The Effects Of Gender, Engineering Identification, And Engineering Program Expectancy On Engineering Career Intentions: Applying Hierarchical Linear Modeling (HLM) In Engineering Education Research

This study had three purposes and four hypotheses were tested. Three purposes: (1) To use hierarchical linear modeling (HLM) to investigate whether students’ perceptions of their engineering career intentions changed over time; (2) To use HLM to test the effects of gender, engineering identification (the degree to which an individual values a domain as an important part of the self), and engineering program expectancy (one’s belief in the possibility of his or her success in engineering) on the growth trajectory of students’ engineering career intentions; and (3) To introduce the uses of longitudinal design and growth curve analysis in engineering education research. Survey data was collected at four time points using measures that produce scores with known validity. Sample sizes at each time point were 470, 239, 129, and 115, respectively. We used SPSS 22.0 to perform descriptive statistics and reliability analyses, and HLM version 7.0 to analyze growth. Between their first and third years, undergraduate students’ perceived engineering career intentions neither grew nor declined significantly, with no significant difference between male and female students. Engineering identification significantly predicted individual differences when controlling for engineering program expectancy, whereas engineering program expectancy did not predict career intentions when controlling for engineering identification. These findings are possibly signs of overall stabilization of the declining trends in career intentions and reversal of women’s perceptions of commitment to engineering careers. The contributions and limitations of this study are also discussed.&nbsp

Effects of an Active Learning Approach on Students’ Motivation in an Engineering Course

Because there are many positive effects of active learning approaches on students’ motivation and achievement, some authors have recommended that these approaches be widely implemented. A research-intensive university located in the Mid-Atlantic US was interested in adopting this instructional technique, and therefore, experimented with it. The purpose of this quasi-experimental study was to compare and contrast the effects of an active learning approach on the motivation of students in a treatment and control group. The results of multiple independent sample t-tests showed that there were no statistically significant differences between the two groups on several motivation constructs. We provide explanations for the lack of significant differences, as well as discuss limitations and future research

Validating the National Survey of Student Engagement (NSSE) at a Research-Intensive University

The National Survey of Student Engagement (NSSE) has been used at universities across the U.S. and Canada to gather information about the quality of engagement of first-year students and graduating students. Institutions use NSSE’s five benchmarks of effective educational practice to compare themselves with other schools and to focus in on ways to improve the educational experiences of their students. However, studies indicate that these benchmarks may not be a valid way to convey NSSE information. This study was conducted to investigate whether or not NSSE’s five-factor model is the best fit for student engagement data collected at a large, public, research-intensive, land-grant university. The five-factor model did not fit the data for the 2008 sample of senior students at this university. Rather, a revised model using six factors instead of five and 21 of 42 items provided a more valid test blueprint. This new model was then tested and found to fit the 2011 sample of senior students at the same university. Discussion regarding use of a nationally collected data at an individual institution is provided

Students’ educational experiences and interaction with residents on night shifts

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137686/1/tct12561.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137686/2/tct12561_am.pd

Validating the National Survey of Student Engagement (NSSE) at a Research-Intensive University

The National Survey of Student Engagement (NSSE) has been used at universities across the U.S. and Canada to gather information about the quality of engagement of first-year students and graduating students. Institutions use NSSE’s five benchmarks of effective educational practice to compare themselves with other schools and to focus in on ways to improve the educational experiences of their students. However, studies indicate that these benchmarks may not be a valid way to convey NSSE information. This study was conducted to investigate whether or not NSSE’s five-factor model is the best fit for student engagement data collected at a large, public, research-intensive, land-grant university. The five-factor model did not fit the data for the 2008 sample of senior students at this university. Rather, a revised model using six factors instead of five and 21 of 42 items provided a more valid test blueprint. This new model was then tested and found to fit the 2011 sample of senior students at the same university. Discussion regarding use of a nationally collected data at an individual institution is provided