How to enrich our gifted students?
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1 1 / 22 Evidence from a talented program in secondary education Adam S. Booij Ferry Haan Erik Plug UvA UvA UvA Workshop Educational Governance and Finance, Asker May 2014
2 Bored in class... Introduction 2 / 22
3 Bored in class... Introduction 3 / 22
4 Gifted and Talented programs Introduction 4 / 22 Education systems seem focused on improving the bottom of the distribution (Neal & Whitmore, RES 10) Therefore, growing interest in educational programs focused on the Gifted and Talented. High diversity in implementation: Grouping special (magnet) schools special classes (GT tracking) special hours/projects ( Taken from class ) Content: compacting, speeding, enriching (depth or breadth) This paper: taken from class for enrichment project
5 Introduction 5 / 22 Treatment: Nijmegen Enrichment program Combined academic Junior - Senior high school in NL A gifted student receives an enrichment passport Valid grade 7-12 (exam year) Taken from class : Freely exchange lessons for project time (3-4 hours per week, minimum 2 lessons) Choose the program subject Coached by selected and trained teachers V-market end of year where students present projects Extra grade on school report (O, V, G, P) Most of the enrichment activity in the first four years Many students trade enrichment activities for extra subjects, or colleges in University
6 Treatment: Nijmegen Enrichment program Introduction 6 / 22
7 Introduction 7 / 22 Gifted and Talented research International evidence is scarce and does not credibly deal with selection (Matthews et al., 2012) Notable exceptions are School: Abdulkadiroglu et al. (RCT,Ectrca 14), Pop-Eleches & Urquiola (RD,AER 13); contradictory effects Classes: Epple et al. (RD,NBER 10) retention effect; Card & Giuliano (RD, WP 13), Bui et al. (RD&RCT, AEJ forth): no effect testscores Hours/Projects: Bhatt (IV, SSRN 09): positive effects testscores
8 Selecting gifted students Context, empirical strategy, and data 8 / 22
9 Descriptive statistics: Selective assignment Context, empirical strategy, and data 9 / 22 Variable Range Regular Enrichment Mean s.d. Mean s.d. Diff p-value Background Boy {0,1} Age [9.7, 14.8] Pretestscores IST score [41, 148] FES score [5,40] CITO score [518,550] Treatment N
10 Variation and balancing 10 / 22 First stage Fraction Treated Fraction Local Average Linear fit Quadratic fit Cubic fit Zoom 1SD Donut 0.2SD normalized IST distance to threshold
11 Variation and balancing 11 / 22 School selects cutoff: Bunching at the threshold McCrary test Density Note: Discontinuity estimate (log difference in height): 0.39 (0.11) [0.000] normalized IST distance to threshold
12 Balancing Variation and balancing 12 / 22 standardized Cito Local Average Linear fit Quadratic fit Cubic fit Zoom 1SD Donut 0.2SD normalized IST distance to threshold
13 Reduced form Dissappointed? Results 13 / 22
14 Reduced form Standardized Math Results 14 / 22 standardized Math Local Average Linear fit Quadratic fit Cubic fit Zoom 1SD Donut 0.2SD normalized IST distance to threshold
15 Reduced form Standardized Language Results 15 / 22 standardized Langauge Local Average Linear fit Quadratic fit Cubic fit Zoom 1SD Donut 0.2SD normalized IST distance to threshold
16 Reduced form Repeater or dropout Results 16 / 22 repeater or dropout Local Average Linear fit Quadratic fit Cubic fit Zoom 1SD Donut 0.2SD normalized IST distance to threshold
17 High school grades IV estimates Results 17 / 22 Sample selection Math Language (1) (2) (3) (4) (5) (6) Matched Retention Average Exam Average Exam Enrichment (0.02) (0.05) (0.13)*** (0.21)** (0.13)** (0.21)+ std FES score (0.00)+ (0.01) (0.02)*** (0.02) (0.02)* (0.03)*** std CITO score (0.00)*** (0.01)*** (0.02)*** (0.03)*** (0.02)*** (0.04)*** IST-Poly Controls i.cohort y sd (y) p-value FS F -stat R N
18 Gender differences IV estimates Results 18 / 22 Math Language (1) (2) (3) (4) Average Exam Average Exam Baseline (N = 3349) Enrichment (0.13)*** (0.21)** (0.13)** (0.21)+ Girls (N = 1540) Enrichment (0.21) (0.34)+ (0.20)** (0.33)** Boys (N = 1809) Enrichment (0.17)*** (0.29)+ (0.18)+ (0.28)
19 University subject choice IV estimates Long-term outcomes 19 / 22 Effect of Enrichment Chose field of study... Predicted wage Fraction Total Boys Girls Healthcare (0.08) (0.10) (0.14)+ Economics and business (0.06) (0.10) (0.08) Law and governance (0.06) (0.08) (0.10) Language and communications (0.05) (0.06)+ (0.10) Educational and pedagogy (0.03)+ (0.03) (0.06) Behavioral and societal sciences (0.05)+ (0.05)* (0.10) The Arts (0.06)* (0.09)** (0.10) Science and informatics (0.07) (0.12) (0.07) Technical (0.07) (0.11) (0.09) Earth and environment (0.06) (0.09) (0.07)
20 Mechanisms Mechanisms 20 / 22 Controls unaffected [no apparant peer, dissapointment, or class size effect ] No substitution away from other subjects Gender diff explained by task choice Other: Survey [May 2014, individualized, online] Homework/Learning time Motivation/Boredom/Academic Self Concept Peers [who and how many?] Clear future plans / visit universities Treated pupils [end of survey] Number of lectures/hour exchanged [current and past] Substitute away from which subjects [best?, least interesting?] Received direct help from tutor Used for other purpose
21 Conclusion 21 / 22 Conclusion The Enrichment program seems to have made Boys perform (substantially) better in Maths Girls perform better in Languages Effects persistent over time, no subs from other subjects HE subject choice is also affected Girls subs. Language and Edu for higher paid Healthcare Boys switch more and subs. Arts for higher paid Science and Law Survey should reveal mechanisms Given relative low cost involved, giving gifted students some (guided) liberty to enrich themselves is effective in improving test-scores and career choice
22 Gifted Conclusion 22 / 22
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