COMPUGIRLS: Stepping stone to future computer-based technology pathways

Article

COMPUGIRLS:Stepping Stone toFuture Computer-Based TechnologyPathways

Jieun Lee1, Jenefer Husman1, Kimberly A. Scott2,and Natalie D. Eggum-Wilkens1

Abstract

The COMPUGIRLS: Culturally relevant technology program for adolescent girls was

developed to promote underrepresented girls’ future possible selves and career

pathways in computer-related technology fields. We hypothesized that the

COMPUGIRLS would promote academic possible selves and self-regulation to

achieve these possible selves. We compared the growth trajectories of academic

possible selves and self-regulation between the program participants and a compari-

son group using latent growth modeling for two semesters. There was no significant

group difference in the growth trajectories of academic possible selves. The findings

support that the COMPUGIRLS did not accelerate the program participants’ aca-

demic possible selves. However, the program participants had a significantly higher

growth rate in self-regulation than the comparison group. We argue that the higher

growth rate of self-regulation could help the program participants achieve academic

possible selves which is important for choosing technology career pathways.

Keywords

gender, adolescents, computer-based technology enrichment, latent growth

modeling, academic possible selves, self-regulation

Journal of Educational Computing

Research

2015, Vol. 52(2) 199–223

! The Author(s) 2015

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DOI: 10.1177/0735633115571304

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1T. Denny Sanford School of Social and Family Dynamics, Arizona State University, Tempe, AZ, USA2School of Social Transformation, Arizona State University, Tempe, AZ, USA

Corresponding Author:

Jieun Lee, PhD, T. Denny Sanford School of Social and Family Dynamics, College of Liberal Arts and

Sciences, Arizona State University, P.O. Box 873701, Tempe, AZ 85287-3701, USA.

Email: [email protected]

The economic vitality of the United Sates is largely dependent on its success inthe fields of Science, Technology, Engineering, and Mathematics (STEM; Beyer,Rynes, & Haller, 2004; Lacey & Wright, 2009; Riegle-Crumb & King, 2010).However, the popular press has recently highlighted a crisis that many people inthe fields of STEM education have known for some time, at a time in our historywhen we need many more people to focus their attention on STEM careers,fewer students are expressing an interest in STEM careers (Boundaoui, 2011;Olson, 2011). The STEM workforce crisis has caused many researchers andorganizations to focus resources on underrepresented populations in the promo-tion of STEM-focused education and careers (e.g., Higher Education ResearchInstitute, 2010; National Science Foundation [NSF], 2011). Although demo-graphics are changing in STEM programs’ enrollment, women of all ethnicitiesare still less participating in the areas of physical science, engineering, and com-puter science (American Association of University Women [AAUW], 2010;Riegle-Crumb & King, 2010). What is more alarming is how insignificant num-bers of underrepresented minority women choose to pursue careers in computer-related technology fields (Goode, 2007; NSF, 2011).

NSF (2011) categorized women and three racial or ethnic groups—Black,Hispanics, and American Indians as an underrepresented minority group inscience and engineering. In this article, a terminology of women of color indicateswomen in the racial or ethnic groups. Adolescent girls of color confront manydifficulties in developing and pursuing academic aspirations to make successfulcareers in computer-related technology fields because they often feel irrelevant tothe fields (Hanson, 2007). Therefore, we argue that the paucity of low partici-pation in computer-related technology areas is, at its heart, a self-concept issuefor adolescent girl of color. Self-concept and identity have given us self-evalua-tions to fundamental questions such as “Who am I?” “Where do I belong?” and“How do I fit (or fit in)?” (Oyserman, 2001, p. 499). Self-concept becomes morediscrete as children age, resulting in a view of the self in which different domainsbecome distinguishable (Harter, Bresnick, Bouchey, & Whitesell, 1997; Marsh,2007). Research showed that adolescents can differentiate multiple domains suchas scholastic competence and job competence (Harter, 1988) as well as self-concept within the present but also their future state (Oyserman, 2008).

A theory of possible selves was introduced by Markus and Nurius (1986)which provided a theoretical framework for investigating adolescents’ futureaspirations (“What I hope for becoming” and “What I expect to become”),future concerns (“What I want to avoid becoming”), and behavioral strategies(“How I can regulate my behaviors”). Oyserman and her colleagues have exten-sively examined the role of academic possible selves and use of behavioral stra-tegies to achieve these possible selves with underrepresented adolescentpopulation (e.g., Oyserman, Bybee, & Terry, 2006; Oyserman, Bybee, Terry,& Hart-Johnson, 2004; Oyserman, Terry, & Bybee, 2002). Oyserman and hercolleagues’ empirical findings were often cited to explain how possible selves and

200 Journal of Educational Computing Research 52(2)

self-regulation can be incorporated in adolescents’ social and personal lives(e.g., Hoyle & Sherrill, 2006; Seginer, 2009). Thus, we adopted the constructdefinitions and operationalization of academic possible selves and self-regulationcreated by Oyserman and her colleagues.

Academic Possible Selves

Possible selves are temporal and future-oriented views, which integrate one’sfuture goals and concerns about the future into self-concept as well as motivateactions (Oyserman & Fryberg, 2006; Oyserman & James, 2009). Two terminol-ogies have been coined to refer specific possible selves that are related to aca-demic domains such as school-focused possible selves (e.g., Oyserman, 2008;Oyserman & James, 2009) and academic possible selves (e.g., Oyserman et al.,2004; Oyserman et al., 2006). We selected the terminology of academic possibleselves because adolescent students can develop and achieve academic futuregoals outside of school. For example, a student may start to study aerospaceengineering on their own after watching the movie Gravity.

Oyserman and her colleagues have found that adolescents can articulate theirpositive possible selves (e.g., what they expect to become next year) as well asnegative possible selves in academic context (e.g., what they want to avoidbecoming next year; Oyserman et al., 2002). However, many schools fail toprovide guidance to adolescent students about the meaningful connectionsbetween their school activities and possible selves (Oyserman et al., 2002).Minority adolescents may find it more challenging to make the connectionsbetween their school activities and STEM careers than their White counterpartsdue to less access to adult models working in STEM fields (Oyserman, 2008).Even if minority adolescent students could perceive the meaningful connections,it would be impossible for them to achieve STEM possible selves without reg-ulating current behaviors (Hoyle & Sherrill, 2006). Therefore, our next focus isuse of behavioral strategies to pursue academic possible selves, which is oper-ationalized as self-regulation in this study.

Self-Regulation

In Oyserman’s framework, self-regulation is defined as an orientation towardconducting behavioral strategies to reach a set of planned academic future goals(Oyserman et al., 2004; Oyserman, 2008). Oyserman et al. (2004) emphasizedthe important role of self-regulation to pursue academic possible selves such that“since our focus is on self-regulatory academic possible selves—that is, detailedacademic possible selves that contain strategies to promote self-regulation,rather than the simple presence of academic possible selves . . .” (p. 134). Otherpossible selves researchers conceded that self-regulatory possible selves are dif-ferent from self-enhancing possible selves by indicating that the later ones only

Lee et al. 201

could buffer a positive mood and have no necessity for regulating behaviors(Hoyle & Sherrill, 2006). Oyserman and her colleagues operationalized theself-regulatory possible selves as self-regulation (e.g., Oyserman et al., 2004)and measured it using an open-ended questionnaire. In the open-ended ques-tionnaire, students are asked to write four or more positive and negative possibleselves as well as strategies to achieve these positive possible selves and to avoidhaving these negative possible selves next year (Oyserman et al., 2004).

Study results showed that adolescents can incorporate strategies to achievepositive possible selves and to avoid having negative possible selves (Oysermanet al., 2004). For example, if students have a positive possible self, becoming anexpert gamer, they might use strategies to understand advanced gaming systems(e.g., exploring new game systems on a regular basis). On the other hand, ifstudents have a negative possible self, online game addiction, they might usestrategies to avoid spending too much time playing online games (e.g., installingwebsite blocking software). Oyserman and her colleagues found that underre-presented adolescents’ self-regulation scores in the fall semester were significantpredictors of positive academic performance and higher academic achievementin the spring semester (Oyserman et al., 2004). Next, we will scrutinize under-represented adolescents’ academic possible selves and self-regulation.

Underrepresented Adolescents

Underrepresented adolescents have a more difficult time developing and pursu-ing future goals in an academic domain because stereotypes related to race,gender, and social class often prevent them from considering such possibilitiesfor themselves (Steele, 1997). However, research indicates there are opportu-nities to motivate these adolescents’ academic possible selves and self-regulation.Lee, Husman, Maez, and Scott (2011) found that contents of possible selvesprovided by underrepresented girls could be categorized into five domains: aca-demic, social, personality, health, and career. The researchers further explainedthat academic possible selves consisted of significant portions of these possibleselves (55% of positive possible selves and 26% of negative possible selves). Themost typical answers for positive academic possible selves were “passing ontothe next grade,” “getting good grades,” and “being a good student.” For nega-tive academic possible selves, “getting bad grades,” “failing classes,” and “poorattendance” were the most common answers.

In addition, an intervention study results supported malleability of under-represented adolescents’ academic possible selves and self-regulation (Oysermanet al., 2002, 2006). A 9-week intervention program that aimed to promoteunderrepresented students’ academic possible selves and use of behavioral stra-tegies to achieve these possible selves demonstrated positive results. Before start-ing the program, the intervention group was not significantly different from acontrol group on scores of plausible strategies to achieve academic possible


selves (i.e., how detail and concrete of the strategies are). However, at the end ofthe program, the intervention participants had higher scores of plausible strate-gies than the control group students after accounting for gender, cohort, andinitial scores (Oyserman et al., 2002). In addition, the intervention group hadmore positive scholastic indicators such as greater sense of school connectedness(Oyserman et al., 2002) as well as higher grade point averages and higher stan-dardized test scores than the control group (Oyserman et al., 2006).

On the basis of these findings, we argue that motivating academic possibleselves and self-regulation of underrepresented girls can be a stepping stone toenter the higher education in computer-based technology disciplines. In the nextsection, we will explain the major obstacles for women, in general, and women ofcolor, in particular, to enter computer-based technology disciplines. Then, wewill introduce an enrichment program of COMPUGIRLS which was designedto provide a stepping stone for underrepresented girls to enter computer-basedtechnology disciplines.

Gender and Computer-Based Technology

Many researchers in psychology, education, and engineering have been con-cerned about girls’ low participation in computer-based technology majors(AAUW, 2010; Adya & Kaiser, 2005; Jacobs, 2005). One reason for girls’ lowparticipation is less access to computers in early stages than boys (AAUW, 2000;Varma, 2009). Study results have indicated that early exposure to computers isone of the most frequent reasons for developing interests in computer-basedtechnologies (Turner, Bernt, & Pecora, 2002; Varma, 2009). However, girlshave significantly lower rates of computer access before college than boys(AAUW, 2000; Varma, 2009). Another reason for low participation is biasedcultural beliefs about task competence by gender. For example, girls tend tounderevaluate their math competence regardless of their actual performancecompared with boys (AAUW, 2010). The biased beliefs toward math compe-tence are critical because they are significantly related to girls’ career choice toquantitatively challenging fields (AAUW, 2010). In addition, many girls haveinaccurate perceptions of the workforce in computer-based technology fields.When adolescent girls imagine careers in computer science, they often envisionWhite or Asian men who are working in cubicles wearing white uniforms(Kekelis, Ancheta, & Heber, 2005). These inaccurate perceptions are criticalbecause career choices to join computer-related careers (e.g., IT) are oftenmade at an early adolescence (Adya & Kaiser, 2005).

Extending to Girls of Color

Racial minority women are significantly less represented in computer-basedtechnology majors in higher education and industries than their White

Lee et al. 203

counterparts (Goode, 2007; NSF, 2011). Much research has been conducted toinvestigate major factors that affect the low participation of racial minority girlssuch as social factors and structural factors (e.g., Margolis, Estrella, Goode,Holme, & Nao, 2008). In terms of the social factors, study results suggestedthat although young African American girls have high levels of interest in sci-ence, sexist and racist cultures in the science industry hinder them from enteringand persisting in postsecondary science education (Hanson, 2004). Regardingthe structural factors, statistics indicated that a large number of girls of colorattended underresourced schools that do not offer advanced technology classesand high-end devices (Margolis et al., 2008). Overall, girls of color confrontdouble barriers, both barriers for girls and for ethnicity (Hanson, 2007).Researchers underlined the need for more technological experiences andresources to overcome those barriers (AAUW, 2000; Goode, 2007). Therefore,a computer-based technology enrichment program that is specially designed forgirls of color is crucial to promote a future technology workforce in thispopulation.

An Enrichment Program to Computer-BasedTechnology Pathways

The Enrichment Program

Overview. The COMPUGIRLS is a computer-based enrichment program forgirls from underresourced areas. The pedagogical approach that was critical inthe program is the development of academic possible selves and self-regulationto pursue these possible selves on the basis of a culturally responsive computingexperience. During the 2-year, six-course program, girls use various software(e.g. iLife, The SimsTM, The Scratch, Teen Second Life�) and hardware (e.g.laptops, digital cameras, video equipment) to research, analyze, and presentsolutions to self-identified social and community issues (see Figure 1 forcourse sequence).

Curriculum. The principal investigator worked with computer scientists, gamedesigners with expertise in gender, and scholars in Indigenous KnowledgeSystems to develop a culturally responsive computing experience (see Scott,Sheridan, & Clark, 2014 for details) for adolescent girls (ages 13–18). Tothis end, COMPUGIRLS offered summer programs (4 days per week for 5weeks) and spring and fall programs (1 day after school and one Saturdayper week for more than 10 weeks) throughout the 2-year period. Over thecourse of six courses averaging more than 200 hours, girls worked in smallgroups (n¼ 4–6), large groups (n¼ 40), and individually while learning howto research a self-identified social justice topic. Topic selection ranged fromsexism in the workplace; lesbian, gay, bisexual, and transgender issues for


high school students, to negative effects of gentrification on urbancommunities.

Each of the six courses integrated culturally responsive pedagogy (CRP;Gay, 2010; Ladson-Billings, 1994; Paris, 2012; Santamaria, 2009) into theirlessons. Critical to CRP is asset building, reflection, and connectedness.Lessons operationalized these three principles in hopes of encouraging thegirls to develop academic possible selves and greater self-regulation.Specifically, program activities revolved around guiding girls to recognize theknowledge they possessed about their raced-, gendered-, classed-self as anasset. Through facilitated dialogues and preplanned interactive exercises ledby mentor teachers or peer mentors (details below), participants acquiredstrategies to reflect upon their self-images along multiple intersecting lines.We anticipated that engaging girls in this process—that is, understandingwho they are and who they could be—would lead to them articulating aca-demic possible selves. For example, while providing girls access to a multitudeof resources (e.g. databases) and ways to critically analyze the data and ima-gery, we regularly reminded them that they were learning the techniques post-secondary and graduate students assumed in their educational pursuits. Thiswas a purposeful tactic as a way to encourage girls imagining their possibleselves as college students.

Moreover, we poised technology as a construct to be manipulated towardcommunal social justice ends (connectedness). It was our contention that thetechnology would serve as an interesting vehicle to describe a complex socialissue. In this context, acquiring technological skills was meant to benefit a

Course I Course II Course III

Introduction

Introduction to social justice,

media, and technology

The Sims™

Participants design a virtual

world in which they

determine the trajectory of

their characters’ lives.

The Scratch

Participants learn and

manipulate graphical

programming language to

create animation, games,

music, and art.

Course IV Course V Course VI

Intro to Teen Second Life®

Participants create characters

and begin to operate in a

virtual world.

Teen Second Life®

Participants begin social

justice projects to affect

change in virtual world.

Capstone of Teen Second

Life®

Participants execute proposed

projects in virtual world.

Figure 1. The COMPUGIRLS course sequences.

Lee et al. 205

particular community. For example, the girls acquired multimedia strategiessuch as creating digital stories, games, and simulations as well as building in avirtual world to document and analyze their self-selected social justice topicswithin their communities. We assumed that if we could excite girls’ interest inscholarship by engaging in these activist practices, then they would be able toimagine and ultimately engage in self-regulatory strategies to further their aca-demic possible selves beyond the program’s setting. Taken together, we hopedthat these strategies of describing one’s developing self while advancing commu-nity would encourage participants’ self-regulation. Finally, the adults’ responsi-bilities for providing technology instruction and guiding students’self-regulatory behaviors were pivotal to this process.

Mentor Teachers

Graduate students from a variety of disciplines and in-service teachers becamecompensated COMPUGIRLS mentor teachers upon successful completion ofover 40-hours of training. Led by the founder or principal investigator and otherinvolved scholars, we required mentor teachers to complete the same assign-ments in which the girls would engage. They, too, were encouraged to challengetheir understanding of race, gender, and class and how these sociocultural fea-tures influenced their professional and personal lives. Mentor teachers learnedhow to support students’ academic possible selves and self-regulated learningdevelopment. Their learning experience was integral to identifying pedagogicalstrategies that encourage girls to develop their own self-regulatory behaviors andconsider how their COMPUGIRLS work poise them to maintain academicpossible selves. As a consequence, we encouraged mentor teachers to imagineand implement culturally responsive lessons beyond those offered during theirtraining.

Recruitment and Retention

Program applicants for this experience were not necessarily the highest academicachievers. Indeed, we hosted school dropouts, delinquents, girls from grouphomes, and pregnant teens to examine the program effect on the students’ devel-opment of academic possible selves and self-regulation over time. The diversepool of participants emerged from our intentional recruitment efforts.Regularly, the principle investigator drew on her already established connectionsthrough teaching in an educational leadership program. Local elementary andhigh school districts with disproportionate numbers of Latino and AfricanAmerican students who also qualified for free or reduced lunch granted theprincipal investigator or a staff member permission to conduct presentationsto potential participants. The presentations involved interactive activities,demonstrating to prospective participants the digital media products past


program participants created and engaging them in a brief discussion similar towhat they would experience in the program.

Interested girls complete an application form including (a) personal informa-tion, (b) current technology experience, and (c) personal statement. Applicantsanswered six current technology experience questions (e.g., “How often do youuse a computer at home?”) using a 4-point Likert-scale (1¼ never to 4¼ all thetime). For the personal statement, applicants wrote an essay about why theywould like to join the program and what they hope to gain from the experience.Three subcommittee members of the program’s advisory board rated the essaysbased on a 10-point system with content weighed more heavily (possiblepoints¼ 7) than grammar (possible points¼ 3). We converted the six self-reported scores of the current technology experience and the three ratingscores of the personal statement into standard scores (i.e., z-scores). Then, thethree z-scores of the personal statement were weighted by 3 for a greater empha-sis in selection determination. A total z-score of the technology experience andthe personal statement was used for a selection criterion. Once admitted, weprovided girls bus passes to travel to a local university where we housed theprogram.

As a means to retain the girls over the six courses, participants received giftcards to a local electronics store at the conclusion of each course. The amountsdepended on their attendance and successful completion of the required work.Generally, girls could earn anywhere from $75 to over $150 per course. Weexplained that continual attendance could result in earning enough gift cardsto purchase a digital camera or tablet. For those who completed all coursework,we orchestrated paid internship opportunities immediately after finishing theirfinal course.

The Present Study

We expected that the intervention would positively impact underrepresentedadolescent girls’ academic possible selves and use of self-regulation techniquesin pursuit of academic possible selves. Therefore, we hypothesized that the pro-gram participants’ latent growth trajectories of academic possible selves and self-regulation would be significantly different from the comparison group. In orderto examine the hypothesis, we recruited a comparison group from a similarsocial context (e.g., girls of color in underresourced school districts). Notethat we tested the program effect for the first two semesters due to availabilityof the comparison group’s data. Given the duration of the program evaluation,we called this study as a short-term longitudinal study. The current studyaddressed the following research questions: (a) Do the program participantshave a higher academic possible selves mean score than the comparison groupat the end of the second semester? (b) Do the program participants have a higheracademic possible selves growth rate than the comparison group per semester?

Lee et al. 207

(c) Do the program participants have a higher self-regulation mean score thanthe comparison group at the end of the second semester? (d) Do the programparticipants have a higher self-regulation growth rate than the comparisongroup per semester?

Methods

Procedures

COMPUGIRLS. We measured the program participants’ growth of academicpossible selves and self-regulation using pre- and postsurveys for two semes-ters. Three waves of data were used in the study: a baseline survey (Wave 1:orientation), the first semester postsurvey (Wave 2: at the end of thesummer of 2009 session), and the second semester postsurvey (Wave 3: atthe end of the fall of 2009 session). The program participants took anonline survey that included a possible selves questionnaire and other meas-ures (e.g., academic self-concept and computer literacy skills) for approxi-mately 30 minutes in each wave. No survey incentives were provided to theprogram participants.

Comparison group. We started to recruit female junior high to high school stu-dents in underresourced school districts during the spring of 2009. Due to lessaccess to students in summer, we started collecting the comparison group’s datain the fall of 2009. In this study, we analyzed the baseline survey (Wave 1: at thebeginning of the fall of 2009 semester), the first semester postsurvey (Wave 2: atthe end of the fall of 2009 semester), and the second semester postsurvey(Wave 3: at the end of the spring of 2010 semester). The comparison grouptook a similar online survey for about 30 minutes and received a five-dollargift card as a survey incentive in each wave.

Participants

COMPUGIRLS. Among initial 40 students, 37 students participated in at least onesurvey during two semesters. There was no student who missed the first semesterbut joined the second semester. Thirty-four students participated in the baselinesurvey (Wave 1), 28 students participated in the first semester follow-up survey(Wave 2), and 29 students participated in the second semester follow-up survey(Wave 3). In terms of an attrition rate, six students attended the first semesterand left the program before the second semester started. Their reasons for leav-ing were family schedule (n¼ 1), time conflict with work (n¼ 1), misunderstand-ing of curriculum (n¼ 1), and no response or unreachable (n¼ 3). Thedemographic information of the program participants at Wave 1 was presentedin Figure 2.


Comparison group. Due to the nature of the longitudinal study, some students didnot participate in all of the three surveys. Sixty-three students participatedin the baseline survey (Wave 1), 52 students participated in the firstsemester follow-up survey (Wave 2), and 36 students participated in thesecond semester follow-up survey (Wave 3). The demographic information ofthe comparison group at Wave 1 was presented in Figure 2.

Measures

Possible selves questionnaire. The possible selves questionnaire was adapted fromOyserman’s possible selves questionnaire to measure students’ possible selves andstrategies to attain these possible selves using an open-ended response format(Oyserman, 2004; Oyserman et al., 2004). Oyserman and colleagues used thequestionnaire to investigate potential effects of a 9-week intervention programon the development of underrepresented adolescents’ academic possible selvesand self-regulation (Oyserman et al., 2006). In the questionnaire, students areasked to write up to four positive possible selves for the next year (“Next year, Iexpect to be . . .”). If they are doing something to achieve each possible self, theywere asked to write down the strategies next to the possible self. Then, the stu-dents were asked to write up to four negative possible selves for the next year(“Next year, I want to avoid . . .”). If they are doing something to avoid eachpossible self, they were asked to write down the strategies next to the possible self.

Figure 2. The demographic information by group. COMPUGIRLS (n¼ 34) and the com-

parison group (n¼ 63).

Lee et al. 209

Two trained research assistants reviewed all students’ open-ended responsesrelated to both their positive and negative possible selves and strategies that wereattached to these possible selves. Then, they coded these possible selves using fivedomains (see Table 1) and calculated self-regulation scores using a self-regula-tion coding chart (see Table 2). The overview of the coding procedures of thequestionnaires and the interrater reliabilities are described in Figure 3.

Analysis

Initial considerations. Using IBM SPSS Statistics 21, we conducted independentsamples t tests to compare initial academic possible selves and self-regulationmean scores of the program participants with the comparison group. Before theprogram started, the program participants had a lower mean score of academicpossible selves (M¼ 2.74; SD¼ 1.52) than the comparison group (M¼ 3.38;SD¼ 1.76), but the mean difference was not statistically significant,t (76.32)¼�1.89; p¼ .063. The program participants had a significantly lowermean score of self-regulation (M¼ 2.85; SD¼ 1.65) than the comparison group(M¼ 3.72; SD¼ 1.74), t (71.44)¼�2.41; p¼ .019. The results supported that theselection criteria for the program which were based on the current technology

Table 1. Five Domains of Positive and Negative Possible Selves.

Domain Example

1. Academic Going to next grade

Joining extra classes

Failing a class

2. Social Family

Social club

Peers

3. Personality Being more nice

Acting more responsible

Being more mature

4. Health Beauty

Drugs

Pregnancy

5. Career Part-time jobs

Working for family

Future profession

Miscellaneous Cooking

Saving money

Religion


experience and the personal statement did not cause the program participants tohave inflated mean scores of academic possible selves and self-regulation at thebaseline.

We also examined univariate nonnormality of item scores according toCurran, West, and Finch’s (1996) recommendation (moderately nonnormal:univariate skewness> W2.00W; kurtosis> W7.00W). After checking the descriptive

Table 2. Self-Regulation Coding Chart.

Self-regulation

Numbers of positive or

negative APS

Numbers of strategies

attached to APS

0 0

1 0

1 1 1

2 0

2 1 2 or more

2 1–2

3 0–1

3 2 3 or more

3 2–3

4 0–2

4 3 4 or more

4 3–4

5 0–2

5 4 5 or more

5 3–5

6 0–3

6 5 6 or more

6 4–6

7 0–3

7 6 7 or more

7 4–7

8 0–4

8 7 8 or more

8 5–8

9 8 or more 9 or more

77 Misunderstanding for the instruction

99 Leaving blanks for all questions

Note. 77 and 99 were coded as missing data. APS¼ academic possible selves.

Lee et al. 211

statistics results (see Table 3), we concluded that the item scores were normallydistributed.

We tested the fit of the proposed models and the data using full informationmaximum likelihood (FIML) estimation. Many methodologists recommendedFIML estimation as a modern missing data handling approach (e.g., Enders,2011; Schafer & Graham, 2002). The correlation matrix of academic possibleselves and self-regulation is presented in Table 4.

Latent growth modeling. We estimated latent growth models using Mplus7.11(Muthen & Muthen, 1998–2012). The latent growth model examines thelatent growth trajectories of an outcome variable using the structural equationmodeling framework (Bollen & Curran, 2006). One advantage of latent growthmodeling over classical repeated-measures analyses (e.g., analysis of varianceand analysis of covariance) is that it allows the modeling of individual differ-ences in growth trajectories over time, whereas classical repeated-measures ana-lyses do not (Bollen & Curran, 2006; Byrne, 2011). In other words, the latentgrowth model estimates an average growth trajectory as well as variation inindividual growth trajectories.

Possible Selves

• Coding positive and negative possible selves using five domains• Select positive and negative possible selves in an academic domain

→ labeling as “Academic possible selves”


• Counting academic possible selves → becoming scores of academic possible selves

• Interrater agreements were 87% for the program participants’ data and 92% for the comparison group’s data

Self-regulation

• Counting academic possible selves and strategies attached to these possible selves

• Making self-regulation scores using a coding chart• Interrater agreements were 74% for the program participants’ data and

80% for the comparison group’s data

Figure 3. The overview of the coding procedures and the interrater agreements.


A unit change in time represented one semester in our latent growth models.Exact time (e.g., a month) could not be applied to a unit change because the firstsemester of the COMPUGIRLS was a summer intensive program which lastedfor 4 weeks. The second semester was a fall program which offered similaramounts of program hours but for a longer duration (more than 10 weeks).

Table 4. Correlations of Academic Possible Selves and Self-Regulation.

APS.1 APS.2 APS.3 SR.1 SR.2 SR.3

COMPUGIRLS

APS.1 –

APS.2 .71 –

APS.3 .66 .54 –

SR.1 .95 .67 .61 –

SR.2 .61 .93 .45 .62 –

SR.3 .66 .55 .97 .64 .47 –

Comparison group

APS.1 –

APS.2 .18 –

APS.3 .36 .44 –

SR.1 .94 .23 .38 –

SR.2 .13 .94 .32 .20 –

SR.3 .32 .30 .92 .36 .20 –

Note. 1, 2, and 3 represent Wave 1, Wave 2, and Wave 3. COMPUGIRLS (n¼ 37) and comparison group

(n¼ 73). Estimator¼ full information maximum likelihood; APS¼ academic possible selves; SR¼ self-

regulation.

Table 3. Descriptive Statistics for COMPUGIRLS and Comparison Group.

N Range M SD Skew Kurtosis

APS.1 34 63 0–6 0–8 2.74 3.38 1.52 1.75 0.42 0.32 �0.14 �0.01

APS.2 28 52 0–7 0–7 3.79 3.37 1.75 1.70 �0.14 �0.11 �0.24 �0.36

APS.3 29 36 0–7 1–6 3.55 3.75 1.62 1.54 �0.45 �0.20 0.43 �0.99

SR.1 34 61 0–6 0–8 2.85 3.72 1.65 1.74 0.38 0.17 �0.48 0.47

SR.2 28 48 0–7 0–8 3.79 3.63 1.79 1.83 �0.28 �0.05 0.16 �0.09

SR.3 28 34 0–7 1–6 3.71 3.74 1.65 1.48 �0.46 �0.11 0.39 �1.06

Note. 1, 2, and 3 represent Wave 1, Wave 2, and Wave 3. Italicized numbers are COMPUGIRLS.

APS¼Academic Possible Selves; SR¼ Self-regulation.

Lee et al. 213

We first tested two unconditional growth models including the program par-ticipants and the comparison group. The first unconditional growth modelexamined the growth trajectories of academic possible selves. The second uncon-ditional growth model investigated the growth trajectories of self-regulation.These unconditional growth models used three waves of data collected fortwo semesters (Wave 1¼ baseline, Wave 2¼ end of the first semester, andWave 3¼ end of the second semester). With a centering point at the end ofthe second semester (Wave 3), we fixed factor loadings at 1 for the latent inter-cept and �2, �1, and 0 for the latent linear slope for Waves 1, 2, and 3, respect-ively. We estimated the means and variances for the latent intercept and thelatent linear slope. The covariance between the latent intercept and slope wasestimated. Residual variances were allowed to vary across time points.

The growth models provided information for fixed and random effects. Thefixed effects included a predicted mean score at Wave 3 and a predicted averagegrowth rate per semester. The random effects included between-person variabil-ity in the predicted scores at Wave 3 (i.e., intercept variance), between-personvariability in the growth rates per semester (i.e., slope variance), and the covari-ance between the latent intercept and slope. After we found significant between-person variability in the latent intercepts of the two unconditional models, wetested two conditional growth models with a time-invariant predictor, groupmembership (0¼ comparison group; 1¼ program participants). The first condi-tional growth model examined a group difference in the growth trajectories ofacademic possible selves. The second conditional growth model tested a groupdifference in the growth trajectories of self-regulation.

Results


Unconditional growth model. The initial linear growth model failed to convergebecause of a negative slope variance estimate. We tried two alternative approachesto estimate the latent slope variance. First, we constrained residual variances to beequal over time to determine whether a more parsimonious model would facilitateestimation of the latent slope variance. The model confronted the same issue of thenegative slope variance estimate. Then, we increased the number of iterations to3,000, but the model did not converge due to the negative slope variance estimate. Itis possible that the negative slope variance estimate was a function of a small samplesize (n¼ 110). We decided not to estimate the latent slope variance which wouldexamine between-person variability in the growth rates per semester. Doing so alsoremoves estimation of the covariance between the latent intercept and slope.Therefore, the final model estimated a predicted mean score at Wave 3, between-person variability of the predicted scores at Wave 3, and a predicted average growthrate per semester. The final model converged without error, �2(3)¼ 0.60, p¼ .896.


At Wave 3, students including the program participants and the comparisongroup had a predicted mean score of 3.64 (p< .001) and a significant averagegrowth rate of .24 per semester (p¼ .012). There was significant variation of thestudents’ predicted scores at Wave 3 (the latent intercept variance¼ 1.23, p< .001).

Conditional growth model. Previous analysis results indicated the presence ofsignificant individual differences in participants’ predicted scores at Wave 3.So, we tested the impact of group membership (0¼ comparison group; 1¼ pro-gram participants) on the latent intercept and slope (see Figure 4). After omittingestimation of the latent slope variance and the covariance between the latentintercept and slope, the model converged without error, �2(4)¼ 3.45, p¼ .485.

At Wave 3, the comparison group had a predicted mean score of 3.62(p< .001) and an average growth rate of .13 (p¼ .287). The mean difference atWave 3 for the program participants was �.006 (p¼ .987) relative to the com-parison group. And, the average growth rate difference for the program partici-pants was .29 (p¼ .142) relative to the comparison group. In other words, theprogram participants had a lower predicted mean score at Wave 3 and a higheraverage growth rate per semester than the comparison group. However, both ofthe group differences were not significant. In conclusion, there were no signifi-cant differences between the program participants and the comparison group fortheir predicted mean scores at Wave 3 and their average growth rates persemester.

Figure 4. Adjusted model-estimated means for academic possible selves by group. X-axis

represents the passage of time (�2¼ baseline, �1¼ end of the first semester, 0¼ end of

the second semester).

Lee et al. 215

Self-Regulation

Unconditional growth model. For self-regulation, we followed identical modelingapproaches as used with academic possible selves. Again, we obtained a negativeslope variance and eliminated estimation of the slope variance as well as thecovariance between the latent intercept and slope. It is possible that the negativeslope variance estimate was a function of a small sample size (n¼ 109). The finalmodel estimated a predicted mean score at Wave 3, between-person variabilityof the predicted scores at Wave 3, and a predicted average growth rate persemester. The model converged without error, �2(3)¼ 1.56, p¼ .668.

At Wave 3, the students including the program participants and the compari-son group had a predicted mean score of 3.76 (p< .001) and an average growthrate of .18 per semester (p¼ .076). The growth rate was not significant. Therewas significant variation of the students’ predicted scores at Wave 3 (the latentintercept variance¼ 1.20, p< .001).

Conditional growth model. Previous analysis results indicated the presence of sig-nificant individual differences in students’ predicted scores at Wave 3. So, wetested the impact of group membership (0¼ comparison group; 1¼ program par-ticipants) on the latent intercept and slope (see Figure 5). After eliminating thelatent slope variance as well as the covariance between the latent intercept andslope, the model converged without error, �2(4)¼ 2.46, p¼ .652.

At Wave 3, the comparison group had a predicted mean score of 3.65(p< .001) and an average growth rate of �.02 (p¼ .909). The mean difference

Figure 5. Adjusted model-estimated means for self-regulation by group. X-axis represents

the passage of time (�2¼ baseline, �1¼ end of the first semester, 0¼ end of the second

semester).


at Wave 3 for the program participants was .16 (p¼ .651) relative to the com-parison group. And, the average growth rate difference for the participants was.50 (p¼ .014) relative to the comparison group. In other words, the programparticipants had a higher predicted mean score at Wave 3 and a higher averagegrowth rate per semester than the comparison group but only the growth ratedifference was significant. Thus, there was no significant difference between theprogram participants and comparison group for their predicted mean scores atWave 3. However, the program participants had a significantly higher averagegrowth rate than the comparison group.

We examined whether the program participants had a significant averagegrowth rate per semester. We changed the group membership indicator in thedata (0¼ program participants; 1¼ comparison group) and tested the identicalmodel. This model converged without error, �2(4)¼ 2.46, p¼ .652. The programparticipants had a significant predicted mean score of 3.82 (p< .001) at Wave 3and a significant average growth rate of .48 (p¼ .002) per semester.

Discussion

The COMPUGIRLS was developed to promote underrepresented adolescentgirls’ viable career pathways in technology fields. By providing girls computer-based informal education lessons executed on the basis of CRP, theprogram encouraged the girls to develop academic possible selves and self-regulation to achieve these possible selves. In addition, the mentor teacher train-ing prior to the program was integral to implement the CRP practices as well asto motivate students’ academic possible selves and self-regulatory behaviors.In sum, working with adolescent females from diverse backgrounds,the COMPUGIRLS aims to change the technological playing field with girlswho can think critically and innovate with technology using self-directedresearch. The technology they learn to use becomes a means to demonstratethis process.

In particular, we compared growth trajectories of academic possible selvesand self-regulation between the program participants and a comparison group ina similar social context (e.g., girls of color). The unconditional model of aca-demic possible selves indicated a significant average growth rate of the studentsincluding the program participants and the comparison group. However, theconditional model of academic possible selves revealed that the comparisongroup did not have a significant average growth rate. Moreover, the growthrate difference between the program participants and the comparison groupwas not significant. We failed to detect the group difference on the averagegrowth rates of academic possible selves. There are two plausible answers forthe results. First, adolescents in underresourced areas may develop academicpossible selves as they move to higher grades and prepare for critical examin-ations (e.g., SAT). However, the comparison group’s average growth rate of

Lee et al. 217

academic possible selves was trivial. Second, participation in two semesters ofthe program might be insufficient for accelerating the program participants’growth of academic possible selves. In contrast, the program participants hada significantly higher average growth rate of self-regulation than the comparisongroup. Further investigation revealed that the program participants had a sig-nificant average growth rate but the comparison group did not. We argue thatthe COMPUGIRLS did support the development of participants’ self-regulationto achieve their academic possible selves.

Despite the significant findings, this study has several limitations. First, wecould not utilize a randomized group design. The program participants appliedfor the COMPUGIRLS and were selected on the basis of their current technol-ogy experience and personal statement scores. On the contrary, the comparisongroup members were recruited from a larger pool of students and they were notprescreened. Although the program participants were “chosen,” their academicpossible selves were not significantly different from the convenience sample usedfor comparison. Interestingly, from the start the girls chosen for participation inCOMPUGIRLS were lower in self-regulation than the comparison group. It ispossible that the lower starting point would give the program participants moreopportunity for growth. Nevertheless, the comparison group still had potentialroom for growth.

Another issue is associated with the sample size. The current sample is 110 foracademic possible selves and 109 for self-regulation after accounting for missingdata using FIML estimation. Researchers often recommend 100 as a minimumsample size for testing growth modeling (Curran, Obeidat, & Losardo, 2010).Thus, the sample size may have been adequate for testing the growth models, butperhaps if it was larger, the variance in the latent slope would have been greaterthan zero. Moreover, we collected unequal sizes of the program participants andthe comparison group members. Thus, the oversampled comparison groupmight have influenced the results of the unconditional models which used acombination of the two groups. However, potential group differences in aca-demic possible selves and self-regulation were examined in the conditionalmodels.

Furthermore, there were no direct measures of the program effect on theparticipants’ academic achievements at school and career development in com-puter-based technology fields. We did not examine whether the program par-ticipants actually increased their GPAs and other standardized test scores atschool while participating in the program. However, previous research uncov-ered that development of academic possible selves and self-regulation within acontext of an afterschool intervention program positively influenced the pro-gram participants’ academic engagement and achievements at school(Oyserman et al., 2002; Oyserman et al., 2006). In terms of the technologicalfuture, we could not track the program participants’ college enrollment rates incomputer-based technology majors because of their diverse starting ages at the


baseline. However, we argue that the program participants’ sense of techno-logical confidence and skill use are important indicators of their future collegeenrollments and career choices in computer-based technology fields (Betz &Hackett, 2006; Hackett & Betz, 1981). Therefore, a subsequent study has beeninitialized to examine how the enrichment program influenced the participants’development of technological self-concept and confidence of knowledge andskills use.

Despite the limitations, this study is unique in the theoretical approach, aca-demic possible selves and self-regulation theories, and noteworthy for its focuson underrepresented adolescent girls. The significant growth of self-regulation toattain academic possible selves in the context of a computer-based technologyenrichment program provides strong evidence for the possibility of broad influ-ence of these programs on students’ academic performance and future. Girlsfrom underresourced schools have the same educational aspirations (academicpossible selves) as girls who participated in the COMPUGIRLS program, butCOMPUGIRLS participants had a better understanding of how to turn thoseacademic possible selves into actions. Technological educational enrichmentprograms can increase girls’ opportunities for stepping into computer-basedtechnology majors. In the future, it is worth investigating whether the develop-ment of self-regulation not only influences the program participants’ academicachievements but also career choice to join computer-based technologyindustries.

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of the National ScienceFoundation.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, author-

ship, and/or publication of this article. The study was supported by the National ScienceFoundation under DRL-0833773.

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Author Biographies

Jieun Lee is a Postdoctoral Research Fellow in the T. Denny Sanford School ofSocial and Family Dynamics at Arizona State University (ASU). In 2013 Dr.Lee received her Ph.D. in Educational Psychology with a LearningConcentration from ASU. During her graduate study, she served as the datamanager and evaluation assistant at the National Science Foundation-fundedprogram, COMPUGIRLS for four years (2008–2012). Her research interestsinclude academic possible selves, self-regulated learning, STEM interventions,self-concept and identity, motivation, and academic achievements.

Jenefer Husman is an Associate Professor in the T. Denny Sanford School ofSocial and Family Dynamics at Arizona State University. Dr. Husman serves asthe Director of Education for the Quantum Energy and Sustainable SolarTechnology Center – an NSF funded Engineering Research Center. In 2006she was awarded the U.S. National Science Foundation CAREER grant


award and received the Presidential Early Career Award for Scientists andEngineers from the President of the United States. Her research interests includethe motivational implications of students’ thinking about the future for theirmotivation for studying STEM education research.

Kimberly A. Scott is an Associate Professor in the Women and Gender StudiesDepartment at Arizona State University and Director of the Center for GenderEquity in Science and Technology. Her research interests include intersection-ality theory, digital equity for underserved populations, and childhood studies.Scott was named in 2014 as a White House Champion of Change for STEMAccess and by Diverse Issues in Higher Education identified as one of the top 30women in higher education. Currently, she is working on two new book-lengthprojects: COMPUGIRLS: Becoming Ourselves in This Digital Age and WomenEducation Scholars and Their Children’s Schooling (Routledge).

Natalie D. Eggum-Wilkens is an Assistant Professor in the T. Denny SanfordSchool of Social and Family Dynamics at Arizona State University. Herresearch interests include children’s social and emotional development.

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COMPUGIRLS: Stepping stone to future computer-based technology pathways

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