Talent Development in Science, Technology, Engineering, in addition to Mathematics (STEM) S
Landrum, Brian, Operations Manager has reference to this Academic Journal, PHwiki organized this Journal Talent Development in Science, Technology, Engineering, in addition to Mathematics (STEM) Sylvia Hurtado, Kevin Eagan, Gina Garcia, Juan Garibay, & Felisha Herrera AERA Annual Meeting, Vancouver, CanadaApril 13, 2012Overview of SymposiumIntroduction to TopicPaper 1: Passing Through the Gates: Identifying in addition to Developing Talent in Introductory STEM CoursesPaper 2: Accentuating Advantage: Developing Science Identity During CollegePaper 3: A Model as long as Redefining STEM through Identity: Insights from the Educational Trajectories of Talented STEM Graduate Students Implications & ConclusionsIntroductionThe symbiotic connections of an ecosystem in addition to survival of the fittest.We explore the interdependences of context, student in addition to the role of faculty that result in talent development, while at the same time, the same elements are involved in sorting to limit the production of scientists.
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Passing Through the Gates: Identifying in addition to Developing Talent in Introductory STEM CoursesBackgroundSTEM attrition in the first two years of collegeLow grades in addition to un-engaging pedagogy are just some of the obstacles students encounterSuccess in addition to talentMeasured by gradesDetermined by prior achievement in addition to study skillsPurposeTo explore alternative measures of talent (beyond grades) in introductory STEM coursesTo determine how talent is developed in addition to harvested within introductory STEM coursesTo examine how thinking in addition to acting like a scientist contributes to success in STEM courses
Sequential, Explanatory Mixed Methods DesignCollected, analyzed, in addition to integrated both quantitative in addition to qualitative data during the research processQuantitative data collected first; in as long as med selection of institutional sites as long as qualitative data collectionData fully integrated during the analysisQuantitative data provided a broad picture of students engagementQualitative data more deeply explored student views regarding their introductory classroom experienceQUANTITATIVE Data CollectionIntegrating Quantitative & Qualitative ResultsConnecting Quantitative & Qualitative PhasesQualitative Data CollectionQualitative Data AnalysisQUANTITATIVE Data AnalysisQuantitative MethodologyFour data sourcesPre- in addition to post-survey as long as students in introductory courseOne-time survey as long as faculty teaching introductory courseRegistrars dataSample15 colleges in addition to universities73 introductory STEM courses2,873 students52% White61% Women42% aspired to earn a medical degree21% aspired to earn a Ph.D. or an Ed.D.75% reported majoring in a STEM discipline.
Quantitative MethodologyThree outcome variablesFinal grade in introductory courseActing like a scientist (latent)Thinking like a scientist (latent)Predictor variablesDemographic characteristicsPre-college preparationExperiences in introductory STEM coursesPedagogical techniques used in introductory STEM coursesQuantitative MethodologyWeighted data to adjust as long as non-response biasMissing values analysisConfirmatory factor analysisMultilevel structural equation modeling (SEM)
Qualitative MethodologyEight sites1 HBCU, 1HSI, 6 PWIsTwo data sourcesStudents: 41 focus groups (n = 241 students)54% White21% Asian/Asian America14% African American8% Latino3% Native American62% WomenFaculty: 25 in-depth interviews with facultyChemistry, biology , mathematics, & engineering Qualitative MethodologySemi-structured interview protocolExperiences in introductory STEM courses, motivation, course structure, learning, instruction, & assessmentGoals in addition to objectives as long as introductory STEM courses, pedagogical approaches, structure, as long as ms of assessment, & institutional support as long as teachingEmergent code developmentOpen coded in NVivo8Inter-rater reliability: 80-85%Re-validated coding architectureLinked codes to participant attributesAlternative Ways to Identify Talent I like the questions they ask, so as long as the vert bio I’ll be lecturing long in addition to I’ll ask a little question here in addition to there that might be pointed. You know, like, how do you think the sharks ventilate if they’re not doing this buccal pumping kind of thing, cuz they dont have the operculum I’ll get them to, I answer questions in class just to make sure [they’re] kinda tracking me or thinking about stuff. But then the ones that I’m like, whew, you’re really good, [ask], “Okay, you’ve told me about how they change their osmoregulation when they go from fresh water to salt water. How exactly does that happen, in addition to how does it happen on the way back (Professor Veerdansky, Western Private Masters College)
Direct Effects: Final GradeGrades Do Not Matter Yeah. I had a student he got [a] B plus, but he would solve problems that nobody could solve. He wouldnt be able to solve the problems that everybody could solve, but he solved the problems that no one could. Now, that was very impressive, but he didnt do well on the exams he actually did very well later on. (Professor Pace, Western Public Research University)Direct Effects: Acting Like a Scientist
Acting Like a Scientist Well, like how the labs really supplement the class, like they really make you think about the main concepts, about like how you would apply it to like real life or what you would actually do that shows this process of whatever. The really helps you kind of think about it other than just like bullet points on a piece of paper, so that really helps. (Marissa, Southeastern Private Masters College) Direct Effects: Thinking Like a Scientist Thinking Like a Scientist Well, I took Basic Chemistry last year, in addition to Im taking General Chemistry, which is the next step above it, in addition to I feel like I was really prepared as long as it. Cuz right now Im in Gen Chem [ in addition to ] like, I already know this, yeah Like, I guess the professor who taught me was good at what she was doing cuz I already knew what I was doing in addition to like, right now some kids are already confused about like, the stuff we learned last year. And we were supposed to know this already, but I guess they were confused because of the professor. But as long as me it was kind of a breeze. (Sameer, Southwestern Public Research University)
DiscussionGrades useful as long as sorting talent but not as long as capturing gains in dispositions as long as scientific workNecessary to broaden per as long as mance criteriaChange pedagogical styles to allow students to apply concepts to encourage thinking like scientistReframe introductory STEM courses to focus on higher-order thinking rather than merely transmission of knowledgeAccentuating Advantage: Developing Science Identity During CollegeKevin Eagan, Sylvia Hurtado, Juan Garibay, & Felisha HerreraBackgroundEarly commitment to STEM can have lasting effects on STEM persistence.Call to identify practices that promote stronger STEM identity given high attrition rates in STEM.Strong STEM identity:Improves STEM retention (Chang et al., 2011)Shapes trajectories within STEM disciplines (Carlone & Johnson, 2007)
PurposeTo examine how students experiences at various time points in addition to across institutional contexts help shape the development of students science identity during college.STEM IdentityCompetence, Per as long as mance, & RecognitionSTEM identity is a negotiated self, constantly under constructionSTEM identity is shaped by^:Individuals own assertionsExternal ascriptionsExperiences in STEM(Carlone & Johnson, 2007)^(Martin, 2007)Influences on STEM IdentityEarly learning experiences (Tran et al., 2011)Number of high school STEM courses (Russell & Atwater, 2005)Pre-college research experiences (Tran et al., 2011)AgentsFaculty & Peers (Carlone & Johnson; Martin, 2007)Parents (Tran et al, 2011)Self-efficacy (Carlone & Johnson; Hurtado et al., 2009)College ExperiencesUndergrad Research Programs (Hurtado et al., 2009)STEM Culture (Seymour & Hewitt, 1997)
Theoretical FrameworksCumulative Advantage (Allison & Stewart, 1972; Cole & Cole, 1973; Merton, 1973)To examine patterns of inequality across timeAccentuation Effects (Feldman & Newcomb, 1969)To acknowledge in addition to comprehend how predispositions are accentuated during collegeQuantitative MethodologyData Sources:2004 CIRP Freshman Survey2005 CIRP Your First College Year Survey2008 CIRP College Senior SurveySample:1,133 aspiring STEM majors137 institutionsAnalysis:Structural Equation Modeling (SEM)MPlus SoftwareCFI= 0.93, RMSEA=0.03
Contact InfoThis study was made possible by the support of the National Institute of General Medical Sciences, NIH Grant Numbers 1 R01 GMO71968-01 in addition to R01 GMO71968-05, the National Science Foundation, NSF Grant Number 0757076, in addition to the American Recovery in addition to Reinvestment Act of 2009 through the National Institute of General Medical Sciences, NIH Grant 1RC1GM090776-01. This independent research in addition to the views expressed here do not indicate endorsement by the sponsors. Papers in addition to reports are available as long as download from project website: http://heri.ucla.edu/nihProject e-mail: firstname.lastname@example.orgFaculty/Co-PIs:Sylvia HurtadoMitchell ChangTanya FigueroaGina GarciaJuan GaribayPostdoctoral Scholars:Kevin EaganJosephine GasiewskiAdministrative Staff:Dominique HarrisonGraduate Research Assistants:Felisha HerreraBryce HughesCindy Mosqueda
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