ED Every Day, Every Child: EC A Partnership for Research with Elementary Math and Science Instructional Specialists This material is based upon work supported by the National Science Foundation under Grant No. DRL- 1316520. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Elementary Science Instruction • 3 basic models, with sub-categories (Gess- Newsome, 1999) – Classroom generalist model (traditional self- contained) – Specialist: Student Instructional Models* • Departmentalized model, or Collaborative Specialist (Nelson & Landel, 2007) >>> Team-Teaching • Pull-out model >>> Science as a special – Specialist: Teacher Mentoring Models • Resource/Coaching model • Science support team model
Our focus: Elementary Science Instructional Specialists Operational definition: An instructional specialist is a full time (elementary) classroom teacher who teaches two or more classes of students in a specific content area. …but the devil is in the details
Our Project - Five Studies EDEC is an exploratory research project to • Study 1: understand and categorize instructional specialist models in mathematics and science; • Study 2: investigate the content knowledge, preparation and needs of teachers in these roles; • Study 3: determine the instructional effectiveness of instructional specialists; and • Study 4: determine the impact of instructional specialists on student learning and attitudes towards mathematics and science. • Study 5: identify the prevalence of models of content specialization in mathematics and science in elementary schools nationally.
Science Specialist Sample • Year one – 15 total – 10 participated as science specialist only – 5 participated as both math & science specialist • Year two – 15 total – 8 participated as science specialist only – 7 participated as both math & science specialist (two had been science only & added math) • 19 total specialists completed at least part of study (4 lost due to retirement, reassignment, & attrition) • 6 districts, 13 schools
Specialists & Self-Contained Matches: Demographics Demographic Elementary Science Science Self-Contained Specialists Participants Mean age (years) 46.6 44.3 Mean teaching experience 15.6 18.2 (years) Mean school % FRL 48.4 52.5 students Mean school % students of 35.3 36.7 color
Diversity of Models in Our Sample Model Other subjects taught # of Teachers besides science Team-Teaching (within- All but reading (1) or 2 grade) mathematics (1) Team-Teaching (within- 2-3 of Spanish literacy, 2 grade) math support in Spanish, social studies Team-Teaching (within- Math (all 6) plus some 6 grade) combination of writing, social studies, and/or art Team-Teaching (within- Social studies (2); word 2 grade) study (1) Team-Teaching (across- Reading, social studies, art 2 grades) (1); None (1) Science as a special Technology (2); Reading 5 (1); None (2)
Study 2
Study 2 Research Question & Measures • RQ: How do factors related to teacher preparation & content knowledge differ for instructional specialists & non-specialist teachers? • Measures – Online survey (preparation) – Harvard MOSART (content knowledge) • Astronomy/Space Science, Earth Science, Life Science, Physical Science subtests
Findings – Formal Preparation • Specialists were more likely to have completed a science content degree (N = 5, 26%) than self- contained teachers (N = 0) • Specialists and self-contained teachers were equally likely to have completed a science education degree (N = 3, 16% for specialists; N = 3, 19% for self- contained)
Findings – Preparedness • Specialists rated themselves higher (p < 0.05, two-tailed independent samples T-test) on survey questions assessing: – Preparedness to teach science – Familiarity with NGSS – Preparedness to teach engineering content – Agreement with: • “I know the strengths & weaknesses of each of my students in science” • “I have enough time with my students to meet their needs in science” • “I have enough time to plan for all the subjects I teach”
Findings – Content Knowledge MOSART Test Specialists Self-Contained Mean p value (1- Effect size (N=19) (N=16) Difference tailed) (Hedges’ g) Mean SD Mean SD Astro/Space 74.74% 14.81 62.67% 22.84 12.07 pp 0.036* 0.6243 pp pp Earth 87.16% 9.94 pp 80.00% 12.38 7.16 pp 0.035* 0.6293 pp Life 85.26% 10.42 81.87% 12.91 3.40 pp 0.201 0.2851 pp pp Physical 70.00% 13.54 60.67% 15.10 9.33 pp 0.034* 0.6388 pp pp OVERALL 80.42% 9.21 pp 72.93% 13.74 7.49 pp 0.034* 0.6367 pp
Study 3
Study 3 Research Questions & Measures • RQ1: How does the quality of instruction of science specialists compare to that of matched non-specialist teachers? • RQ2: What factors related to preparation, content knowledge, & instructional model predict qualify of instruction? • Measures – Modified AIM Observation protocol (Horizon Research) – Online Survey + MOSART scores
Study 3 Findings – Instructional Quality Effect Size (Hedges' Score Type Teacher Type N of Observations Mean SD g) Specialist 47 2.8436 0.65112 Adjusted Total Self-Contained 44 2.6015 0.65049 0.3688 Specialist 47 2.9362 0.81838 Targeted Idea Self-Contained 44 2.6818 0.82892 0.3063 Specialist 30 2.7333 0.69149 Initial Ideas Self-Contained 28 2.7857 0.99469 -0.0607 Specialist 45 2.8667 0.78625 Engaging with Phenomena Self-Contained 43 2.9302 0.73664 -0.0826 Specialist 39 2.6923 0.95018 Using Evidence to Make Claims* Self-Contained 41 2.2439 0.76748 0.5155 Specialist 21 2.4286 0.87014 Sense Making* Self-Contained 24 1.875 0.78741 0.6577 Specialist 47 3.0851 0.58346 Classroom Culture Self-Contained 44 2.8409 0.86113 0.3312 * Indicates p < 0.05, two-tailed
Differences in Instructional Quality • Specialists scored significantly higher on the following lesson aspects, with medium effect sizes: – Using evidence to make claims – Sense-making • What factors might predict these differences? What is it about being a specialist that made the difference?
Study 3 – Predictors of Observation Scores • Variables included in stepwise regression: – Teacher Type (specialist / self-contained) – Composite MOSART scores (content knowledge) – Number of semester science or science education courses completed by teacher – Years of teaching experience – Time per week spent teaching science – Time per week spent planning for science – Whether teacher had completed science PD in last 3 years* *Note: Nearly all specialists had completed recent science PD while very few matches had done so, making this variable nearly identical to the variable for teacher type
Study 3 Findings – Predictors of Observation Scores Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate .302 a 1 .091 .079 .79657 a. Predictors: (Constant), MWSP ANOVA a Model Sum of Squares df Mean Square F Sig. .009 b 1 Regression 4.598 1 4.598 7.246 Residual 45.686 72 .635 Total 50.284 73 a. Dependent Variable: EvClaims b. Predictors: (Constant), MWSP Best model predicting score on using evidence to make claims was one that only included MWSP – minutes per week of science planning
Study 3 Findings – Predictors of Observation Scores Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate .380 a 1 .145 .124 .77686 a. Predictors: (Constant), MWSC ANOVA a Model Sum of Squares df Mean Square F Sig. .011 b 1 Regression 4.289 1 4.289 7.106 Residual 25.348 42 .604 Total 29.636 43 a. Dependent Variable: SenseMake b. Predictors: (Constant), MWSC Best model predicting score on sense-making was one that only included MWSC – minutes per week spent teaching science content
Summary • Elementary science specialists in our sample scored higher than self-contained teachers on measures of content knowledge and preparation to teach science • Specialists appeared to be more skilled at leading students through the processes of using evidence to make claims and sense-making. – Differences primarily attributable to specialists having MORE TIME to both plan for and engage students in science lessons
Limitations • Sample size (< 20 per teacher type) • Possible limitations of content knowledge measure • Data on time spent planning for & teaching science are self-report
Thank you! - Participating students & teachers - Today’s audience Joe Brobst, Ed.D. Science, Mathematics, & Technology Education Western Washington University Joe.Brobst@wwu.edu (302) 383-5194 (cell)
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