Calibrating a Measure of Gender Differences in Motivation for Learning Technology
Hwang, Young Suk, Fisher, William, Vrongistinos, Konstantinos, Journal of Instructional Psychology
This paper reports on the theory, design, and calibration of an instrument for measuring gender difference in motivation for learning technology. The content of the instrument was developed based upon the motivational theories of Eccles and others. More specifically, the learners' self-concept of ability, perception of technology, perception of parental beliefs, causal attributions (success and failure), value factors, and gender issues in using technology were investigated. The function of the instrument was evaluated according to the principles of Measurement theory, using a Rasch rating scale measurement model.
The Global society is increasingly technological. Consequently, keeping pace with other industrialized nations requires educators to ensure that students who are future entrants into the workforce have the necessary knowledge and skill in learning and using technology (Gore, 1999). Unfortunately, the number of American students pursuing degrees in the sciences or technology is smaller than those in the poorest developing nations (Committee on Equal Opportunities in Science and Engineering, 2000).
Within the American population, it is apparent that there is a significant gender gap in pursuing scientific and technical careers. According to Fountain's report (1999), from 1984 to 1999 the percentage of undergraduate computer science degrees awarded to women has decreased from 37% to less than 20%. The rate of female and male high school students completing the advanced levels of mathematics and science courses is almost same. More than half of female students took Advanced Placement tests. However, only 10% of computer science test takers in 1999 were female.
Why are women choosing not to pursue technology careers? Although many factors contribute to the gender gap in pursuing careers in technology, one is of particular interest to educators and researchers: motivation. In the past decade a number of mathematics and science researchers have identified the important role of motivational factors related to students' career choices. According to expectancy-value theories (Eccles, 1983,1987, 1994; Eccles & Wigfield, 1995; Wigfield 1994; Wigfield & Eccles, 2000), motivation to perform a certain task is strongly influenced by one's expectation of success or failure and the value or appreciation the individual places on the task. For example, those who believe that using technology/computer is important or easy are more likely to desire a career in a technology/computer field and outperform those who do not hold such beliefs. The expectancy-value model has been shown to predict students' career choices and their academic performances in different subject matters (Eccles, 1994; Pintrich & Schunk, 1996). However, compared to the numerous studies on the academic subjects of mathematics and reading, there has been little research on gender differences in motivation of using technology. Therefore, our understanding of gender imbalance in technology career choices is still largely based upon research concerning motivation in mathematics and reading.
As a first step toward understanding what women's technology related motivations may be, the present study investigates the function of an instrument for measuring motivational factors in learning technology. The content of the instrument was developed based upon the motivational theories of Eccles and others (Eccles, 1983,1987,1994; Eccles & Wigfield, 1995; Pintrich & Schunk, 1996; Weiner, 1985, 1992, 1995; Wigfield 1994;Wigfield & Eccles, 2000).The function of the instrument was evaluated according to the principles of Measurement theory, using a Rasch rating scale measurement model.
Achievement expectations and value theories can be conceptualized in terms of five components: tasks values, students' perceptions of their own ability, perceptions of parental beliefs, perceptions of technology, and causal attribution patterns for success and failure. …