Gender Discrepancies through the College Years

Gender Discrepancies through the College Years

Christie L. McDaniel (University of North Carolina at Chapel Hill, USA)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-098-1.ch009
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Women made significant contributions to the beginning of the computing revolution. For example, Ada Byron Lovelace helped write the first subroutine, the women of the ENIAC age programmed the first computer during World War II, and Admiral Grace Hopper wrote the first compiler. While there have been female pioneers in the field, today men dominate the world of information technology (Riemenschneider, Armstrong, Allen, & Reid, 2004). Gürer and Camp (2002) report that many science fields hold women in low esteem, and attempt to reject them. Moreover, women are actually declining as part of the technology workforce: they made up 41% of the information technology workforce in 1996, but in 2002 that proportion was down to 35% (Cockburn, 1999). Furthermore, the number of female university students currently studying information technology and computer science will not lead to an increase of females in the profession: in 2002, only 28% of all degrees in computer and information sciences went to women (NCES, 2003); in 2003, only 19% of computer science students were female (Wilson, 2003) and only 28% of the undergraduate students in information science were female (Saye & Wisser, 2004). In a time where women make up the majority of university students (NCES, 2003), why is information technology seeing the opposite trend (Zeldin & Pajares, 2000)? There are a number of theories as to why so few women have chosen to pursue a career in technology (Acker, Barry, & Esseveld, 1990; Cooper & Robinson, 1985; Wilson, 2003; Zeldin & Pajares, 2000). Furthermore, nearly all studies on the subject have been done in the United States (Irani, 2004; Lips, 2004; Wilson, 2003) while only one study cited here explored the gender gap among university students at the University of Hong Kong (Huang, Ring, Toich, & Torres, 1998). A number of feminist researchers believe that science (including technology) has a language that is masculine in nature (Acker et al., 1990). Furthermore, since our society understands gender as binary—that is, what is masculine is not feminine and vice versa—the very nature of science leaves women out. Once women get into the IT world, they face issues of personality and confidence that differ by gender. One theory, to be explored in depth here, is that women enrolled in introductory programming courses have less confidence in themselves than do their male counterparts and that the confidence level of female students decreases significantly between secondary and post-secondary education (Lips, 2004). In addition to being shaped by their comparisons of their performance with the performance of their male peers, women’s self-confidence is likely influenced by their experience of stress in their technology-oriented courses. These influences, combined with inaccurate views of IT careers, are influential in whether or not college students decide to work towards an IT-related major or choose another discipline all together (Irani, 2004; Zeldin & Pajares, 2000).
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General Overview

In today’s digital economy, knowledge is regarded as an asset, and the implementation of knowledge management supports a company to developing innovative products and making critical management strategic decisions (Su, Chen, & Sha, 2005). This digital economy has caused a tremendous explosion in the amount of data that manufacturing organizations generate, collect, and store, in order to maintain a competitive edge in the global business (Sugumaran & Bose, 1999). With global competition, it is crucial for organizations to be able to integrate and employ intelligence knowledge in order to survive under the new business environment. This phenomenon has been demonstrated in a number of studies, which include the employment of artificial neural network and decision tree to derive knowledge about the job attitudes of “Generation Xers” (Tung, Huang, Chen, & Shih, 2005). The paper by Tung et al. (2005) exploits the ART2 neural model using the collected data as inputs. Performance classes are formed according to the similarities of a sample frame consisting of 1000 index of Taiwan manufacturing industries and service firms. While there is a plethora of data mining techniques and tools available, they present inherent problems for end-users such as complexity, required technical expertise, lack of flexibility, and interoperability, and so on. (Sugumaran & Bose, 1999). Although in the past, most data mining has been performed using symbolic artificial intelligence data mining algorithms such as C4.5, C5 (a fast variant of C4.5 with higher predictive accuracy) and CART (Browne, Hudson, Whitley, Ford, & Picton, 2004), the motivation to use decision tree in this work comes from the findings of Zhang, Valentine, & Kemp, (2005). The authors claim that decision tree has been widely used as a modelling approach and has shown better predictive ability than traditional approaches (e.g., regression). This is consistent with the literature by considering the earlier study by Sorensen and Janssens (2003). The authors conduct an exploratory study that focuses on the automatic interaction detection (AID) — techniques, which belongs to the class of decision tree data mining techniques.

Decision tree is a promising new technology that helps bring business intelligence into manufacturing system (Yang et al., 2003; Quinlan, 1987; Li & Shue, 2004). It is a non-parametric modelling approach, which recursively splits the multidimensional space defined by the independent variables into zones that are as homogeneous as possible in terms of response of the dependent variable (Vayssieeres, Plant, Allen-Diaz, 2000). Naturally, decision tree has its limitations: it requires a relatively large amount of training data; it cannot express linear relationships in a simple and concise way like regression does; it cannot produce a continuous output due to its binary nature; and it has no unique solution, that is, there is no best solution (Iverson & Prasad, 1998; Scheffer, 2002). Decision trees are tree-shaped structures that represent sets of decisions. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) (Lee & Siau, 2001).

Figure 1 is a good illustrative example of potential sources of data for mining in manufacturing. The diagram shows the various areas of manufacturing where massive data are generated, managed, and used for decision making. Basically, nine aspects of the manufacturing organization are discussed: production system, customer relations, employee database, contractor/supplier unit, product distribution, maintenance, transportation, research and development, and raw materials.

Figure 1.

Data generated in a modern manufacturing system


The production system is concerned with transformation of raw materials into finished goods. Daily production and target figures are used for mining purposes. Trends are interpreted and the future demand of products is simulated based on estimation from historical data. Data on quality that are also mined relate to the number of accepted products, the number of scraps, and reworks, and so forth. The maintenance controller monitors trends and predicts the future downtime and machinery capacity data. Customer relations department promotes the image of the company through programs. This department also monitors the growth of the company’s profit through the number of additional customers that patronize the company, and also monitors libel suits against the company in the law courts.

Data are also mined from the employee database. Patterns observed in this database are used to predict possible employee behaviour, which include possibility of absence from duty. Practical data mining information could be obtained from an example of a production supervisor who was last promoted several years ago. If a new employee is engaged and placed higher than him, he may reveal the frustration by handling some of the company’s resources and equipment carelessly and with levity. A large amount of data could be obtained from historical facts based on the types and weights of the raw materials usage, quantity or raw materials demanded, location of purchase, prices and the lead-time to supply, and more. Yet another important component of modern manufacturing system is research and development. For product distribution activities, the data miner is interested in the population density of people living in the distribution centers, the number of locations covered by the product distribution, the transportation cost, and so on.

The contractor/supplier unit collects data on the lead-time for product delivery to customers. This information would be useful when considering avoidance of product shortage cost. The transportation unit spends an enormous amount of money on vehicle maintenance. Historical data on this would guide the data mining personnel on providing useful information for the management.

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