– The students constructed personal narratives mediated by symbolic cultural systems to make meaning of their experiences, which more often disputed than conﬁrmed the model minority stereotype.
– Eleven students brought up the notion that some Asian students are encouraged to pursue STEM-based ﬁelds because the perception is that they will not be successful in other ﬁelds, such as English, religion, or history. They discussed being pigeonholed into majoring in STEM in spite of their many diverse life and career interests.
– The experiences of Black and Asian STEM college students overlap signiﬁcantly, in that both are bound by society’s misrecognition of their race and ability.
– These students were not immune from believing in the stereotypes and biases about their own race, even as they recognized that these stereotypes might be harming them.
– Five students in this study discussed using the MMM and their high achievement in STEM to capitalize on or take advantage of the stereotype.
– South Asian (Indian or Pakistani) students in particular have similar experiences that differ from those of East Asian students. Many women in this sample talked about the salience of skin tone discrimination in their lives and its effects on their academic performance.
– The students constructed personal narratives mediated by symbolic cultural systems to make meaning of their experiences, which more often disputed than conﬁrmed the model minority stereotype.
Inclusive STEM high schools (ISHSs) (where STEM is science, technology, engineering, and mathematics) admit students on the basis of interest rather than competitive examination. This study examines the central assumption behind these schools – that they provide students from subgroups underrepresented in STEM with experiences that equip them academically and attitudinally to enter and stay in the STEM pipeline. Research questions: 1) To what extent do STEM interests, activities, achievement, and expectations among 12th graders attending inclusive STEM high schools differ from those of similar students attending regular comprehensive high schools? 2) To what extent do STEM interests, activities, achievement, and expectations among 12th graders from demographic groups underrepresented in STEM fields differ between those attending inclusive STEM high schools and those attending regular comprehensive high schools?
Public Understanding of Science and K-12 STEM Education Outcomes: Effects of Idaho Parents' Orientation Toward Science on Students' Attitudes Toward Science
The authors focus on the potential effects of parents’ attitudes toward science on their children’s STEM learning outcomes.
The purpose of this study is to identify key college experiences that are correlated with long-term success for female technologists. Research questions include whether long-term career success is more likely for female technology graduates who, during their undergraduate studies, (1) personally interacted with professional and academic role models, (2) were able to apply their classroom learning to real world problems, and (3) actively participated in campus life.
Laying the Tracks for Successful Science, Technology, Engineering and Mathematics Education: What Can We Learn from Comparisons of Immigrant-Native Achievement in the USA?
This paper examines the immigrant-native achievement gap in science, technology,
engineering, and mathematics (STEM) fields in college in the USA.
Does students’ decision of STEM enrollment in college differ systematically by family SES?
This paper asks whether exposure to female role models may be an effective way to induce more
women to major in a male-dominated field.
Academic Performance of African American High School Students Related to Socioeconomic Status and School Size
– There was a negative correlation between school level SES and reading at -.50, -.44 for mathematics, and -.35 for science performance.
– There was a positive correlation between school size and reading at .10, .01 for mathematics, and .07 for science performance.
– School level SES and school size had significant impact on school
performance in reading.
– School level SES had significant impact on school performance in mathematics.
– School level SES and school size had significant impact on school performance in science.
Can learning communities boost success of women and minorities in STEM? Evidence from the Massachusetts Institute of Technology
– Author finds no statistically signiﬁcant eﬀects on academic outcomes for ESG enrollees generally, but women who participate in the program have higher GPAs and complete more credits of coursework.
– Minority students are more likely to major in math, computer science, or electrical engineering after participating in the ESG program.
– Though quite noisy, the results are suggestive that women and minorities in STEM may beneﬁt from learning communities.
– Author ﬁnds evidence that female instructors are particularly beneﬁcial for female students at MIT. However, the magnitude of the estimates suggests that the gender-mix of ESG instructors cannot account for most of the academic eﬀects the author observes for female students.
– High school math and science teacher gender aﬀects student interest and self-eﬃcacy in STEM. However, such eﬀects become insigniﬁcant once teacher behaviors and attitudes are taken into account, thus pointing towards an omitted variables bias.
– Teacher beliefs about male and female ability in math and science – as well as how teachers treat boys and girls in the classroom – matter more than teacher’s own gender.
-Creating a positive learning environment and making math and science interesting are pivotal in engaging students in these subjects.
– Student interest and self-efﬁcacy are substantially aﬀected by teacher ability to make their subject interesting and to create a positive learning environment.
– Rather than hiring more female teachers or segregating students by gender, training teachers ( increasing empathy and reducing gender biases) could be more eﬀective in increasing student self-efﬁcacy and interest in STEM.
– What matters primarily in this context are not the role models played by teachers (or the stereotype threats), but the time and skills that instructors put in preparing their lectures and supporting their students.
(1) What factors influence students’ interest in STEM fields of study or work? (2) Are there any significant differences in students’ responses with respect to their gender? (3) Are there any significant differences in students’ responses with respect to their grade level?
– Teachers’ expectancy for children’s success in science did not signiﬁcantly predict students’ ﬁfth grade science achievement.
– Parents’ expectancy did predict students’ ﬁfth grade science achievement.
– Children’s science self-efﬁcacy signiﬁcantly inﬂuenced science achievement scores. This was a weaker inﬂuence than the direct effect of parents’ expectancy of children’s success in science.
– None of the dependent variables showed significant difference between genders.
– The inﬂuence of parent expectancy on child self-efﬁcacy for science and science achievement is equally strong for both boys and girls.
Cracking the Code: Girls' and Women's Education in Science, Technology, Engineering and Mathematics (STEM)
The report is intended to stimulate debate and inform STEM policies and programmes at global, regional and national levels. Specifically, it aims to: i) document the status of girls’ and women’s participation, learning achievement, and progression in STEM education; ii) ‘crack the code’, i.e., decipher the factors that contribute to girls’ and women’s participation, achievement and progression in STEM education; and, iii) identify interventions that promote girls’ and women’s interest in and engagement with STEM studies.
Macrosystem Analysis of Programs and Strategies to Increase Underrepresented Populations in the Geosciences
– Key approaches identified in the literature to advance participation of underrepresented populations in the geosciences include: mentoring, peer support networks and community building, bridge programs, pedagogies, undergraduate research experiences, institutional climate and culture, specific geoscience education programs.
– In mentorship of underrepresented students, interactions of minority students with their research mentor can result in increased likelihood of graduate school pursuit and in choosing a career in scientific research.
– A faculty member’s commitment to fostering the student’s academic success results in positive mentor relationship outcomes regardless of the racial similarity between mentor and mentee.
– As it pertains to the geosciences in particular, positive student outcomes of mentoring have been demonstrated in geoscience-specific programs.
– Macrosystem perspectives of peer support networks and community building efforts play an important role in fostering student engagement and retention in STEM majors and positive student outcomes.
– Many positive student outcomes are associated with bridge programs, including increased interest in the geosciences, relationship building between student and faculty members, development of research skills, knowledge gained regarding careers in STEM and the geosciences, knowledge gained about the college application process, and increased self-efficacy.
What proportion of the STEM-interested students enroll in STEM-related career academies? Are there differences in course taking patterns among STEM-interested students who do or do not enroll in such academies? How do the course taking patterns of STEM-interested students in Florida compare with other students in the USA?
This paper examines differences in STEM retention between minority and non-minority
undergraduate students. It examines the role of ability in the switching decision and timing, they estimate STEM and non-STEM ability, and then compare the joint distribution of students who switch out of STEM versus STEM stayers.
1) How do students’ math and science self-efficacies relate to students’ post-secondary education plans? Are there differences by gender? 2) Is gender or race related to students’ taking of computer science courses? In the student’s choice of a computer science career? 3) Do students with individualized education plans (IEPs) differ from general education students in their expectations to obtain a degree post high school? Of the students that have an IEP, are there differences in their expectations for post-secondary plans by socioeconomic status? 4) Does participating in extracurricular activities (EA) have an effect on a student’s plans to attend college? Does SES status affect the relationship between participation and educational plans?
– Elementary students tend to perceive science classes as important, useful, and interesting.
– Students are likely to use various cognitive strategies in science classes.
– The mean science achievement score of 7.36 out of 14 revealed that students have a moderate level of science achievement.
– Self-efficacy and task-value significantly predicted students’ science achievement; cognitive engagement did not.
– Self-efficacy provided the strongest contribution to explaining science achievement. Task value makes the second strongest contribution.
– All independent variables were positively correlated with each other – higher levels of self-efficacy and task value were associated with higher levels of cognitive engagement.
– Student motivation (i.e., self-efficacy and task value) significantly contributed to the prediction of students’ science achievement.
– Positive and significant correlations were found among self-efficacy, task-value and cognitive engagement.
– Cognitive engagement failed to significantly predict students’ science achievement.
From Description to Explanation: An Empirical Exploration of the African-American Pipeline Problem in STEM
Which contemporary theoretical perspectives on access and participation best explain the differences between African-American science majors in the pipeline and those African-Americans who have successfully matriculated into STEM careers?
Early Experiences and Integration in the Persistence of First-Generation College Students in STEM and Non-STEM Majors
To what extent are demographic and academic background, commitment and support, early experiences and integration, and ï¬rst semester academic outcomes related to the persistence of First generation college students (FGCSs) based on major in physical sciences, engineering, math, and computer sciences (PEMC-STEM), other-STEM, and non-STEM)?.
STEM Field Persistence: The Impact of Engagement on Postsecondary STEM Persistence for Underrepresented Minority Students
1) Do the BPS:04/09 data support that underrepresented minority students leave STEM fields? 2) Does the BPS:04/09 demonstrate differential engagement for underrepresented minority students in STEM fields? 3) Do the differing engagement behaviors contribute to STEM attrition of underrepresented minorities?
– Gendered choices they make remain largely intact after conditioning on prior test scores, indicating that these choices are not driven by differences in perceived mathematical ability, or by boys’ comparative advantage in mathematics.
– Girls who choose matriculation electives in physics and computer science score higher than boys, on average.
– Girls and boys react differently to early signals of mathematical and verbal ability.
– Girls are less adversely affected by socioeconomic disadvantage.
– Girls score higher in all four subjects, with a greater advantage in
language arts than in mathematics and science, implying that boys have a comparative advantage in mathematics and science.
– There is a strong pattern of gender streaming in the choice of electives in science and mathematics. The share of boys choosing advanced physics or computer science is more than twice that of girls; the share of boys choosing advanced mathematics is about 20% higher; while the share of girls choosing advanced biology is about 60% higher than boys and their share in advanced chemistry is 40% higher.
– For physics or computer science and for advanced mathematics, accounting for the observed gender difference in the distribution of prior mathematics achievement widens the gender gap very slightly.
– For biology and chemistry, accounting for differences in prior
achievement reduces the gap favoring girls by 0.6 percentage points.
– In the regression, as girls do slightly better than boys in eighth-
grade mathematics, controlling for prior achievement in mathematics increases the gender gap favoring boys in physics or computer science and in advanced mathematics, by 1.0 and 1.2 percentage points respectively while reducing the gender gap favoring girls in biology or chemistry by 0.8 of a percentage point.
– The largest effect is in advanced mathematics and the smallest in biology or chemistry, in line with the relevance of mathematical ability for each subject.
– All prior scores exhibit a statistically signiﬁcant, positive (and in most cases convex) relationship with the probability of choosing a science or mathematics elective.
– An interaction term, the product of the mathematics and Hebrew scores, also has a signiﬁcant positive effect.
– Boys’ and girls’ different propensities to choose science and mathematics electives are partly a reﬂection of their different responses to prior signals of ability. A signal of strong mathematical ability has a positive effect on both boys and girls for all three categories, but the effect is stronger for boys with regard to choosing advanced mathematics and physics or computer science, and stronger for girls with respect to choosing biology or chemistry; and a similar pattern applies to prior achievement in science.
– Selection of science and mathematics electives increases in parents’ education. The rate of increase is more moderate in biology or chemistry; and the share of girls declines with parents’ education in all electives. These ﬁndings are a further indication that boys beneﬁt from a strong family background more than girls.
– The size of the gender gap increases in parental education for all electives, and more steeply in the male-dominated subjects, mathematics and physics or computer science, showing again that boys beneﬁt more from a strong family background.
– Of the three groups, coeducational religious schools serve a population of students from markedly lower income groups, and achieve the lowest GEMS scores in all subjects for both male and female students in these schools. Coeducational non-religious schools and single-sex religious schools have more similar student populations.
– In non-religious schools, girls outperform boys, whereas boys outperform girls in religious schools.
– Single-sex religious schools have the highest matriculation rates, followed by coeducational non-religious schools.
Exposure to School and Classroom Racial Segregation in Charlotte-Mecklenburg High Schools and Students College Achievement
1. Do the effects of school racial segregation extend into early college outcomes among students graduating from CMS schools and entering the UNC system?
2. Is minority representation in the upper-track classes related to students’ first year college achievement?
3. Do the levels of within-school segregation due to tracking exacerbate the negative effects of attending a segregated black high school?
The Role of STEM High Schools in Reducing Gaps in Science and Mathematics Coursetaking: Evidence from North Carolina
The authors examined whether underserved students in North Carolina STEM high schools have similar or higher rates of advanced science and mathematics course taking than students in neighboring traditional high schools.
To estimate the impact of “inclusive” science, technology, engineering, and mathematics (STEM) high schools.
Money or Diversity? An Implementation Analysis of the Voluntary Transfer Program in St. Louis, 1999-2009
How did fiscal resources and human interests affect suburban implementation of the voluntary transfer program between 1999 and 2009?
Aligning Science Achievement and STEM Expectations for College Success: A Comparative Study of Curricular Standardization
This paper examines student science
achievement in the precollege years, focusing
on students who indicate they plan to major
in science or pursue a science career. It compares the United States with other industrialized countries in terms of science achievement and determines the degree to which crossnational variations in standardization of the curriculum are related to science achievement, net of other country-level factors such as teacher quality and economic development. The authors then examine cross-national variations in students’
future orientations toward STEM to determine
whether curricular standardization is related
to the alignment of students’ science achievement with their plans to pursue a STEM major or career
Exploring the Foundations of the Future STEM Workforce: K-12 Indicators of Postsecondary STEM Success
- What K-12 indicators predict postsecondary STEM success?
- To what extent do K-12 indicators of postsecondary STEM success differ for Hispanic and non-Hispanic students?
The Role of Mothers’ Communication in Promoting Motivation for Math and Science Course-Taking in High School
– There was a signiﬁcant effect of the experimental intervention on course-taking, such that adolescents whose parents received the intervention took more MS in 12th grade, compared with controls.
– There was an indirect effect of personal connections on STEM course-taking through adolescent’s interest.- More years of mother’s education were associated with higher perceptions of adolescents’ math ability.
– Neither mothers’ years of education nor mothers’ perception of adolescents’ math ability predicted number of conversations between mothers and adolescents or personal connections articulated in the interviews.
– Mothers with more years of education generated more elaborated responses in their interview.
– There was a signiﬁcant interaction between number of conversations and elaboration, such that the highest level of interest occurred with high elaboration and few conversations.
– Adolescents whose parents received the intervention reported more UV in 10th grade than those whose parents were in the control group.
– Higher levels of interest in 10th grade predicted more STEM courses taken in 12th grade.
– There was a signiﬁcant interaction between elaboration and number of conversations such that the highest levels of course-taking were achieved either with the combination of high elaboration and fewer conversations, or less elaboration but more conversations.
Does persistence within STEM majors differ by gender?
Racial/Ethnic Differences in Perceptions of School Climate and Its Association with Student Engagement and Peer Aggression
Do Black, Hispanic, and White students differ in their perceptions of school climate?
Do the associations between authoritative school climate and student engagement as well as peer aggression differ for Black, Hispanic, and White students?
– Their findings indicate that attending a high school with better disciplinary order and stronger school attachment for the students is associated with a decreased likelihood of dropping out, above and beyond individual characteristics.
-They found that higher school SES translated to better school attachment, disciplinary order, and academic climate. Yet, disciplinary climate was the most positively influenced by school SES, with a one standard deviation (SD) increase in school SES being associated with about half a unit increase in disciplinary climate.
-The percentage of minority students was inversely related to school attachment, controlling for model variables.
-There is an indirect effect of school composition on dropping out. The larger the percentage of minority students the less attached they feel to their school so they are more likely to drop out.
– The researchers found that attending a high school with better school attachment greatly reduced the odds of a student being a dropout.
-Attending a school with more disciplinary order also directly de- creased the likelihood that a student was currently identified as a dropout.
-They also found that both prior math achievement and student SES were again strong predictors of whether a student had ever dropped out.
1) How do curricular emphases differently affect engineering learning outcomes by gender? 2) How do instructional approaches differently affect engineering learning outcomes by gender? 3) How does participation in co-curricular experiences differently affect engineering learning outcomes by gender?
If a student enrolls in a charter school rather than a non-charter school in the same district, what will the student encounter in terms of racial isolation, poverty level, and the school’s performance?
Examination of Factors that Predict Academic Adjustment and Success of Community College Transfer Students in STEM at 4-Year Institutions
1) What background characteristics, community college experiences, and university experiences predict academic adjustment for community college transfer students in engineering and other STEM (nonengineering) disciplines? 2) What background characteristics, community college experiences, and university experiences predict cumulative GPA for community college transfer students in engineering and other STEM (nonengineering) disciplines?
Computing Whether She Belongs: Stereotypes Undermine Girls’ Interest and Sense of Belonging in Computer Science
The authors predict that belonging will have a particularly strong influence on interest because belonging is a fundamentally important motivator. They also examine a potentially important individual difference that may affect belonging- whether students feel that they personally fit the stereotype of a computer scientist.
Characteristics of US Students That Pursued a STEM Major and Factors That Predicted Their Persistence in Degree Completion
1) What are the characteristics of students’ who declared a STEM major? 2)What are the characteristics of students who completed a STEM major? 3)What factors influence students who persisted to complete a STEM major?
1) To what extent do students attending inclusive STEM high schools experience more advanced STEM courses, engaging STEM teaching, real-world STEM experiences, and supports for succeeding in STEM courses and applying to college than do students attending other high schools? 2) To what extent do ISHS students’ STEM interests, activities, achievement, and expectations differ from those of demographically similar students attending high schools without a STEM focus? 3) How are the features promoted for inclusive STEM high schools related to student STEM outcomes?
- How large are general knowledge gaps occurring in kindergarten, and to what extent do these continue to occur by the end of first grade?
- As children move from third to eighth grade, what is their typical initial level (i.e., intercept) and rate of achievement growth (i.e., slope) in science?
- Are these gaps consistent with stable, cumulative (i.e., gap increasing), or compensatory (i.e., gap decreasing) achievement growth trajectories? How do these initial third-grade science achievement levels and third- to eighth-grade growth trajectories vary by children’s race, ethnicity, language, and family SES status? How are a more general set of child- and family-level characteristics, including parenting quality, related to typical levels of third-grade science achievement in the United States as well as to achievement growth from third to eighth grade?
- To what extent are the third-grade science achievement gaps, as well as third- to eighth-grade science achievement growth, explained by such modifiable factors as general knowledge, reading and mathematics achievement, and behavioral self-regulation? How much of children’s later science achievement can be predicted by their first-grade achievement-related knowledge, skills, and behaviors?
- With the aforementioned first-grade predictive factors accounted for, how important are the modifiable factors of children’s subsequent reading and mathematics achievement, and behavioral self-regulation at each of third, fifth, and eighth grades to their science achievement during these grades?
- To what extent does a school’s academic climate and racial, ethnic, and economic composition explain children’s science achievement, over and above the afore- mentioned child- and family-level factors?