55

A Quantitative Investigation into Student Outcomes from
Learning Assistant Engagement in Economics Class Hours
Masanori Ono
Abstract
This paper employs an econometric method in estimating student outcomes fromlearning assistant (LA) engagement in economics class hours. Many US universities
have already introduced an LA system into undergraduate courses, primarily inscience, technology, engineering, and mathematics (STEM) disciplines. The author
and his colleagues applied the LA model to an economics course at a Japanese
university. We expected that LAs would also help economics students learn more
readily and energize their in-class learning. Thus this paper investigates the effect oflearning assistant support on the students in an introductory macroeconomics
course. While circumventing collinearity problems between explanatory variables,
the ordinary least squares estimation reveals that the LA model in economics fosters
students’ exam performance. Hence this paper provides one piece of evidence
supporting the use of LAs not only in STEM education but also in economics.
Keywords: learning assistant, undergraduate education, economics education,
variance inflation factors (VIFs)

1. Introduction
This paper investigates the learning assistant (LA) support of the students in an
introductory macroeconomics course. Many instructors have recently transformed a
traditional lecture into a new instructional format, such as a flipped classroom or online
homework. LAs are one of the new approaches to undergraduate education. The
Learning Assistant Alliance (2019) defines learning assistants as follows:
A Learning Assistant, or LA, is an undergraduate student who, through the guidanceof course instructors and a special pedagogy course, facilitates discussions amonggroups of students in a variety of classroom settings that encourage student
engagement and responsibility for learning. (p. 1)

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The alliance has developed methods for implementing and assessing the LA model.
In this regard, Otero et al. (2010) demonstrate that the LA model yields learning gains
to students and LAs themselves and enhances LAs’ future career prospects in teaching
and research. Herrera et al. (2018) report that the LA model originated at the Universityof Colorado Boulder and has spread to over 75 institutions. As the use of the model
expands across the US, many universities have already introduced LAs into
undergraduate courses, primarily in science, technology, engineering, and mathematics(STEM) disciplines.
Economics is positioned near the STEM disciplines because it frequently uses
mathematical tools to understand economic phenomena. Students sometimes find it
challenging to understand economics—especially when it comes to the mathematicalinterpretation of human behavior. Therefore, the author and his colleagues expected thatthe engagement of LAs would help students learn more readily and energize their inclass learning. Thus in 2018, we initiated the use of LAs in teaching an introductory
economics course. We have collected data for our new instructions to quantitatively
gauge the extent to which LA support enhances students’ achievement.
In Japan, the Ministry of Education, Culture, Sports, Science, and Technology(2000) argued that universities should provide an educational opportunity for seniors toassist in undergraduate education. This report, well known as the Hironaka report, madethe first official statement declaring the importance of undergraduates’ involvement inundergraduate education. As this paper describes, the LA model embodies the report’s
proposition.
The purpose of this paper is to investigate whether the LA model can afford a better
understanding of economics for Japanese undergraduate students. In this way, this paper
investigates two areas that are as yet relatively unexplored. First, the LA model has not
yet spread widely in economics courses around the world, to our knowledge. Second,
Japanese students do not have much experience with LAs in class. By discovering how
LAs work out for them, we can help to meet the contemporary demand forundergraduate involvement in education.
In the remainder of this paper, we first introduce a literature review. Next, we
describe our methodology concerning the data that we use and a theoreticalspecification. We then report and discuss our estimation results. Based on our evidence,
we propose recommendations. Finally, we present conclusions derived from this study.

A Quantitative Investigation into Student Outcomes from Learning Assistant Engagement in Economics Class Hours Masanori Ono

57

2. Literature Review
The past two decades have seen renewed interest in economics education at theundergraduate level. Mixon and Upadhyaya (2008) report that more papers about
economics education have appeared in print since 2000 than before. A traditional outlet
in this field had been the Journal of Economic Education. However, two new journals
were launched in this field in 2002 and 2003, respectively: the Journal of Economics
and Finance Education and the International Review of Economics Education. Generalinterest journals, such as the American Economic Review, also publish economics
education research.
Table 1
Previous Studies on Economics Education
Attainment
Measurez-scoreChevalier,
Dolton, andLührmann(2018)
Pozo and Stull score quantile(2006)

Method A

Method B

Conclusion

Institution

Countryquizzes in
grade

quizzesA works better University of UK
recommended than B
Londonmathlearning in
graderankingreportedmath learning A works better WesternUSA
recommended than B
Michigan U

final examranking not
MIxed results A Japanese Japan
Kajitani,
reported
university
Morimoto, and score
Suzuki (2020)
blendedtraditional
A works better Carlton
USA
Swoboda and Test oflearning
than B
CollegeFeiler (2016) Understanding learningin College
Economics(TUCE)
onlinetraditional
No clearSaint Mary’s USA
Lee, Courtney, TUCE
homework homework
differenceCollege ofand Balassi
California(2010)
final exam
flippedtraditional
A works better U of North USA
Balaban,classroom classroom
than B
Carolina atGilleskie, and score
Chapel Hill
Tran (2016)
more-required B works better Universidad Colombia
Arteaga (2018) wage
lessold coursework than A
de LosrequiredAndes
newcoursework

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Table 1 summarizes several papers about economics education. Chevalier et al.
(2018) in the UK and Pozo and Stull (2006) in the US investigate learning outcomes
associated with students’ motivation. Both papers suggest that instructors shouldincorporate students’ understanding of supplementary materials into their grading
considerations rather than simply recommend them as an optional study. They also
report that lower-performing students benefit more from the built-in grading policy.
Related to students’ incentive to study, Kajitani et al. (2020) examine the effect on
students’ final scores of disclosing their mid-term exam ranking. They report thatstudents’ knowing their ranking may motivate them to study for the final exam—
depending on how well they performed in the mid-term exam. For students with a lowscore in the mid-term exam, the disclosure led to a higher final exam score. However,for students with a high score on the mid-term exam, the disclosure lowered the final
exam score.
Swoboda and Feiler (2016) and Balaban et al. (2016) compare new learning styles
such as blended learning and the flipped classroom, respectively, to traditional styles at
US universities. Both argue that new approaches work better for students than traditional
learning. On the other hand, the work of Lee et al. (2010) demonstrates no apparent
difference in exam scores between online homework and traditional assignments. Apartfrom class teaching, Arteaga (2018) reveals that a curriculum transition toward lessrequired coursework at a Colombian university reduced graduates’ earnings.
In sum, researchers in various countries have shed light on different aspects ofeconomics education. Roughly speaking, new approaches (i.e., Method A in Table 1)
help students understand economics better than traditional ones (i.e., Method B in Table1). However, new practices do not always prove superior.
Evidence-based suggestions valuing LAs’ roles in universities arise from research in
the STEM field (Herrera et al., 2018; Knight et al., 2015; Otero et al., 2010; Thompson
et al., 2020). In general, we are not sure whether support staff in schools help students
understand the content they must learn. For example, in the UK, Blatchford et al. (2009)
collect data from elementary and secondary schools and conclude that the involvementof support staff makes a slight improvement in pupils’ learning. On the other hand, weare not familiar with how LAs influence students in teaching economics. However, we
expect that LAs will have an indispensable role in economics teaching because LAs are
increasingly common in teaching the STEM disciplines. Hence it is worth examining

A Quantitative Investigation into Student Outcomes from Learning Assistant Engagement in Economics Class Hours Masanori Ono59

how they work in economics instruction.

3. Methodology
Data
We studied an introductory macroeconomics course taught by the author at Seikei
University in Japan during the 2018 fall semester. About 7500 undergraduates attend theprivate university, located in a suburb of Tokyo. The macroeconomics course wascompulsory for f irst-year economics students, with limited courses accepted ascompensation for a failing grade. We introduced the LA model into this course, andthree LAs attended the class regularly. Following procedures recommended by Otero etal. (2010) and the 2018 version of the Learning Assistant Alliance (2019), we held aweekly content preparation meeting between LAs and faculty members, where wereflected on the past week, examined the following week’s materials, and proposed aquestion-solving time appropriate to students. Before this semester, LAs took a 10-weekpedagogy course as one of the LA model’s significant activities.
Table 2 illustrates the timeline of the course during the semester. In Weeks 1 and 2,
123 out of 128 students consented to the use of their data for this research. From Week
1 to Week 7, we performed quizzes using traditional answer sheets. However, from
Week 8 to Week 14 we conducted quizzes using answer sheets with an LA-support
format. Although LAs were ready to support students even when taking quizzes using
traditional answer sheets, students rarely asked the LAs for help. To encourage them touse LA support, the instructor revised the answer sheet to an LA-support format.
Therefore, we can regard the first seven weeks as a traditional class because of
traditional answer sheets and the second seven weeks as an LA-supported class.
Whether the answer sheet was traditional or LA-supported, the quiz took place for about
15 minutes in the middle of the 90-minute class. Before and after the quiz, the lecture
went on.

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Table 2Timeline of the Course
Week
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Schedule
collecting consents
collecting consents

Traditional quiz

LA confirmation quiz

test 1

test 2

test 3

test 4
final exam

Figure 1 shows a sample of the revised sheet. The first question (Q1) concerns abasic concept. Those that follow (Q2 and Q3) include questions for which the student’s
understanding of the first (Q1’s) answer serves as a scaffold. The instructor inserted aspace for the LA to confirm the student’s answer to the first question. Students may raise
their hands if they want an LA to check their answers. When the LA confirms that theanswer is correct, he or she signs in the space provided. In other words, no signaturemeans that the student either had a wrong answer or did not request the LA’s help. In
this way, we could track which students used LAs and which did not. These trackabledata allow us to distinguish the learning effect between LA users and non-LA users
within the LA-supported class.
A researcher may want to compare classes with and without LAs as treatment andcontrol groups, respectively. In this case, the researcher would compare the averageclass performance with that of the counterpart. However, for fair and equitable
treatment, it is problematic to assign LAs to one class but not to another class.
Therefore, this paper compares the weeks of using traditional answer sheets with thoseof using the LA-support format. Also, it traces individual performance by using

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61

individual records of how many times the student used LA support. However, as
described in the following subsection, we must avoid collinearity problems that could
arise from mutually dependent data to make up for not dividing the students in advanceinto treatment and control groups.

Figure 1
Sample of the Answer Sheet with an LA-Support Format

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At the end of the semester, we had a 50-minute final exam covering all content
presented from the first week to the concluding week. We also had four short-time(about 15-minute) tests (tests 1, 2, 3, and 4) in the period of the course. A short-time test
covered the contents treated during the preceding few weeks. Hence, we regard the final
exam as an appropriate output measure. In class, students absent from the final exam
have zero points or a make-up exam. In this study, however, we designated those absent
students’ scores as “data unavailable.” Therefore, observations reflect the number ofstudents who attended the final exam.

Theoretical Methodology
Here we specify our basic equation based on the education production function, in
which output is student content understanding and inputs are their ability, efforts, andexogenous characteristics (Balaban et al., 2016; Hanushek, 2020).
For student i,
final test’s z-score i=β1 the number of traditional quiz submissions i+β2 the number of LA-format quiz submissions i+β3 the number of LA confirmations i+ ∑ j > 3β j student i’s individual factor j+ constant + residual.

(1)

We employ ordinary least squares regression to estimate the parameters of Equation 1.
As stated in the preceding subsection, the final test covered all contents taught in theentire semester. Therefore, the final test score represents the output from inputs for anindividual student. For student i, we converted the test score to a z-score, following
Chevalier et al. (2018). Thus student i’s z-score is calculated by subtracting theexamination average from the student’s raw score and dividing the difference by thestandard deviation.
For student i, submitting traditional quizzes is equal to attending the class when
conducting the traditional quiz. Similarly, for student i, submitting an LA-format quiz isequal to attending the class when conducting LA-format quizzes. The course attendance
was almost compulsory, as described in the preceding subsection. Therefore, absencefrom the class is mainly caused by exogenous factors such as illness or casual incidents,
although the lack of diligent effort accounts for some students’ absences. Hence, we

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63

presume that the number of quiz submissions depends on exogenous factors. We canconclude that LA-format quizzes function better than traditional quizzes if the estimatesof β1 and β2 are close to zero and greater than zero, respectively. The first researchquestion is whether the estimates of β1 and β2 satisfy the condition
The number of LA confirmations indicates student i’s effort realization madepossible by the LA model. We can conclude that LA involvement enhances student
performance if the estimate of β3 is greater than zero. The second research question iswhether the estimate of β3 comes out as expected. The quiz-solving condition allows thestudents who received LA confirmations to help neighboring students. In this sense, β3represents the direct effect of the LA on a student leading a small study group on the
spot. On the other hand, β2 demonstrates the indirect effect of LA confirmations on
those who benefited from LA support but did not directly obtain LA support.
Regarding exogenous characteristics, student i’s individual factors include
hometown location, entrance examination type, gender, birth year, and club affiliation.
We obtained approval to use the data from the Seikei University’s Faculty Development
Committee for individual factors. Table 3 describes the variables that we use in thispaper and reports the descriptive statistics.
In performing regression analysis, we should note the possibility of collinearity
problems in the estimation. If a correlation between explanatory variables is sufficiently
strong, the coefficient standard errors become so needlessly large as to make significant
test results inconclusive (Belsley et al., 1980). Hence we test for the collinearity between
one explanatory variable and others, using variance inflation factors (VIFs). A highvalue of VIF indicates that the explanatory variable for the estimated coefficient
excessively increases its standard errors due to collinearity with other explanatory
variables. As a theoretical argument, O’Brien (2007) suggests that researchers eliminate
one or more explanatory variables from the regression when the VIF is greater than 10.
In investigating education data, Entrich (2021) uses the VIF of 10 as the threshold point
above which the collinearity problem occurs. Therefore we adopt their criterion (i.e., the
VIF of 10) for diagnosing collinearity between explanatory variables.

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Table 3
Descriptive Statistics
Variable
Final
Number of traditional quiz
submissions
Number of LA-format quiz
submissions
Number of LA confirmations
Number of LA confirmations
with perfect answers
Number of LA confirmations
with imperfect answers
Number of no LA
confirmations with perfectanswers
Number of no LA
confirmations with imperfectanswers(Individual factor)
Entrance exam type 1

Entrance exam type 2

Entrance exam type 3

Entrance exam type 4

Entrance exam type 5

Hometown location

Club affiliation
GenderBirth year

Variable description for student i Mean
SD.
Max.
Min.
z-score of final exam
0.0000 1.0045 1.7796 -3.1490
Described under “Variable”
6.1463 1.2590 7.0000 1.0000
Described under “Variable”

5.7398 1.8370 7.0000

0.0000Described under “Variable”
Described under “Variable”

2.8049 1.9019 7.0000
2.6585 1.8145 7.0000

0.00000.0000Described under “Variable”

0.1463 0.4185 2.0000

0.0000Described under “Variable”

2.0081 1.6669 7.0000

0.0000Described under “Variable”

0.9268 1.0494 5.0000

0.0000=1 if the student had passed
entrance exam type 1, =0
otherwise.
=1 if the student had passed
entrance exam type 2, =0
otherwise.
=1 if the student had passed
entrance exam type 3, =0
otherwise.
=1 if the student had passed
entrance exam type 4, =0
otherwise.
=1 if the student had passed
entrance exam type 5, =0
otherwise.
=1 if the student comes fromoutside the area of Tokyo and its
vicinities (i.e., Kanagawa,
Saitama, and Chiba prefectures),=0 otherwise.
The number of clubs to which thestudent belongs.
=1 if the student is a female, =0
otherwise.
=1 if the student is older than 18,=0 otherwise.

0.3496 0.4788 1.0000

0.00000.0488 0.2163 1.0000

0.00000.0325 0.1781 1.0000

0.00000.1301 0.3378 1.0000

0.00000.2520 0.4360 1.0000

0.00000.1545 0.3629 1.0000

0.00001.7642

1.255 6.0000

0.00000.4146 0.4947 1.0000

0.00000.1301 0.3378 1.0000

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65

4. Results and Discussion
Basic Estimations
Results. Table 4 presents ordinary least squares estimates based on Equation 1. Robust
standard errors are in parentheses. P-values are in brackets. Values in angle brackets
report the VIFs for the estimated coefficients. Column 1 presents the estimation of theparameters of Equation 1 that excludes the number of LA confirmations from theexplanatory variables. The estimated coefficient of β1 is 0.1423, with a p-value of0.1188. The effect of traditional quizzes on the final exam is statistically insignificant
from zero at the 10% level. Because the p-value is slightly greater than 0.1, we cannot
say that using the traditional answer sheet does not shape learning outcomes.

Table 4
Basic Estimations

Coef.

Column number
Estimation name
Explanatory variables＼Dependent variable

β1

Number of traditional quiz submissions

β2

Number of LA-format quiz submissions

β3

Number of LA confirmations

R-squared
Obs.

(
[
<
(
[
<

1
Basic 1
final
0.14230.09050.11881.40200.28390.07700.0004
1.3371

)
]
>

(
[
<

)
]
>

(
[
<
(
[
<

0.3617111

2
Basic 2
final
0.10550.08960.24191.4052
0.23750.08170.00451.45660.08700.04110.03681.35920.3819
111

)
]
>
)
]
>
)
]
>

Note: R
 obust standard errors are in parentheses. P-values are in brackets. Variance inflation
factors (VIFs) are in angle brackets. The regressions control for constant and individual
factors: Hometown location, entrance examination type, gender, birth year, and club
affiliation.

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On the other hand, the estimated coefficient of β2 is 0.2839, with a p-value of
0.0004. The effect of the LA-format quiz on the final exam is statistically significantly
different from zero at 1%. Both estimates are positive, but the p-value for β2 is much
lower than that for β1. The LA-format quiz makes a greater contribution to students’
understanding than the traditional quiz does. In Column 1, VIFs for β1 and β2 range
from 1.0 to 2.0, far below 10.0. Thus we can conclude that there is no collinearity
problem, and the corresponding standard errors do not needlessly inflate.
Column 2 reports the estimation that includes the number of LA confirmations in
the explanatory variables. The estimated coefficient of β1 is 0.1055, with a p-value of0.2419. On the other hand, the estimated coefficient of β2 is 0.2375, with a p-value of
0.0045. As in Column 1, both estimates are positive, but the p-value for β2 is much
lower than that for β1. The estimated coefficient of β3 is 0.0870, with a p-value of
0.0368. The effect of LA confirmations on the final exam is statistically significant fromzero at the 5% level. In Column 2, VIFs for β1, β2, and β3 range from 1.0 to 2.0, far
below 10.0. Thus we can conclude that there is no collinearity problem, and thecorresponding standard errors do not needlessly inflate.
Discussion. The positive coefficient on the LA-format quiz (β2>0), with a statisticalsignificance, demonstrates that the use of LA-format quizzes enhances the students’
final score regardless of LA confirmations obtained during the quiz hours. We assigned
the weeks using traditional answer sheets to a traditional class and the weeks using LAsupport format answers sheets to an LA-supported class. Therefore, our finding concurs
with Herrera et al. (2018), who report that LA-supported physics courses produce
higher posttest scores than traditional courses. Having the advantage over the traditionalclass, the LA-supported class either in physics or economics aligns with economics forboth the flipped classroom (Balaban et al., 2016) and blended learning (Swoboda &Feiler, 2016).
Also, students who received LA confirmations achieve an additional increase in the
final exam z-score (β3 > 0). In specific terms, one confirmation raises the student’s final
exam z-score by 0.0870, which means an increase in the final exam row score of 1.54
points out of a possible 100. This estimate comes from multiplying 0.0870 by the final
exam’s standard deviation of 17.65.

A Quantitative Investigation into Student Outcomes from Learning Assistant Engagement in Economics Class Hours Masanori Ono

67

Extended Estimations
Results. Even if the LA confirms the correctness of a student’s answer, the instructor
does not give them a perfect score in some cases, primarily because the student provides
an insufficient explanation for the conclusive answer confirmed by LAs. On the other
hand, many students can obtain the perfectly correct answer without LA confirmations.
In terms of the basic question (Q1 in Figure1), including the conclusive portion that an
LA may confirm, we can classify the answers into four cases: (1) a perfect answer with
LA confirmation, (2) an imperfect answer with LA confirmation, (3) a perfect answer
without LA confirmation, and (4) an imperfect answer without LA confirmation.
Figure 2 illustrates the total number of LA-format answer sheets for each case. The
number of LA confirmations used in Column 2 of Table 4 is the sum of Cases (1) and(2), which amounts to be 345. The sum of Cases (3) and (4) is 361. Therefore thenumber of LA confirmations turns out to be approximately the same as the number of
no LA confirmations. On the other hand, it is not surprising to observe that there areremarkably fewer instances of Case (2) than of Case (1), although Case (2) could
happen sometimes. Most of the students confirmed by LAs could express correct
induction to reach the conclusive answer, which an LA confirms. In the end, thedifference between Cases (3) and (4) is not as significant as that between Cases (1) and(2).

Figure 2
Four Types of LA-Format Answers

Note: The total number of LA-format answer sheets submitted in the semester.

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Table 5 reports the regression in which each case for student i replaces the numberof LA confirmations as the third regressor of Equation 1. Robust standard errors are inparentheses. P-values are in brackets. VIFs are in angle brackets. In each case, β2
captures an average effect of the other three cases. The total effect of the case concernedis the sum of the estimated β2 and β3. In other words, β3 demonstrates the adjustment
effect of the case concerned, by which the total effect differs from the average effect of
the other three cases.
Regarding estimated β 3, the highest is for Case 1, at 0.0916, with statisticalsignificance at the 5% level. The lowest is for Case 4, at –0.2078, with statisticalsignificance from zero at the 10% level. Thus perfect answers with LA confirmations
have a positive adjustment effect on the final z-score. Imperfect answers without LA
confirmations have a negative adjustment effect on the final z-score. For cases 2 and 3,
the estimated coefficient’s sign is positive for Case 2 and negative for Case 3. However,both estimates are statistically insignificant from zero at the 10% level.
We can say that LA confirmed cases (Cases 1 and 2) increase the final exam z-score,and no LA-confirmed cases (Cases 3 and 4) decrease the z-score. These results are inaccord with Column 2 of Table 4. Regarding β1, the estimated coefficient is statisticallyinsignificant at the 10% level in all columns. The estimated β2 is statistically significant
at the 1% level in all columns. These are the same as Table 4.
In all columns of Table 5, VIFs for β1, β2, and β3 range from 1.0 to 2.0, which are far
below 10.0. Thus we can conclude that there is no collinearity problem, and thecorresponding standard errors do not needlessly inflate.

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69

Table 5Extended Estimations

Coef.

β1

β2

β3

Column number
Estimation name
Dependent variable／ Explanatory variables

1
Case 1

2
Case 2

3
Case 3

4
Case 4

final

final

final

final

0.09930.14700.14090.0689( 0.0905 ) ( 0.0925 ) ( 0.0926 ) ( 0.0740 )Number of traditional quiz
submissions[ 0.2750 ] [ 0.1152 ] [ 0.1313 ] [ 0.3537 ]< 1.4192 > < 1.4979 > < 1.4160 > < 1.3993 >0.23850.28010.29080.3117(0.0810)(0.0783)(0.0795)(0.0718)
Number of LA-format quiz
submissions[ 0.0041 ] [ 0.0005 ] [ 0.0004 ] [ 0.0000 ]< 1.4522 > < 1.3431 > < 1.3861 > < 1.3423 >0.0916(
0.0436)
Number of LA confirmations
with perfect answers[ 0.0384 ]< 1.4034 >0.1010( 0.2060 )Number of LA confirmations
with imperfect answers[ 0.6251 ]< 1.4249 >-0.0207( 0.0490 )Number of no LA confirmations
with perfect answers[ 0.6733 ]< 1.4610 >-0.2078(
0.1139 )Number of no LA confirmations
with imperfect answers[ 0.0711 ]< 1.3737 >R-squared
0.38140.36340.36270.4004
Obs.
111
111
111
111

Note: Robust standard errors are in parentheses. P-values are in brackets. Variance inflation
factors (VIFs) are in angle brackets. The regressions control for constant and individual factors:
Hometown location, entrance examination type, gender, birth year, and club affiliation.

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Discussion. Most notably, perfect answers without an LA confirmation do not have astatistically significant adjustment effect. Besides, the insignificant coefficient isnegative. Students who solved the question perfectly without LA support do not match
those who produced perfect answers with LA confirmation. In addition, imperfectanswers without LA confirmation do have a negative adjustment effect in a statisticallysignificant manner. Of the students who gave imperfect answers, those without LA
confirmations trail those with LA confirmations. Even if students gave imperfectanswers, the student’s effort assisted by an LA increases their understanding of
economics content. In a biology course, Knight et al. (2015) report that discussion
quality depends on whether students regularly interact with LAs. In an economics
course, we introduced LA confirmations into quiz-solving time to begin student
interaction with LAs and discovered its enhancement effect on learning outcomes.
Our findings demonstrate that the LAs’ in-class support helps undergraduate
students learn economics. The favorable evidence for learning support differs from
Blatchford et al. (2009), who conclude that support staff make a slight improvement in
pupils’ learning at elementary and secondary schools. The difference in conclusions
may arise from students’ ages or the varied support they receive. Because many
researchers highly value the LA model and many universities have adopted it, likely theLAs’ intervention for support works effectively, at least in undergraduate education(Herrera et al., 2018; Knight et al., 2015; Otero et al., 2010; Thompson et al., 2020).
In our class, LA confirmations did not give any points to the student. Making arequest for LA confirmations was entirely the student’s voluntary choice. If theconfirmation had given the students an extra score, the incentive would have motivatedmore students to request LA confirmations. As suggested by Chevalier et al. (2018) andPozo and Stull (2006), the score-inclusion policy might have been beneficial for students
who would have asked for LA confirmations under such a scheme. However, in thatcase, we would need more LAs and a longer time period to complete LA confirmations.
Class size, the number of LAs, and time length are signif icant factors in thepracticability of such a study. Regarding in-class management, we need more research
to strike a suitable balance among these factors.

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71

5. Recommendations
The LA model provides one of the new approaches for university teaching. In
particular, LAs can become a driving force in transforming traditional lectures into
more interactive class frameworks. This paper demonstrated how to utilize LAs during
the quiz-solving time and revealed that the LA involvement enhances Japanese students’
understanding of economics.
Economics teaching usually takes place in lecture rooms rather than in laboratories.
Herrera et al. (2018) demonstrate that LAs in the laboratory bring more gains tostudents than LAs in lecture settings. Nonetheless, we recommend introducing LAs into
economics and other disciplines beyond STEM. We can modify our traditional lectureformat by using LAs and thereby enhance students’ learning outcomes. To this end, weneed to explore the appropriate use of LAs in economics and other non-STEM
disciplines.
However, to incorporate the LA model, universities must establish a school-wide
framework that comprises pedagogical LA training courses, an LA hiring and allocation
system, instructors participating in the LA model, and other elements. Therefore theimplementation of the LA model relies heavily on high-ranking university officials’
perceptions. We expect LA-related research results to provide university managers witha compelling argument for the benefits of LAs. Beyond investigations on individual
courses, we also need research outcomes about collective changes, like that of Arteaga(2018), who analyzed a curriculum revision.

6. Conclusion
This paper demonstrated that the LA model fostered students’ exam performance
when applying the model to an introductory economics course. LAs influence students
through both direct and indirect contact. LA confirmations help the assisted students
achieve high z-scores in the final exam as a direct contact effect. These students canhelp neighboring students who do not directly obtain LA support. As an indirect contact
effect, having LAs in class leads to students’ outperforming those without LAs in classin terms of final exam scores.
In this research, we observed that the LA model worked for Japanese students.
Beyond the year 2018, we have used LAs in class teaching with more subjects. Goingforward, we share our experience with other universities utilizing the LA model inside

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and outside Japan.
For future research, we need more investigation into other cases. LAs are beneficial
not only through in-class confirmations but also LAs’ enhancement of students’
discussion. As stated in the subsection for data, LAs also participate in content
preparation meetings with instructors. If researchers succeed in quantitatively measuring
those LA activities, the LA model will become more efficient and effective in its
systematic function and more persuasive to university administrators.
(Professor, Faculty of Economics, Seikei University)

Acknowledgments
This research is one of the author’s outcomes during the sabbatical period (Choki
Kenshu) granted by Seikei University. This paper reports an empirical analysis derived
from Seikei University’s education reform and improvement project in 2018, entitled“Class Reform by Utilizing QLAs (Qualified Learning Assistants).” The author would
like to thank the project members and associated QLAs for their cooperation. He
presented a poster version of this research, formerly entitled “A Way to Induce Students
to LAs’ support in Class Hours,” at the 2020 LA Research Symposium and an earlier
version of this paper at the Japanese Economic Association 2021 Spring Meeting. The
author also would like to thank those participants for their valuable comments and thefollowing individuals for their invaluable contributions to this paper: Kristi Hein,
Tommo Inoue, and Shinya Kajitani.

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