教育学特殊XIV　第4講

慶應義塾大学教育学特殊 XIV(第 4講)学級規模を研究する

2. 海外の文献のレビュー

文部科学省国立教育政策研究所

文部科学省

国立教育政策研究所

総括研究官やま山もり森

こう光よう陽

(教育心理学)[email protected]

2015年 5月 13日

この内容は個人的見解であり国立教育政策研究所の公式見解ではありません

はじめに

本日の出席とスライド

本日の出席

https:

//questant.jp/q/150513

本日のスライド

http://www.slideshare.net/

koyoyamamori/cs150513

慶應義塾大学教育学特殊 XIV 第 4 講 2015 年 5 月 13 日 2 / 37

はじめに

今回の内容

1 学級規模と学力

2 学級規模と動機づけ等

3 学級規模が児童生徒に与える影響を検討する視点


学級規模と学力


小学校 [Bosworth, 2014]

問題1 先行研究では小規模学級の方が学力が高く学力差が小さい傾向。

2 小規模学級への割り当てはランダムではない。3 上記 2の要因は上記 1と交絡するのではないか。

目的小規模学級への割り当てはランダムか。小規模学級ほど学力が高いか (得点)。小規模学級ほど学力差が小さいか (標準偏差)。




対象米国ノースカロライナ州の 4, 5年生。NCRDCの大規模データセット。

方法マルチレベルモデル教師要因を固定

学校に差がない：変量効果　ある：固定効果先生に差はない：変量効果　ある：固定効果変量＝ランダム




結果小規模学級への割り当てはランダムではない (Table 3)。テスト得点は小規模学級の方が高い傾向 (Table 4)。

テスト得点は一人多いと標準偏差で 0.002～ 0.005低い10人では偏差値 0.2～ 0.5くらい。

テストの標準偏差は小規模学級の方が小さい傾向 (Table 8)。一人多いと標準偏差で 0.02くらい大きい。10人減ると 0.2くらい小さくなる。

考察では指導方法の違いに言及

論点� �last year scoreをモデルに投入する意義学級規模による指導方法の違いと児童の個人差� �



読書力 [Konstantopoulos and Traynor, 2014]

問題実験研究 (RCT)ではなく調査研究では学級規模が学力に与える影響についての結果がまちまち (クラスサイズパズル)[Bosworth and Caliendo, 2007, Lazear, 2001]実験的研究 (STAR)のデータは古く現状がよくわからない[Word et al., 1990]

目的操作変数法 (instrumental variables (IV) method)で推定。学級規模が読書力に与える影響を検討。

対象ギリシャの小 4の PIRLSデータ




方法マルチレベルモデル操作変数法 (回帰係数に影響を与えると考えられる変数をモデルに投入して当該回帰係数を正しく見積もろうとする)

結果操作変数を投入したモデルでは学級規模が読書力に影響を与えるとは示唆されず。

論点� �このような結果となったのはどういうことが背景にあるのだろうか？� �




- 43 -



中学生 [Krassel and Heinesen, 2014]

問題調査研究では学級規模が学力に与える影響についての結果がまちまち (クラスサイズパズル)[Bosworth and Caliendo, 2007, Lazear, 2001]

中学生を対象とした研究は珍しい。

目的小規模学級ほど学力が高いか (得点)。適切な推定結果を得るための分析手法の適用。

対象2002～ 07年までのデンマークの 10年生。




方法説明変数にGPA(いわゆる評定)を投入目的変数に上級学力テスト

学級規模が小さいほど受験する傾向にある

学校を固定効果として扱う。分断回帰デザイン

maximum-class-size rule:

Mst =eist

(int((eist − 1)/m) + 1)(3)

with e denoting enrollment, m the maximum class size, and int(z) the largest integersmaller than or equal to z. Hence, small changes in enrollment around thresholdvalues of the maximum-class-size rule have substantial influence on predicted classsizes. In the estimations, we set m ¼ 28. By national law, the maximum class size is28 for grades 1–9 in Denmark. Although there is no binding rule for grade 10, 28seems to function as a de facto maximum-class-size rule as illustrated in Figure 1,which shows M (with m ¼ 28) and average class size for each value of enrollment.Although the RDD is ‘fuzzy’, the probability of a small class size is higher when enroll-ment is just above a discontinuity point compared to just below (especially for values ofenrollment below about 100 where the density of enrollment is high), and the corre-lation between class size and M is high.

The 2SLS estimator of the parameter of interest, bCS, may be interpreted as aweighted average causal effect of changing class size in a more general model whereclass-size effects are heterogeneous and nonlinear, given independence and monotoni-city assumptions (Angrist and Imbens 1995): After control for other variables, theinstrument should be independent of individuals’ potential outcomes (potential GPAgiven class size); and an increase in enrollment may not reduce the number of classes.

The independence assumption is crucial. Since M is a (discontinuous) function ofenrollment and enrollment may be correlated with educational outcomes for otherreasons than class size, it is essential to control for these other channels of correlationbetween enrollment and outcomes. We use a rich set of control variables (for parentalbackground and other student specific characteristics, especially lagged academicachievement) and we also control for SFE. In addition, we control for a smooth function

Figure 1. Average and predicted class size for each value of enrollment, and density ofenrollment.

Education Economics 415

Dow

nloa

ded

by [

126.

249.

179.

227]

at 1

8:13

28

Apr

il 20

15




結果小規模学級ほど上級テストの受験率は高い。大規模学級ほどテスト得点が低い。過去の学力や学校独自の特性などを考慮したモデルの意義。

論点� �なぜ上級テストの結果を使うこととしたのか？操作変数法と分断回帰デザインの意義は？分断回帰デザインで見落とされている要因は何か？� �




at the 10% level. For brevity, we do not show first-stage estimates, but the instrument(M) is highly significant with F-values of 138 and 58 in models (4) and (5), respect-ively. Thus, there is no weak instrument problem. Estimating the IV–SFE model (5)for public and private schools separately, the class-size estimates become 20.062and 20.091, respectively, but they are not significantly different.

The point estimate in model (5) indicates that a reduction in class size by 10 pupilswill increase the probability of taking the six advanced exams by 6.5 percentage points;the estimates in models (1)–(4) indicate an effect of 1–2.5 percentage points. The signof the estimated effect is as expected, since smaller class size gives the teacher moretime with the individual student whereby achievement is likely to be higher and thecost of effort required for taking the advanced exam lower. As discussed above, inthe main regressions of the effect of class size on GPA, we restrict the sample topupils who have taken all six advanced exams in the mandatory subjects. Hence, ifthe share of academically weak pupils taking the advanced exams tends to be higherin smaller classes, then the estimated class-size effect on GPA (on the restrictedsample of exam-takers) tends to be biased towards zero, so (numerically) we arelikely to estimate a lower bound of the effect of class size on GPA.8

5.2. Class-size effects on GPA: main results

Table 5 shows the main estimation results where the dependent variable is the GPA ofthe six advanced exams in mandatory subjects. The models are similar to those inTable 4, except for the dependent variable. Column (1) shows OLS estimates of themodel without control for lagged GPA while columns (2)–(5) show estimates fromvalue-added models where R2 is much larger. In column (1), class size is clearly insig-nificant (with a small positive point estimate). Controlling for lagged GPA in model (2)results in a negative point estimate of 20.012 which is however not significant either.Controlling for lagged marks tends to lead to numerically larger negative OLS esti-mates of class-size effects. One interpretation is that a large share of academicallyweak tenth-grade pupils (who typically got low marks in ninth grade) tends to

Table 4. Linear probability models of choosing all six advanced exams in mandatory subjects.

(1) (2) (3) (4) (5)OLS OLS SFE IV IV, SFE

Class size/10 20.007 20.016 20.024∗ 20.019 20.065+

(0.014) (0.012) (0.010) (0.026) (0.034)GPA ninth grade (exam

marks)0.201∗∗∗ 0.192∗∗∗ 0.201∗∗∗ 0.192∗∗∗

(0.008) (0.007) (0.008) (0.007)GPA ninth grade (year marks) 0.152∗∗∗ 0.153∗∗∗ 0.152∗∗∗ 0.153∗∗∗

(0.006) (0.006) (0.006) (0.006)R2 0.080 0.284 0.353 0.284 0.250F-test, weak instrument 138.349 58.227Schools 482 482 482 482 482Observations 46,267 46,267 46,267 46,267 46,267

Notes: Robust standard errors clustered at schools in parentheses. In (5) R2 is from the regression using thewithin-school transformed variables. All regressions include the control variables of Table 3.+p , 0.10.∗p , 0.05.∗∗p , 0.01.∗∗∗p , 0.001.

K.F. Krassel and E. Heinesen422

Dow

nloa

ded

by [

126.

249.

179.

227]

at 1

8:13

28

Apr

il 20

15




induce schools to reduce class size, which will bias the estimated class-size effecttowards positive values in case controls for lagged achievement are not included,whereas this bias will be reduced if such controls are included. The class-size estimateof the SFE model (3) is similar to the estimate in model (2), and still not significant.Apart from nonrandom class-size variation, one reason why the class-size estimatesin the OLS models (1)–(3) are small and insignificant may be measurement error inclass size. Especially, some schools may tend to underreport the number of classesimplying unusually large observed class size. The estimation sample has been restrictedto observations with class size of 35 or less (as explained in footnote 6), but 35 is still avery large class size in the Danish context. If we exclude observations with class sizeabove 30, the OLS estimate of model (3) becomes larger numerically (about 20.025)and statistically significant at the 10% level.

In columns (4) and (5), we deploy our quasi-experimental RDD identification strat-egy and estimate IV models without and with SFE, respectively. Again, the instrumentis highly significant in the first-stage regression with F-values of 119 and 57, respect-ively. The IV estimate of the class-size effect in model (4) is 20.035, but only margin-ally significant (the p-value is 0.102), whereas the SFE–IV estimate in model (5) is20.08 and significant at the 5% level. The estimate in the preferred model (5) indicatesthat a reduction in class size by 10 pupils will increase GPA by about 0.08 standarddeviations in the distribution of individual marks. This is about 40% of the estimatesfound in Krueger (1999) for grades 0–3 using the STAR experiment data, in Angristand Lavy (1999) for grade 5 using a RDD (without control for SFE or lagged achieve-ment) and in Heinesen (2010) for grade 9 using variation in subject-specific class size,and of about the same magnitude as the estimates for fifth graders in the value-addedmodel of Rivkin, Hanushek, and Kain (2005). Possible explanations for our relativelysmall estimates for tenth graders is that the ‘treatment’ of a given class size only lasts forone year, that class-size effects might be smaller in tenth grade compared to earliergrades, and that pupil characteristics are different because the choice of the optionaltenth grade is highly selective. We have tried to include interaction terms betweenclass size and background variables such as gender, ninth-grade GPA, immigrant

Table 5. Regression models of GPA from advanced exams in mandatory subjects.

(1) (2) (3) (4) (5)OLS OLS SFE IV IV, SFE

Class size/10 0.008 20.012 20.015 20.035 20.080∗

(0.013) (0.010) (0.012) (0.021) (0.035)GPA ninth grade (exam marks) 0.660∗∗∗ 0.654∗∗∗ 0.660∗∗∗ 0.653∗∗∗

(0.008) (0.008) (0.008) (0.008)GPA ninth grade (year marks) 0.232∗∗∗ 0.250∗∗∗ 0.233∗∗∗ 0.251∗∗∗

(0.008) (0.007) (0.008) (0.007)R2 0.086 0.637 0.657 0.637 0.633F-test, weak instrument 119.474 56.622Schools 471 471 471 471 471Observations 29,184 29,184 29,184 29,184 29,184

Notes: Robust standard errors clustered at schools in parentheses. In (5) R2 is from the regression using thewithin-school transformed variables. All regressions include the control variables of Table 3.+p , 0.10.∗p , 0.05.∗∗p , 0.01.∗∗∗p , 0.001.

Education Economics 423

Dow

nloa

ded

by [

126.

249.

179.

227]

at 1

8:13

28

Apr

il 20

15


学級規模と動機づけ等


非認知的能力 [Dee and West, 2011]

問題低所得層やマイノリティの児童の学力に正の影響 (Krueger,2003)小規模学級はコストに見合うのか [Hanushek, 2006]学力ではなく「非認知的能力」と呼ばれる要因 (動機づけや学習態度等)が個人の将来の成否に影響 (Heckmanが代表的)。

学力を「認知的」と呼び対照させている。

目的学級規模の大小による非認知的能力の違い。非認知的能力とその後の学歴や従事状況との関係。学級規模の大小の成人後の影響。




対象米国 8年生 815校 19,396人。NELS:88という大規模なデータセットを利用。

生徒の学校や指導の実施状況，家庭背景などを込みにした縦断データ。

方法マルチレベルモデリング固定効果 (生徒，教師，学校)制約の置き方で比較。学級規模と非認知的能力との関連を検討するモデル。AFraid to ASK questions in subject classを教科別に検討。非認知的能力と学歴，就業との関連を検討。




結果大規模学級の方が AFraid to ASK questions in subject class,subject NOT USEful for my future, do NOt LooK ForwarD tosubjectが高い，すなわち学習態度や学習観が悪い (Table 2)。大規模学級の方が AFraid to ASK questions in subject classが高い傾向は教科をまたいでいる (Table 4)。AFraid to ASK questions in subject class, subject NOTUSEful for my future, do NOt LooK ForwarDが高いと学歴が低くなる (Table 7)とともにフルタイムの就業や収入が低くなる (Table 8)。学級規模はひいては収入に影響。

なぜここまでリターン (とロス)が問題となるのか？慶應義塾大学教育学特殊 XIV 第 4 講 2015 年 5 月 13 日 22 / 37



TABLE 1 Sample Means, National Education Longitudinal Study of 1988 (N ELS: 88) Base-Year Sample

Standard Variable Description Mean deviation Sample size

NOTUSE Subject not useful for my future 0.0018 0.9953 32,152 NOLKFD Do not look forward to subject 0.0008 0.9945 32,246 AFASK Afraid to ask questions in subject class -0.0061 0.9936 32,197 DISRUPT Student is frequently disruptive 0.1368 0.0019 33,018 INATT Student is consistently inattentive 0.2255 0.0023 32,962 TRYH Frequency of trying hard in subject ( 1 st follow-up) -0.007 1 1 .00 1 2 18,612 STEST Test score in subject 0.0219 0.9976 32,646 CLSSIZE Class size 24.5067 5.8675 33,162 OTHRACE Teacher of opposite race/ethnicity 0.3172 0.0025 33,802 OTHSEX Teacher of opposite gender 0.5028 0.0025 33,802 SCERTIFD Teacher certified by state in subject 0.8838 0.0017 33,802 PCTLEP % classmates with limited English proficiency 0.0141 0.0718 31,362 SUBJECT1 English 0.2576 0.0024 33,802 SUBJECT2 History/social studies class 0.2424 0.0023 33,802 SUBJECT3 Mathematics class 0.2568 0.0024 33,802 SUBJECT4 Science class 0.2432 0.0023 33,802

individual students are based on binary indica- tors for whether they viewed a particular student as frequently disruptive or consistently inattentive (DISRUPT and INATT). The test score measure (STEST) is the cognitive assessment based on the subject for which a teacher was sampled and is standardized by subject (Table 1).

To examine the persistence of any effects of smaller classes, we also use a subject-specific, non-cognitive outcome reported by the subset of students participating in the 1990 follow-up survey (when most were in 10th grade). The student survey administered in 1990 did not include the same battery of questions as the base-year survey. However, it did include a closely related measure of student effort. Specifically, with respect to each of four academic subjects, partici- pants in the first follow-up survey were asked, "How often do you try as hard as you can?" We numbered the five possible responses (which ranged from never to almost every day) 1 to 5 and standardized them separately within each subject to create the variable TRYH. These paired- subject data are available for more than 9,000 base-year students.10

These diverse NELS variables reflecting effort, motivation, self-confidence, and self- control clearly correspond to the broad array of work habits and behavioral traits commonly

28

identified as non-cognitive skills (e.g., ter Weel, 2008). In fact, some of the same data and variables have been used in recent non-cognitive studies (e.g., Segal, 2008a). Recent studies in the parallel literature on school engagement have also used some of the same NELS variables and data (e.g., Glanville & Wildhagen, 2007). It is not surprising that we find that the variables we use tend to cluster into the behavioral engagement (i.e., DISRUPT and INATT) and the psy- chological engagement (i.e., NOTLF, NOTUSE, AFASK) constructs identified in this literature.11

Table 1 reports the means and standard devia- tions for these and other variables for which variation might exist after conditioning on student and teacher fixed effects. Other variables identify whether the student and teacher share the same race or gender, whether the teacher has state certification in the given subject, and the share of a student's classmates who have limited English proficiency. The base-year sample from NELS:88 includes 19,396 students from public schools. However, this sample is limited to 33,802 student-by-subject observations because two teacher questionnaires are available for only 16,901 of these students. More than half of the students for whom two teacher questionnaires are unavailable are also missing data on test scores. Students missing two teacher sur- veys are also more likely to be minorities and,

This content downloaded from 131.113.213.130 on Wed, 29 Apr 2015 01:04:16 UTCAll use subject to JSTOR Terms and Conditions




32

3 5

i ! •g

! I i I о а «M

&

"S 1 ? t I I ■è

а

"s Sì

1 §

1 sì

ЧО 00

ЧО CN CN

^

^ VO M

^ СП »Л

цц

^ ГП

(N

(N

fO

Г)

+_,

ö ö о ö ö m

g

Ф Ф

en cÑ

" Ф ^

ж

/-^ со

со со

«- <»- i

й ^

2 *п

о о о о о > I

« w

О О

О О

О с ¿

'о о d о о о

$ &

^ г? 2 о о

"S

§!§?§ I 'S

о оо тг >л m

vo 73

Г4- СП СО О

*- < OS

G

^Tj-fôocomON

-^ ^

СО CN CN СО СО CN

i2 ООООООЧО

^ ОО

о ^

^^ ^^

^ _^ ^_

^ ^_^ »Г)

tí ^ ^tC

OCNCN'ít^H

Tr4 ^^

^^ ^

_^ ^_

^ ^_^

î ^-N

m

m

m

гн

ri ri

гн -~з

^-N

Tř ooooooi

< w

oooooo»o

c¿ §3

dddddd^ g

.S | tbt^^Sí:222

I § .S

(Nîn

gSo §

S ОООЯЯР

ä P. g

ddd?OO

P. Ö

T3 g

votJ-cocN

^-hoo

¿> S

ЧО Ю

CM

"- 1 OO (N

tí

Uh

^ ГО

^û h- (N

^t

O'

g îu

"^COCNCNCOCOCN

gH

4? dddddd^H

^

£

cÑT^^^coô4^

ьо / -

n

CO CO CO '- it-

i CN 'Г1

ö CO

OOOOOOl

'С

^^ ppppoooo

ü dododoÇ

: 2

sls§§§ I

ЧО CM

Tf m

On Г-

Й

ЧО Tf

CN O

CN со

^

Appoppo

*S dddddd^H

'g

a §§§§g§<¿

e

ssggs! ^

° ° ° • ° s

s ¿¿¿?¿?

i Ö

00 On C

N С

О O

S W4

.2 00

Tt i- ( CN C

N V

O

a> 'L

oo vo'û гн гн o'

25 Oí

oo qooH

H(s

гн гн g

d d d d d d «

vo *o t*** ô t - os

7 is

^ 000000e?

&■

w

pppppp^)- 00

§ g ! § g s r

.

S v

1 l

ïi««S I ! I

& siiaggr uliîilf

til

This content dow

nloaded from 131.113.213.130 on W

ed, 29 Apr 2015 01:04:16 U

TC

All use subject to JST

OR

Term

s and Conditions



非認知的能力 [Dee and West, 2011]TABLE 4 National Education Longitudinal Study of 1988 (NELS:88): Estimated Effects of Class Size on AFASK by Academic Subject

Change in dependent variable

Math AFASK Reading AFASK History AFASK Independent Baseline replaced by Science AFASK replaced by replaced by variable measures science replaced by math history reading

Class size 0.0153* (0.0051) 0.0024 (0.0044) 0.0145* (0.0051) 0.0145* (0.0051) 0.0109f (0.0051) in math

Class size 0.0083* (0.0048) 0.0082* (0.0048) 0.0064 (0.0053) 0.0045 (0.0051) 0.0104* (0.0053) in science

Class size 0.0201* (0.0053) 0.0171* (0.0050) 0.0170* (0.0054) 0.0104* (0.0055) 0.0103* (0.0053) in English

Class size 0.0115f (0.0052) 0.0124f (0.0051) 0.0069 (0.0060) 0.0111Ť (0.0052) 0.0040 (0.0051) in history

p value 0.3669 0.0920 0.3968 0.5666 0.1998 № Pa/ =

ßs = ß£ = Ря) Note. Standard errors, adjusted for school-level clustering, are reported in parentheses. All models include gender-specific subject fixed effects and student and teacher fixed effects. * Statistically significant at the 10% level. Statistically significant at the 5% level. Statistically significant at the 1% level.

eighth-grade class size on engagement persist over time or whether they simply reflect the interaction between classroom environments and fixed student traits. It is worth noting that even tran- sient effects on school engagement could have policy relevance, to the extent that our measures are, in fact, instrumental to subsequent academic success. However, the interpretation of these effects on school engagement as a form of "skill" development would clearly be strengthened if the subject-specific effects were to persist over time.

It is fortunate that the subject-specific questions about the frequency of trying hard (TRYH), which were asked of students participating in the first follow-up study, allow us to address this question. Table 5 reports the key results from specifications that estimate the effect of subject-specific class sizes in eighth grade on these longer term measures. The basic FD speci- fication suggests that a smaller class size in an academic subject during eighth grade implies a statistically significant increase in effort in that subject 2 years later. The effect size implied by this point estimate (.032) is smaller than the effect size for contemporaneous grade 8 measures. In models that introduce teacher fixed effects as well as other controls (e.g., OTHRACE, OTHSEX, SCERTIFD, and PCTLEP), this effect is some- what larger but becomes weakly significant because

However, these estimated effects are all relatively small and, in 3 of the 4 cases, statistically insignificant. The only exception is that a smaller English class implies a relatively small but weakly significant increase in school engagement with history. Overall, these results imply that the non- cognitive benefits of smaller classes are largely concentrated in the particular subject taught with a smaller class size.

In addition to providing evidence on the nature of the educational production function, these results also provide a useful ad hoc falsification exercise for the basic FD identification strategy employed in this study. In particular, if the other- subject effects of class size had been compara- tively large, it would have underscored concerns about whether students with a propensity for good non-cognitive outcomes in a broad subject area (e.g., math and science) are more likely to be assigned to smaller classes in those subjects. Instead, the results in Table 4 suggest that, for all four academic subjects, the spillover effects to related subjects are relatively small. Like the prior robustness checks, this pattern implies that non-random, within-student selection into smaller classes is not confounding our results.

Although reassuring with respect to the inter- nal validity of our main results, the subject- specific nature of the class-size effects also raises the question of whether the apparent effects of

34

This content downloaded from 131.113.213.130 on Wed, 29 Apr 2015 01:04:16 UTCAll use subject to JSTOR Terms and Conditions



非認知的能力 [Dee and West, 2011]j I t I 1 î | 1 i 1 §

I S I s; •S 1 I s: о

1 î i I I s? w

1 S ►

J S

9 I

^О'

W O

' t

rt

^ ©

ddood m

s~/ ¿Г ¿Г ¿Г ¿Г +Г +Z

x x

x s~/ h

a О* «о «л m

СО Ч

О Tf

Tf ЧО Ч

О

W

Ю

W

Ю

^ Ф

q -h

q -

q -h

о

о о

о о

о

S írT 0s ř^ o4 G

1 řT 4)

О

О

О

О

О

О

О

W

t^^^H

VÌ

О

ОО О

13

Tf О

^ ОО тГ

(> й

CN Г-

ГО Ч

О *-н Ч

О

*5 q

^н о »-и о

"н ¿g

ср о о о о о

Г-~ ЧО

«О

8 8

8 Ö

Ö

Ö

- Г I Г I Г I

х х

Os

»П

Wì

СП

V)

-*t q

q q

i i

i ^

S' ^ Ss

^

Г~- ЧО t -

ЧО I -

I - •

гп4 ^

^ ^^^^

X

X

X

^-s ^н о

Г- CN O

s CO

Tt »л Tt ^ fN

m

m o

^t o <N

о q

<N | q

<n q

<n

Ö Ö

Ö Ö

Ö Ö

g ^

dòòòdòH

jg ^ CN

^ ^

^ ^

^ ^

«O

XX

,~ 5)

w

TtOOOONONÒ

Ž

<L> О

Г- Tl" Tf

O

ЧО

О

О

fN О

(S

О

fS 'S

U

oöoöod я

t^ о"

G4

§ t^

г** чо

55 о

о о

S q

q q

БЬ

о о

о 'S

^ Г

I Г I Г

I хх

è Г-

"3" 00

О

TJ-

Г^ «О

-о

q q

q .2

9 9

9 «i S

-2 III

S и

гТ^оо'оо'4^^4 ^*^

ÖÖÖÖÖÖ

ö un

m

^

^ ^^^

^ XXX

'~ «

^^oooior-nst^

^"5

<Э <O

> СЭ C

ï g

C>

D, -g

oöoöoö S g

^ § s

*"O

^^ ^^

*^ ^^

^^ ^í"

C3 ^

i? öööööör-

1*2 o

řT ГГГГГГ°Я

xx -II

j sssili

|l ад

о о

о о

о о

*о тз w

^

^ ^

-g ia Г-

Tf ЧО

и чо

«о u->

rf fe è:

8. 8

8 ^s

000 a я

S ST I &

I » I ^^

Да

g & 8

» g fâ

S S

g | g I

? ?

? SI

g s

I 11

li-

II áegísgeli

I null

11

38

This content dow


ed, 29 Apr 2015 01:04:16 U

TC


OR

Term

s and Conditions



非認知的能力 [Dee and West, 2011]I I £

1 I f I I Ì I *- » Q

.О

I j Î §

1 I Ì I ¿

1 00

"*3 «

Ì 3 i

*- • On ^t

On 00

u-> «П

CO CO CO */-) Tt

S ©

о о

© ©

SSpsss o

d °

о о

о

гГ Ç4 «лГ

оо4 оо4 <л м

m es ю

м ©

© о

© q

©

^^ ©

^¿©^©

^©^©

^

I I § I § 2 Š

g ^©

°©©

©с^

OS

/-N -v ^-v ^-^

^-n /-^ч

On

^" On O

n O

n C^ O

n 1~h

On

fO O

n ©

On (N

On

w 2 ^ is r^ ч о

о °

© ©

©

/~' /"-N

/^V

m

oo

i- i ^4

en

©

ťN

o

p ©

©

iž

©

©

©

s©

£ ř1^ ig1

* и ï

s ¡ °

i 1

? °

? 1 о

írT ř^1 í^4 00s 00s í^4 ît3

On 00 O

n 00 0O

OO

ft ^5

^5 ^5

^5 ^5

^5 ,2<

О

О

О

О

О

О

«J

©^¿©

^©^©

^©^

^> g

^VÍT^ÍTr

х х

х х

"g ГЛ

г* оо с^ со

'с Я «о ©

*о ©

»г>

.2 pSgS§S

^S odO

©°©

gH

3

f^fřSř li

s: §§8888

S.* S! ^s šššššš

-s »

! ttltlU

il

&1

i-hOOOOOncO

On©

OÛ-ÏÏ*

Д

00 ЧО t***1

4D Г4^ О

д

*v3

i ,-v ŠŠŠŠŠŠ

I 'S я

t^^oS^©1^

«'S £

ssss§s I«

^ f^

co4 м

^ 00

Г- Г-

»-. ^

g g

§ £2

©

©

©

'S а

^ШГ4

i i

I хх

'S*3 ^

r^ I ©

I оо

' Д

'S -н

vo ЧО

*Ö

^

©

©

©

«я« § i

i i

¡i

tlI^HbliilU

ìtlI IÍ

39

This content dow


ed, 29 Apr 2015 01:04:16 U

TC


OR

Term

s and Conditions



精神衛生 [Jakobsson et al., 2013]

目的学級規模の大小と中学生 (相当：9年生)のメンタルヘルスとの関係。

対象瑞典ヴェルムランド群の 40校 (公立 38，私立 2)。9年生 2,755人。

方法プロビットモデル

説明変数が 2値の場合の予測モデル分断回帰デザイン。




結果集中力，不安，悲嘆，目眩，頭痛，胃痛と学級規模は関係なし(Table5, 6)。

論点� �分断回帰デザインは何のための方法だったか？Table 5と 6で符号の逆転が起こっているのをどう解釈するか？教師の目が生徒に行き届きやすいことの難点は？� �




Table 5. Class size and mental health/well-being: instrumental variable regressions.

Concentrationproblems Headaches Stomachaches Worrying/anxiety Sadness Dizziness

Class size 20.006 (0.016) 0.028∗ (0.016) 0.006 (0.012) 0.022 (0.015) 20.003 (0.014) 0.027∗∗ (0.012)

Enrollment 0.000 (0.000) 20.000 (0.000) 20.000 (0.000) 0.000 (0.000) 20.000 (0.000) 20.000 (0.000)

Girl 0.074∗∗∗ (0.017) 0.171∗∗∗ (0.016) 0.143∗∗∗ (0.013) 0.150∗∗∗ (0.015) 0.240∗∗∗ (0.015) 0.067∗∗∗ (0.013)

Single parent 0.081∗∗∗ (0.019) 0.077∗∗∗ (0.018) 0.048∗∗∗ (0.014) 0.013 (0.017) 0.081∗∗∗ (0.016) 0.035∗∗ (0.015)

First-generationimmigrants

20.068 (0.043) 20.073∗ (0.039) 20.062∗ (0.034) 20.074∗ (0.039) 20.0002 (0.042) 20.046 (0.034)

Second-generationimmigrants

0.028 (0.029) 0.005 (0.028) 0.029 (0.024) 0.043 (0.027) 0.062∗∗ (0.027) 0.044∗ (0.025)

Nonworking mother 0.041∗ (0.025) 0.044∗ (0.024) 0.042∗∗ (0.021) 0.059∗∗ (0.023) 0.016 (0.022) 0.016 (0.020)

Nonworking father 0.061∗ (0.034) 0.041 (0.033) 0.045 (0.027) 0.039 (0.031) 0.032 (0.030) 0.014 (0.027)

Constant 0.327 (0.363) 20.539 (0.360) 20.093 (0.279) 20.432 (0.360) 0.133 (0.331) 20.554∗ (0.284)

N 2755 2755 2755 2755 2755 2755

Notes: Robust standard errors in parentheses. All estimations use the predicted class size as an instrument for actual class size. The AIC suggests that not including higher orderpolynomials of enrollment is preferred; the findings, however, do not change if higher order polynomials are included. Note that models are based on treating outcomevariables as continuous, but as noted in Note 10, results are qualitatively similar if treating outcome variables as binary.∗p , .1.∗∗p , .05.∗∗∗p , .01.

Education

Econom

ics259




Table 6. Class size and mental health/well-being: instrumental variable regressions, discontinuity sample (+3).

Concentrationproblems Headaches Stomachaches Worrying/anxiety Sadness Dizziness

Class size 20.025 (0.028) 20.030 (0.030) 20.017 (0.020) 20.010 (0.024) 0.013 (0.024) 20.047∗∗ (0.023)

Enrollment 0.000 (0.001) 0.001∗∗ (0.001) 0.000 (0.000) 0.000 (0.000) 20.000 (0.000) 0.000 (0.000)

Girl 0.128∗∗∗ (0.035) 0.209∗∗∗ (0.034) 0.149∗∗∗ (0.028) 0.145∗∗∗ (0.030) 0.249∗∗∗ (0.031) 0.098∗∗∗ (0.030)

Single parent 0.118∗∗∗ (0.039) 0.064∗ (0.039) 0.079∗∗∗ (0.030) 0.011 (0.032) 0.101∗∗∗ (0.034) 0.063∗ (0.033)

First-generationimmigrants

20.081 (0.069) 20.104 (0.068) 20.044 (0.056) 20.013 (0.072) 20.011 (0.066) 0.043 (0.066)

Second-generationimmigrants

20.055 (0.056) 20.017 (0.054) 20.003 (0.042) 0.059 (0.052) 0.028 (0.047) 20.003 (0.050)

Nonworking mother 0.084 (0.053) 0.154∗∗∗ (0.056) 0.030 (0.041) 0.069 (0.050) 0.058 (0.048) 0.018 (0.048)

Nonworking father 20.008 (0.064) 0.067 (0.071) 20.049 (0.046) 20.003 (0.060) 20.010 (0.059) 0.016 (0.060)

Constant 0.754 (0.642) 0.702 (0.678) 0.451 (0.450) 0.288 (0.548) 20.260 (0.532) 1.167∗∗ (0.514)

N 622 622 622 622 622 622

Notes: Robust standard errors in parentheses. All estimations use the predicted class size as an instrument for actual class size. The AIC suggests that not including higher orderpolynomials of enrollment is preferred; the findings, however, do not change if higher order polynomials are included. Note that models are based on treating outcomevariables as continuous, but as noted in Note 10, results are qualitatively similar if treating outcome variables as binary.∗p , .1.∗∗p , .05.∗∗∗p , .01.

N.

Jakobssonet

al.260


学級規模が児童生徒に与える影響を検討する視点


今回のレビューから

研究上の問題学級規模の効果を推定する上での難しさはどこにあるのか。クラスサイズパズルという現象はどうして起こるのだろうか小規模学級ほど児童生徒の学力に有利ということを示した研究にはどのような特徴が見られるか。

学級規模の効果に関して学級規模は誰の，どの部分に影響を与えるのか。それはなぜなのか。

次回の講義学級規模と児童生徒との関係の間 (学習指導)。



引用文献 I

[Bosworth, 2014] Bosworth, R. (2014).Class size, class composition, and the distribution of studentachievement.Education Economics, 22(2):141–165.

[Bosworth and Caliendo, 2007] Bosworth, R. and Caliendo, F.(2007).Educational production and teacher preferences.Economics of Education Review, 26:487–500.

[Dee and West, 2011] Dee, T. S. and West, M. R. (2011).The non-cognitive returns to class size.Educational Evaluation and Policy Analysis, 33(1):23–46.



引用文献 II

[Hanushek, 2006] Hanushek, E. A. (2006).School resources.In Hanushek, E. A. and Welch, F., editors, Handbook of theEconomics of Education, Volume 2, pages 865–908. Elsevier,Amsterdam.

[Jakobsson et al., 2013] Jakobsson, N., Persson, M., andSvensson, M. (2013).Class-size effects on adolescents’ mental health andwell-being in Swedish schools.Education Economics, 21(3):248–263.



引用文献 III

[Konstantopoulos and Traynor, 2014] Konstantopoulos, S. andTraynor, A. (2014).Class size effects on reading achievement using PIRLS data:Evidence from Greece.Teachers College Record, 116(2):1–29.

[Krassel and Heinesen, 2014] Krassel, K. F. and Heinesen, E.(2014).Class-size effects in secondary school.Education Economics, 22(4):412–426.

[Lazear, 2001] Lazear, P. E. (2001).Educational production.The Quarterly Journal of Economics, 116:777–803.



引用文献 IV

[Word et al., 1990] Word, E., Johnston, J., Bain, H. P., Fulton,B. D., Zaharias, J. B., Achilles, C. M., Lintz, M. N., Folger, J.,and Breda, C. (1990).Student/Teacher Achievement Ratio (STAR) Tennessee’s K-3class size study. Final summary report 1985-1990.Tennessee Department of Education, Nashville, TN.


教育学特殊XIV 第4講

Education

Transcript of 教育学特殊XIV 第4講

教育学特殊XIV　第4講

Transcript of 教育学特殊XIV　第4講