (Harrison and Bramson, 1984). Therefore, these students show a high negative correlation, because they cannot fall for the ideas of others, since the exam requires students to work on their own.
Thinking Styles of Second Year IT Students
T h e Results
This shows that IT people must have equal leverage of thinking styles to remain productive in all aspects of their work. Four of the thinking styles show a positive relationship between the style of thinking and the students' exam marks. T he strongest relationships exist between the Synthesist and Pragmatist thinking styles and their relevant exam scores.
Discussion of the Results
The correlation between the exam grades and the one-way thinkers showed a low positive correlation of 0.18, while the two-way thinkers had no correlation between their exam grades and either of their thinking styles.
Thinking Styles of IT Honours Students
T h e Results
Thirty-three percent of students scored 60 or higher on this way of thinking. Two students who were idealists correlated perfectly with their exam grades, achieving a correlation of l. Idealists are goal-oriented and future-oriented, very receptive and show a broader view. The correlation between their exam scores and thinking style was 0.01, which is quite low.
Discussion of the Results
Conclusion
Introduction
T h e research and its findings
Therefore, the author concludes that as the IT practitioner develops from an IT student to an IT practitioner, he is likely to develop a thinking style that fits the characteristics of his career. This can be confirmed by a report by DeLisi (2002) who states that the training that I T professionals receive focuses on analytical skills and code development and debugging work that requires a great attention to detail. Therefore, this statement is consistent with Harrison and Bramson's (1984) definition that the Analytical thinking style approaches problems in a careful, logical and methodical manner; paying great attention to detail.
Research Questions
For the last research question in this study, it was found that a general guideline for a thinking style profile cannot be made because each individual thinks differently.
Guidelines for I T practitioners and students
Staff should also be trained in communication strategies and learn about the different types of thinking styles that people have and how to manage them. Academics should also ensure that this component is also covered by students before group work begins. This can help them understand each other's thinking styles and the usefulness each contributes to problem solving, as discussed in Section 2.5.
Recommendation
Organizations and academic departments should also develop support facilities such as training sessions, debate groups, and such to help staff or students with problems. For one to become an idealist, data and theory must be considered equal. IT managers and academics need to educate their staff and students about thinking styles in general and the purpose they serve.
Future research
To become more of a pragmatist, practice incremental thinking and encourage others to experiment and learn to think tactically. To develop your ability to think like a realist, be specific and give examples when explaining an idea and ask others for examples when making abstract statements. Unless this is done, the common assumption that all men think alike will prevail.
Conclusions
An Inquiry into the Thinking Styles of IT Executives and Professionals, [Internet] available at http://www.org-synergies.com/thinkingStyles.htm [accessed July 17, 2003]. Effectively accumulating students' thinking styles through the World Wide Web: experiences with construction and application, International Journal of Instructional Media, vol. dissertation, University of Zululand: South Africa. Thinking styles and mindsets: implications for education and research, T h e Journal of Psychology, vol.
APPENDIX B: QUESTIONNAIRE
Only important in relation to the conclusions to be drawn from them 5.” No more or less important than the narrative.
WERE TO BE TESTED, I WOULD PREFER
