Research & Teaching
By Christine King, Kameryn Denaro, and Brian Sato
There has been a significant amount of research that highlights issues in engineering education with differential success for different groups of undergraduates (de Cohen & Deterding, 2009; Garrison, 2013; Malicky, 2003; Sax et al., 2016; Varma, 2018). This is particularly true in engineering for women, whose low retention rate has been a continual issue for the past 40 years (Sax et al., 2016). Despite more women students entering the field of engineering in recent years, the gender gap is expanding, as engineering is becoming more popular as a whole, and male enrollment continues to outpace female student enrollment (Sax et al., 2016).
Several factors have been identified to explain the consistent gender gap in undergraduate engineering programs. Studies have found that women and underrepresented minorities have insufficient preparation and experience barriers in recruitment into engineering programs, possess lower self-efficacy and a lack of peer support, and face harmful stereotypes that influence interactions in classrooms or within peer groups (Barnes et al., 2018; Eddy & Brownell, 2016; Foor et al., 2007; Lowe et al., 2018; Mariano et al., 2018; Ohland et al., 2011; Palmer et al., 2011). These challenges might be compounded by engineering faculty and professional engineers who may perpetuate culture issues that arise in the classroom (Solanki & Xu, 2018). Furthermore, for students from underrepresented minority groups, a lack of peer support through group work and learning communities (Backer et al., 2018; Dagley et al., 2016; Palmer et al., 2011) and a need to “see more people that look like them among the faculty” (Barnes et al., 2018, p. 11), were also considered significant barriers to retention in engineering disciplines.
One means to address issues for students from underrepresented groups is to be intentional regarding the makeup of student groups in engineering courses (Dasgupta et al., 2015). By systematically increasing the presence of female students and students from underrepresented minority backgrounds among peers working in groups, it is possible to generate positive effects on engagement in course activities. A study that analyzed the importance of group composition for first-year engineering group work (Dasgupta et al., 2015) found that females feel less anxious in female-majority groups, as female peers enhanced their confidence and career aspirations. This effect was particularly significant for first-year college students, as advanced engineering students did not have the same variance in feelings of threat in the female minority group. However, both first-year and advanced students had a more significant willingness to speak up in female majority groups during group problem-solving. This is consistent with the stereotype inoculation model (Dasgupta, 2011; Stout et al., 2011), as the sex composition of working groups has a significant impact on women’s situational appraisals (Dasgupta et al., 2015).
Working in teams is an essential skill for engineering students to become workforce ready, as engineers are often required to work in multidisciplinary and heterogeneous teams (Bhavnani & Aldridge, 2000; Brunhaver et al., 2018). Desirable teamwork skills include understanding group dynamics; supporting relationships between individuals, teams, and the task; establishing practices that build trust; and resolving conflicts (Itani & Srour, 2016; Johnson & Johnson, 1991). However, these skills are only marginally obtained after completion of an undergraduate engineering education (Bahner, 1996; Patil, 2017). As a result, many efforts have focused on better preparing undergraduate engineers through team cohesion activities using groupware systems (Conole & Dyke, 2004; Ford & Morice, 2003; Whatley, 2009). Researchers utilizing groupware have found that the use of communication tools and purposeful team diversity can improve inclusion (Hui & Farnham, 2016).
Team groupware systems are used to establish positive team dynamics by having members identify their expectations for group work and the norms they want to establish to encourage team cohesion at the start of a project (Whatley, 2009). We have implemented a similar groupware system in lower-division, large-enrollment undergraduate engineering courses with an online survey that established team roles, a meeting schedule, and which ground rules and norms the team will follow (King, 2019). In this work, we found that establishing ground rules via this online team system helped students develop more professional teamwork skills throughout the project and provided them with insight into proper work distribution (King, 2019). However, the study also found that there was a significant association between whether female versus male students found establishing ground rules to create team norms useful, as more male students than female students found the use of ground rules more acceptable to establish at the beginning of the project (King, 2019).
The values, expectations, and roles using groupware and ground rules systems have been found to be different among female students and students from underrepresented minority groups (Meadows & Sekaquaptewa, 2013). For instance, female students and students from racial and ethnic minority backgrounds are often assigned less important, nonpreferred roles and tasks when working in engineering teams (Fowler & Su, 2018; Ingram & Parker, 2002), and gender stereotyping often results in females being assigned to nontechnical positions within the group, such as organizational roles (Meadows & Sekaquaptewa, 2013). Female students and students from underrepresented minority backgrounds are also more likely than white male students to value collaboration and collective action through group communication as well as charismatic orientation over authenticity (Chin, 2013; Thoman et al., 2015).
To examine whether student demographic characteristics also influence the group rule selection process, we examined the following research questions:
Students in a large engineering design course at an R1 (highest research activity) university participated in the study (n = 128). Students were recruited from a sophomore-level engineering design course that focuses on SolidWorks computer-aided design (Dassault Systèmes, Waltham, MA) and prototyping for biomedical engineering applications and were provided with informed consent. All students, the instructor, and the teaching assistants (TAs) who participated in the study were provided with informed consent (Institutional Review Board approval number 2019-5084). The summary statistics for the student demographic characteristics are shown in Table 1.
Prior to forming teams, all students were asked to submit an individual team contract that determined their desired role on a team and which ground rules that they felt were most important to follow when working on a team. They were asked to choose five ground rules from the list in Table 2 that they deemed most important to improve team cohesion.
Once individual ground rules were chosen, students formed teams of five or six students and engaged in a “negotiation” with their team where the group chose the top five rules the team would follow and the team roles each individual would play (designated as project manager, manufacturer, materials engineer, lead designer, and researcher). These roles were chosen for the teams because they highlight major roles that are required in a design and prototyping team to design, develop, and test a new device. Specifically, the project manager oversaw the project’s progress and was in charge of ensuring the ground rules were followed by the team members; the manufacturer was in charge of the prototyping process; the materials engineer was in charge of materials choices during the design process; the lead designer was in charge of the assembly process; and the researcher was in charge of documentation and addressing the market and value of the innovative device.
After the negotiation process, each group submitted an online team contract that identified their team name, the five ground rules the team agreed to follow, team members’ specific roles, their team meeting schedule outside of class, and an audio recording of their negotiations. These negotiations were then followed by a brainstorming session for the team to determine which biomedical engineering application they are interested in solving within the timeframe of the 10 weeks of the course.
For the team project, students were instructed to apply their knowledge of SolidWorks to design, prototype, and test a biomedical engineering problem of interest that was disseminated through a project proposal, a mid-project in-class presentation, a final prototype live demonstration, and a final written report. The final report addressed existing research and objectives, a step-by-step design and corresponding engineering drawings of each part of the device, a step-by-step design and corresponding engineering drawings of the assembly, the team’s materials and prototyping design, a cost analysis, and the methods and results of their iterative testing of their prototype.
Statistical analyses were performed to identify the factors that contribute to an individual’s influence on the teams’ chosen ground rules. To investigate the relationship between the individual demographic characteristics (gender, team lead, first generation, and underrepresented minority status) and the influence of an individual (measured as the number of individual rules that were chosen among the teams’ ground rules for the project), separate Fisher’s exact tests were conducted. Although some demographic characteristics may have overlap, such as a first-generation female student, Fisher’s exact tests (Irwin, 1935; Yates, 1934) examine the relationship between each of the demographic characteristics and the influence of an individual. Furthermore, Fisher’s exact tests were used rather than a chi-square test due to the fact that some of the individual cell sizes were less than five; the data violated the large sample-size assumption of the chi-square test (Kim, 2017).
To determine whether specific demographic characteristics (e.g., gender, ethnicity, first-generation status, low-income status, and transfer-student status) impacted whether or not a specific rule was selected by the individuals prior to group discussion, we performed logistic regression analyses. Transfer-student status was used as one of the inputs to the regression model because it is hypothesized that transfer students may be less connected to the institution and the other students and thus might have a different influence over the group. The logistic regression model assesses the likelihood for an individual to choose a particular rule. Let xt = x1, x2, …xk be the predictors (where the k predictors are gender, ethnicity, first-generation status, low-income status, and transfer-student status). Let Y represent whether or not the individual chose the rule and the probability of the individual choosing the rule be denoted with p = P(Y = 1). We assume a linear relationship between the predictor variables and the log-odds of the event that the individual selected a particular rule. Assuming we have a sample of n independent observations (xi, yi), we obtain estimates for βt = (β0, β1, β2, …, βk). Thus, the model is given as log (p/1-p) = β0 + β1x1 + β2x2 + … + βkxk. Note that the p value of the regression model is the probability of obtaining that estimate of the parameter (e.g., the probability of obtaining that estimate for the intercept value of the model), or a more extreme value if the population value for that parameter is zero.
To assess whether certain individuals carry more weight in the group decision-making process, we used a Poisson regression model. The “overlap” of each individual was determined by calculating the number of ground rules chosen by an individual that were also chosen by their group. For example, if four out of the five rules chosen by an individual during the individual team contract were also chosen during the team’s contract negotiations, then this individual received an overlap of 4 (percentage overlap was 80%). Let xt = (x1, x2, …, xk) be the k predictors (where the predictors are gender, ethnicity, first-generation status, low-income status, and transfer-student status). Let Y be the number of individual rules overlapping with the group contract. We assume a linear relationship between the predictor variables and the logarithm of the expected value of the response. Assuming we have a sample of n independent observations, (xi, yi), we obtain estimates for βt = (β0, β1, β2, …, βk). The model is thus given as log (E (Y | xt)) = β0 + β1x1 + β2x2 + … + βkxk.
Poisson and logistic regression are generalized linear models that are used to model count data (Poisson) and dichotomous outcomes (logistic), and both have been well studied (Fahrmeir & Tutz, 1994; Frome, 1983; Gardner et al., 1995; McCullagh & Nelder, 1989; McCulloch & Searle, 2001; Nelder & Wedderburn, 1972). Poisson regression was used to model the number of individual ground rules that were chosen among the teams’ ground rules for the project. Logistic regression was used to model the odds of selecting a particular ground rule. Analyses were performed using the open-source programming environment R and the stats package (Bunn & Korpela, 2018). Specifically, the following demographic information was used as inputs to the regression model: low-income status, first-generation status, underrepresented minority status, gender, whether or not the individual is a transfer student, year in school based on number of credit units, cumulative grade point average (GPA), team role (whether or not they were chosen as team lead), and group project score. In addition, GPA and year in school were included because higher-performing students may have a stronger influence over group decisions in a classroom setting.
Descriptive analysis of the individual and team-chosen ground rules found that the rules “complete agreed work on time” and “play an active part in the team” were the most popular ground rules selected (Table 3). Conversely, the least common rules selected by both individuals and groups were “value diversity” and “send apologies if unable to attend.” This was also consistent among individuals, as few students chose these rules as important (e.g., only 8.5% of 136 students chose “value diversity” as an important ground rule to follow within their group). Overall, individual selection and group selection were fairly consistent, with an exception being “respect each other,” which was not selected by any individuals as one of their top five most important rules yet was selected by 20.2% of groups.
We were curious to see whether certain groups of students were more likely to select specific rules based on their gender, underrepresented-minority status, first-generation status, low-income status, and transfer-student status. Selection of the vast majority of the rules was not impacted by student demographic characteristics (Tables 4 and 5; see also Tables A1–A11 in the online appendix). The exception to this was that female students were more likely to select “play an active part in the team” relative to their male counterparts (Table 4). Additionally, the “read and respond to messages within agreed time” was also more likely to be selected by female students and first-generation students compared with their continuing-generation peers (Table 5).
Once students individually selected the rules that were most important for them, each group discussed which rules they would collectively include as guidelines for the group interactions during the academic term. The following analysis examined the overlap (the number of rules an individual chose that also were selected as group rules) for each student to identify where specific groups of students were more or less likely to be successful at having their preferred rules adopted as group rules. Based on a regression analysis, none of the student demographic characteristics we examined impacted the overlap with respect to the group rules (Table 6).
To see whether group decision-making in a lower-division, large-enrollment engineering course is impacted by student demographic characteristics and to identify the rules of group work that students valued, we examined the group rule selection process students undertook at the beginning of the academic term. The findings presented demonstrate that different student group populations do not have a significant influence over which ground rules are chosen when forming a team and establishing norms and expectations. This is highlighted by the fact that characteristics of individual team members and which individual rules they chose compared to the chosen ground rules of the teams were not predictive.
Students found that “complete agreed work on time” and “play an active part in the team” were the most valuable rules and norms, both individually and as a team. Conversely, the rules “value diversity” and “send apologies if unable to attend” were chosen the least by both individuals and teams. This is problematic because education and industry fields have been trying to increase diversity in engineering (Backer et al., 2018; Dagley et al., 2016), and that one would imagine that “valuing diversity” is something we should embrace as a field. It is important for faculty and students to stress the importance of team diversity (Chin, 2013; Thoman et al., 2015).
The rule “respect each other” was not selected by any individual but was chosen by 20% of the teams. The fact that students valued this rule as a group may highlight desirable teamwork skills being developed upon group formation, such as supporting relationships among individuals, teams, and the task and establishing practices that build trust, such as respecting each other and shared values (Johnson & Johnson, 1991; Missingham & Matthews, 2014). Furthermore, it is possible that the negotiation process allowed students to reflect on their own thinking, leading to novel outcomes, a known benefit of group work.
When examining rule selection, we found that students from different demographic groups agreed with the importance of the vast majority of the rules. The exception to this is that female students were more likely to select “play an active part in the team” compared with their male counterparts. This was also true for first-generation students compared with their continuing-generation peers. This finding highlights that female and first-generation students value collective action and values more than their male or continuing-generation peers. This is consistent with prior studies (Chin, 2013; Thoman et al., 2015) that found that collective action and altruistic rules of the team are valued as more important for female students and students from racial and ethnic minority backgrounds than for white male students. This is also reflected in the finding that female and first-generation students valued the importance of active communication, as they were more likely to select “read and respond to messages within agreed time.” Conversely, this finding may be due to the fact that female students and students from underrepresented minority backgrounds are often placed in nontechnical roles such as organizational roles (Meadows & Sekaquaptewa, 2013), which require active messaging among a team.
The finding that there was no difference in overlap by student demographics, despite known issues with representation (de Cohen & Deterding, 2009; Garrison, 2013; Malicky, 2003; Sax et al., 2016; Varma, 2018) and team cohesion (Chin, 2013; Thoman et al., 2015) in engineering for students from underrepresented groups and female students, is a positive finding. This finding highlights that despite a lower representation of these students among the teams in general, it did not lead to an imbalance in group decision-making regarding ground rules.
As individual rule selection did not vary much by student demographics, it is perhaps not surprising that specific demographic characteristics also did not predict a greater overlap score. Still, it is promising that there was considerable agreement among individuals, as the engineering education literature does not point to such a harmonious scenario. This may also be influenced by the particular site at which this work was conducted, as the institution is a Hispanic-serving institution with a diverse student body. The study population alone was 40% female and one-third students from underrepresented racial and ethnic minority groups and first-generation students. It may be the case that consistently being around students of other backgrounds leads to greater open-mindedness when conducting group work in the classroom; this also highlights the importance of making engineering fields more heterogeneous.
We found that demographics and educational background did not have an influence on decision-making regarding team ground rules. This is a positive finding, as it was determined that all types of students had an equal amount of input over which rules were established among a team as important to use and follow throughout a project. Furthermore, even though analysis of the audio recordings was unable to distinguish which student identified with which demographic during the negotiation process, the authors were able to conclude from these recordings and surveys that all students had an equal amount of influence over the rules established among the team. Although our prior study (King, 2019) found that female students did not find the ground rules as useful as male students, they still had contributions equal to their male peers concerning which rules were chosen when comparing their individual rules to those the team chose. This finding is significant, as the percentage distribution of male to female students in biomedical engineering programs is closer to equal than it is in other engineering disciplines. Given this equality in gender distribution, the study highlights the importance of improving equal demographic distribution of students in engineering disciplines and suggests that retention of underrepresented students can improve team cohesion and project formation in engineering design courses.
Christine King (email@example.com) is an assistant professor of teaching in the Department of Biomedical Engineering, Kameryn Denaro is a project scientist in the Division of Teaching Excellence and Innovation, and Brian Sato is a professor of teaching in the Department of Molecular Biology and Biochemistry, all at the University of California Irvine in Irvine, California.
Backer, P., Green, J., Matlen, B., & Kato, C. (2018, April 29–May 2). Impact of first-year initiatives on retention of students: Are there differences in retention of students by ethnicity and gender? [Paper presentation]. CoNECD—the Collaborative Network for Engineering and Computing Diversity Conference, Crystal City, VA, United States. https://scholarworks.sjsu.edu/aviation_pub/18
Bahner, B. (1996). Report: Curricula need product realization. Mechanical Engineering—CIME, 118(3), S1.
Barnes, T. N., Zhang, X., Trauth, A. E., Enszer, J., Rooney, S., Davidson, R., & Buckley, J. M. (2018, June 23–27). How granular is the problem? A discipline-specific focus group study of factors affecting underrepresentation in engineering undergraduate programs [Paper presentation]. ASEE Annual Conference, Salt Lake City, UT, United States.
Bhavnani, S. H., & Aldridge, M. D. (2000). Teamwork across disciplinary borders: A bridge between college and the work place. Journal of Engineering Education, 89(1), 13–16.
Brunhaver, S. R., Korte, R. F., Barley, S. R., & Sheppard, S. D. (2018). Bridging the gaps between engineering education and practice. In R. B. Freeman & H. Salzman (Eds.), U.S. engineering in a global economy (pp. 129–163). University of Chicago Press.
Bunn, A., & Korpela, M. (2018). An introduction to R. Washington University.
Chin, J. L. (2013). Diversity leadership: Influence of ethnicity, gender, and minority status. Open Journal of Leadership, 2(1), 1–10.
Conole, G., & Dyke, M. (2004). What are the affordances of information and communication technologies? ALT-j, 12(2), 113–124.
Dagley, M., Georgiopoulos, M., Reece, A., & Young, C. (2016). Increasing retention and graduation rates through a STEM learning community. Journal of College Student Retention: Research, Theory & Practice, 18(2), 167–182.
Dasgupta, N. (2011). Ingroup experts and peers as social vaccines who inoculate the self-concept: The stereotype inoculation model. Psychological Inquiry, 22(4), 231–246.
Dasgupta, N., Scircle, M. M., & Hunsinger, M. (2015). Female peers in small work groups enhance women’s motivation, verbal participation, and career aspirations in engineering. Proceedings of the National Academy of Sciences, 112(16), 4988–4993.
de Cohen, C. C., & Deterding, N. (2009). Widening the net: National estimates of gender disparities in engineering. Journal of Engineering Education, 98(3), 211–226.
Eddy, S. L., & Brownell, S. E. (2016). Beneath the numbers: A review of gender disparities in undergraduate education across science, technology, engineering, and math disciplines. Physical Review Physics Education Research, 12(2), 020106. https://doi.org/10.1103/PhysRevPhysEducRes.12.020106
Fahrmeir, L., & Tutz, G. (1994). Multivariate statistical modelling based on generalized linear models. Springer-Verlag.
Foor, C. E., Walden, S. E., & Trytten, D. A. (2007). “I wish that I belonged more in this whole engineering group”: Achieving individual diversity. Journal of Engineering Education, 96(2), 103–115.
Ford, M., & Morice, J. (2003, June 24–27). Using micro management techniques to overcome problems in group assignments [Paper presentation]. Informing Science and Information Technology Education Joint Conference, Pori, Finland. http://www.proceedings.informingscience.org/IS2003Proceedings/docs/161Ford.pdf
Fowler, R. R., & Su, M. P. (2018). Gendered risks of team-based learning: A model of inequitable task allocation in project-based learning. IEEE Transactions on Education, 61(4), 312–318. https://doi.org/10.1109/TE.2018.2816010
Frome, E. L. (1983). The analysis of rates using Poisson regression models. Biometrics, 39(3), 665–674.
Gardner, W., Mulvey, E. P., & Shaw, E. C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin, 118(3), 392–404.
Garrison, H. (2013). Underrepresentation by race–ethnicity across stages of US science and engineering education. CBE—Life Sciences Education, 12(3), 357–363.
Hui, J. S., & Farnham, S. D. (2016, November 13–16). Designing for inclusion: Supporting gender diversity in independent innovation teams [Paper presentation]. 19th International Conference on Supporting Group Work, Sanibel Island, FL, United States.
Ingram, S., & Parker, A. (2002). Gender and modes of collaboration in an engineering classroom: A profile of two women on student teams. Journal of Business and Technical Communication, 16(1), 33–68.
Irwin, J. O. (1935). Tests of significance for differences between percentages based on small numbers. Metron, 12(2), 84–94.
Itani, M., & Srour, I. (2016). Engineering students’ perceptions of soft skills, industry expectations, and career aspirations. Journal of Professional Issues in Engineering Education and Practice, 142(1), 04015005. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000247
Johnson, D. W., & Johnson, F. P. (1991). Joining together: Group theory and group skills. Prentice-Hall.
Kim, H.-Y. (2017). Statistical notes for clinical researchers: Chi-squared test and Fisher’s exact test. Restorative Dentistry & Endodontics, 42(2), 152–155.
King, C. E. (2019, April 4–6). Assessing effectiveness of a ground rule system for group work in large engineering courses [Paper presentation]. 2019 Pacific Southwest Section Meeting, Los Angeles, CA, United States. https://peer.asee.org/assessing-effectiveness-of-a-ground-rule-system-for-group-work-in-large-engineering-courses
Lowe, D., Machet, T., Wilkinson, T., & Johnston, A. (2018, September 17–21). Diversity and gender enrollment patterns in an undergraduate engineering program [Paper presentation]. SEFI Annual Conference, Copenhagen, Denmark.
Malicky, D. (2003, June 22–25). A literature review on the underrepresentation of women in undergraduate engineering: Ability, self-efficacy, and the “chilly climate” [Paper presentation]. ASEE Annual Conference, Nashville, TN, United States.
Mariano, N., Miguel, A., Rempe, M., & Sloughter, J. M. (2018, April 29–May 2). Quantitative analysis of barriers to completion of engineering degrees for female-identifying and under-represented minority students [Paper presentation]. CoNECD—the Collaborative Network for Engineering and Computing Diversity Conference, Crystal City, VA, United States. https://peer.asee.org/29567
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Chapman and Hall.
McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models, John Wiley & Sons.
Meadows, L. A., & Sekaquaptewa, D. (2013, June 23–26). The influence of gender stereotypes on role adoption in student teams [Paper presentation]. ASEE Annual Conference Exposition, Atlanta, GA, United States.
Missingham, D., & Matthews, R. (2014). A democratic and student-centred approach to facilitating teamwork learning among first-year engineering students: A learning and teaching case study. European Journal of Engineering Education, 39(4), 412–423. https://doi.org/10.1080/03043797.2014.881321
Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Ohland, M. W., Brawner, C. E., Camacho, M. M., Layton, R. A., Long, R. A., Lord, S. M., & Wasburn, M. H. (2011). Race, gender, and measures of success in engineering education. Journal of Engineering Education, 100(2), 225–252.
Palmer, R. T., Maramba, D. C., & Dancy, T. E. (2011). A qualitative investigation of factors promoting the retention and persistence of students of color in STEM. The Journal of Negro Education, 80(4), 491–504.
Patil, T. V. (2017). Refining soft skills of engineering students to make them future ready. International Journal of Multidisciplinary Educational Research, 6(9), 211–215.
Sax, L. J., Kanny, M. A., Jacobs, J. A., Whang, H., Weintraub, D. S., & Hroch, A. (2016). Understanding the changing dynamics of the gender gap in undergraduate engineering majors: 1971–2011. Research in Higher Education, 57(5), 570–600.
Solanki, S. M., & Xu, D. (2018). Looking beyond academic performance: The influence of instructor gender on student motivation in STEM fields. American Educational Research Journal, 55(4), 801–835.
Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100(2), 255–270.
Thoman, D. B., Brown, E. R., Mason, A. Z., Harmsen, A. G., & Smith, J. L. (2015). The role of altruistic values in motivating underrepresented minority students for biomedicine. BioScience, 65(2), 183–188. https://doi.org/10.1093/biosci/biu199
Varma, R. (2018). U.S. science and engineering workforce: Underrepresentation of women and minorities. American Behavioral Scientist, 62(5), 692–697. https://doi.org/10.1177/0002764218768847
Whatley, J. (2009). Ground rules in team projects: Findings from a prototype system to support students. Journal of Information Technology Education: Research, 8(1), 161–176.
Yates, F. (1934). Contingency tables involving small numbers and the χ2 test. Supplement to the Journal of the Royal Statistical Society, 1(2), 217–235. https://doi.org/10.2307/2983604
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