ISSN (Print) 0473-453X

Discussion Paper No. 1242

ISSN (Online) 2435-0982

When Learning Together Goes Wrong:
Negative Peer Effects in Online Learning

Shohei Yamamoto
Shuma Iwatani

Revised July 2024
April 2024

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

WHEN LEARNING TOGETHER GOES WRONG

1When Learning Together Goes Wrong: Negative Peer Effects in Online Learning

Shohei Yamamoto1 and Shuma Iwatani2

Abstract
This research examined the impacts of peer perseverance on learning outcomes and investigated how
this influence varies with the partners' skill levels, either higher or similar to the subjects, across two
experiments resembling online learning platforms. Study 1 recruited active English learners, while
Study 2 involved subjects who had not engaged in English studies for more than six months. The
results of both experiments revealed negative rather than positive peer effects. The subjects ceased
studying earlier and displayed lower performance when learning with less perseverant peers,compared to when studying alone. This pattern was observed when paired with similarly skilled peersin Study 1 and with higher-skilled peers in Study 2. Further analysis indicated that the negative peer
effects predominantly originated from subjects with lower levels of motivation. Additionally, it was
shown that social proximity could foster positive effects when peers possess similar skills and higher
perseverance levels.
Keywords: peer effects, perseverance, performance, online learning

1

Graduate School of International Corporate Strategy, Hitotsubashi University, Tokyo, Japan (ORCID:
0000-0002-8136-6842). Correspondence concerning this article should be addressed to Shohei Yamamoto.
Email: syamamoto@ics.hub.hit-u.ac.jp
2
School of Sociology, Kwansei Gakuin University, Hyogo, Japan (ORCID: 0000-0002-1836-0456). Email:iwatani.shuma@kwansei.ac.jp

WHEN LEARNING TOGETHER GOES WRONG

2When Learning Together Goes Wrong: Negative Peer Effects in Online Learning
The advent of the digital age has revolutionized various sectors, with the education industry
being a prominent beneficiary. The emergence and subsequent ubiquity of online learning platforms
have provided an opportunity to transcend geographical boundaries and time constraints, offering a
new dimension to the traditional learning methods. These platforms cater a diverse range oflearning needs, from programming (e.g., Codecademy) and language acquisition (e.g., Duolingo) to
professional certifications courses (e.g., Udemy). The online learning market has been expanding at
an unprecedented rate. As of 2022, the industry was valued at $198.2 billion and is projected toreach a value of $602.0 billion by 2030 (Vantage Market Research, 2023). Duolingo, a leading
language learning platform, reported a 23% increase in monthly active users, rising to 49.2 million
in 2022.
Despite the surging popularity and the seemingly limitless opportunities offered by online
learning platforms such as Coursera, Skillshare, LinkedIn Learning, as well as massive open online
courses, a recurring issue that plagues the sector is the high attrition rate (Eriksson et al., 2017;
Narayanasamy & Elçi, 2020). Indeed, research suggests that student attrition rates on online courses
are generally higher than those in traditional classroom-based courses (Levy, 2007; Tello, 2007).
High dropout rates would undermine the potential of these platforms and pose a significant
challenge to realizing the full potential of online learning. To tackle the problem, some platforms
have introduced social features for learners to engage more. As an example, Duolingo has
implemented several such features including leaderboards and a function to follow other users.1 One
particularly intriguing feature is “Friends Quest,” where users are randomly paired up with one of1

The following function allows users to follow other users. Duolingo claims that learners who follow otherusers on the platform are 5.6 times more likely to finish their course (Zabell, 2023). Leaderboards list the top
engaging users and enable learners to monitor their weekly progress compared to other learners worldwide.
Users can ascend the leaderboard through active engagement with the platform and gauge their standings
relative to the users they follow.

WHEN LEARNING TOGETHER GOES WRONG

3their Duolingo friends each week. They are assigned a challenge such as completing a certain
number of lessons to earn rewards.
As illustrated in the examples above, online learning platforms have been striving to strengthen
the engagement of users by promoting interactions among users. This raises an essential question
regarding the impact of such interactions on individual perseverance and performance. The
following section will provide a comprehensive literature review to explore this issue.
Effects of Peer’s Perseverance
Literature suggests that individuals are influenced by the perseverance of their peers, withpositive effects stemming from high perseverance and negative effects from low perseverance(Battaglini et al., 2017; Buechel et al., 2014; Gerhards & Gravert, 2020; Golsteyn et al., 2021).
Battaglini et al. (2005) developed an economic model to explain such peer effects. The model posits
that witnessing peers successfully overcome self-control challenges boosts individuals' confidence
and belief in their own ability to manage temptations. Conversely, observing peer failures
undermines individuals' belief in their potential for success.
Peer Effects from Similar Peers
The extent of peer effects varies based on peer characteristics, with individuals more likely to be
influenced by peers they resemble (Battaglini et al., 2005; Booij et al., 2017; Carrell et al., 2013;
Hanna et al., 2013). Social learning theory supports this concept, showing that individuals are more
inclined to imitate the behaviors of peers who are similar (e.g., skills, status, or age) (Hanna et al.,2013). Furthermore, Battaglini et al. (2005) posited in their model that individuals acquire insights
about their own perseverance level from similar others.
The stronger influence from similar peers may be attributed to perceived social closeness. Rosazet al. (2016) suggested that similar quitting times for tasks between partners were due to reducedsocial distances among them. Bicchieri et al. (2022) examined the effects of peer influence on

WHEN LEARNING TOGETHER GOES WRONG

4socially desirable and undesirable behaviors, analyzing the role of social proximity. Their findings
indicate that in the absence of social proximity, peers predominantly influenced undesirable
behaviors but not desirable behaviors. However, when social proximity was present, both desirable
and undesirable behaviors were significantly impacted.
This concept of social proximity may also elucidate the findings of Bandiera et al. (2010), whofound that worker productivity was influenced when friends, rather than mere acquaintances, were
working nearby. Additionally, Lomi et al. (2011) demonstrated that students with similar skill levels
are more likely to form friendships and advisory relationships, further reinforcing the link between
similarity and enhanced peer influence.
Peer Effects from High-skilled Peers
Peers with higher skill levels also exert substantial influence. According to social learning theory,
individuals are more inclined to imitate behaviors of peers who are perceived as attractive,
successful, or of high status (Hanna et al., 2013). Individuals are more likely to pay attention to andlearn from those skilled at a task (Bandura, 1986) and copy successful strategies (Mesoudi, 2008;
Mesoudi & O’Brien, 2008). Similarly, Henrich & Gil-White (2001) identified a prestigious bias,
where individuals selectively copy prestigious peers. This phenomenon of copying successful,prestigious, or high-skilled people is known as indirect bias. Adopting the traits of such individuals
is seen as increasing one's own chances of success (Boyd & Richerson, 1985; Mesoudi & O’Brien,2008). Research beyond social comparison theory also showed significant influences from highskilled peers, where being observed by them can increase productivity due to self-image concerns(Boyd & Richerson, 1985; Bursztyn & Jensen, 2017; Mas & Moretti, 2009), while some studies
report negative effects from high-skilled peers among low ability individuals (e.g., Feld & Zölitz,2017).

WHEN LEARNING TOGETHER GOES WRONG

5

Field Studies and Laboratory Experiments
Our literature review indicates that the perseverance of learning partners with similar or higher
skill levels than the individuals significantly impacts the magnitude of peer effects. However, thesestudies are likely subject to biased statistical estimates of peer effects. Feld and Zölitz (2017) notedthat estimates of peer effects vary widely across research possibly because field studies on peer
effects commonly face identification problems (Angrist, 2014; Manski, 1993; Sacerdote, 2014).2
Several studies have rigorously addressed these issues by exogenously assigning individuals to peergroups and using pre-treatment characteristics to measure peer ability (Carrell et al., 2009, 2013;
Duflo et al., 2011; Lyle, 2007). However, despite these sophisticated approaches, the estimates ofpeer effects can still be prone to biases (Angrist, 2014; Feld & Zölitz, 2017).
Conversely, experimental studies in the lab can circumvent this issue by having subjects observe
peers before engaging in separate real-effort tasks that hold little to no intrinsic value (e.g., Beugnot
et al., 2019; Buechel et al., 2014, 2018; Georganas et al., 2015; Gerhards & Gravert, 2020)
However, these real-effort tasks lack realism and generalizability to real-world settings.
Research Questions
Drawing on the previous literature mentioned above, we anticipate that individuals are affectedby the perseverance of their peers. The impact is expected to be strong from high-skilled peers or
peers with similar skills to individuals. However, the differential impact of these skill levels remains
unclear. Therefore, this study seeks to address the following research questions:⚫

How does the perseverance of peers affect both an individual’s own perseverance andperformance?

2

Two major obstacles in identifying peer influences are the selection and reflection issues. The selection issue
arises because peer groups often form naturally, making it challenging to separate the influence of peers fromthe effects of group selection. The reflection issue occurs because it is difficult to differentiate the impact thatpeers have on an individual from the influence that the individual has on their peers when these effects occur
simultaneously.

WHEN LEARNING TOGETHER GOES WRONG⚫

6

Does this effect vary when peers possess higher skills than the individual compared to whenpeers have similar skills?

We used a novel experimental method designed to mirror realistic online learning environmentswith intrinsic value, free from the identification problems typically encountered in field studies.
This approach enables us to identify and estimate unbiased peer effects while ensuring strong
external validity.
Overview of Two Experiments
We recruited Japanese residents and conducted experiments,3 offering subjects opportunities tolearn English vocabulary online which resemble a typical English online learning environment. The
choice of English vocabulary learning as the context was driven by the expectation that subjects
would have some intrinsic value in learning English.
In the experiment, we compared the amount of effort exerted in studying English as well as testscores between the subjects studying alone (single condition) and those studying with a partner who
was pre-recruited in advance of the main study (pair condition). In the pair condition, individuals
were informed about their partners’ level of perseverance, specifically whether the partner
continued learning or not.
This approach enabled us to clearly assess the causal influence of the partners’ perseverance on
objective measures of subjects' perseverance as well as their performance, unlike typical studies that
only measure perseverance or rely on self-reported measures (e.g., Buechel et al., 2014).

3

Japan serves as a suitable context for this research due to its large market for English language education.
The immense interest in English proficiency within the country is evident in the participation rates for the
Eiken (an English proficiency exam inaugurated in 1963) and Test of English for International
Communication (TOEIC). In 2022 alone, over 4 million individuals took the Eiken test, while more than 2
million participated in the TOEIC exam (including the “TOEIC Listening & Reading Test” and “TOEIC
Speaking & Writing Tests”). These figures underscore the significant demand for English language
proficiency in Japan.

WHEN LEARNING TOGETHER GOES WRONG

7

We conducted two studies. In Study 1, we recruited subjects who were currently studying
English. In Study 2, we recruited subjects who had not engaged in studying for more than six
months. The subjects in Study 2 were less skilled and did not enjoy studying compared to thesubjects in Study 1.
We hypothesize that, in such scenarios, the duration of time committed to studying is likely to be
more closely aligned with that of their peers. Specifically, individuals’ perseverance is expected toincrease when paired with high-perseverance peers and decrease when paired with lowperseverance peers, relative to studying alone. The distinction in the strength of influence between
peers with similar versus higher skills presents an intriguing empirical question due to the lack ofclear predictions in existing literature. Additionally, we also anticipate that this influence intensifies
if individuals perceive a sense of social proximity to their peers.
In both experiments, we observed negative peer effects stemming from partners with lower levelsof perseverance. Both the number of words studied and the test scores were lower when partners
possessed low level of perseverance, compared to outcomes when studying independently. The
magnitude of the effect was substantial. The number or words studied dropping by about 11 to 15
words in both studies, where they could study up to 95 words. In terms of test scores (maximum of
30 points), there was a decrease of approximately 1.8 points in Study 1. These findings wereconsistent with prior research indicating negative peer influences across various contexts, including
cooperation, dishonesty, and prosocial behaviors (Bicchieri et al., 2022; Isler & Gächter, 2022;
Rauhut, 2013; Thöni & Gächter, 2015). In contrast, positive peer effects were not observed on
average. This asymmetry in peer effects is also consistent with previous studies (Isler & Gächter,2022; Thöni & Gächter, 2015).
In addition, the source of negative effects varied between the two studies. In Study 1, negative
effects from partners with similar skill levels were observed. In contrast, in Study 2, negative effects

WHEN LEARNING TOGETHER GOES WRONG

8emerged among subjects paired with high-skilled partners, who were less proficient than those in
Study 1. The interpretation of the findings will be discussed in the General Discussion andConclusion section.
In addition to this, we found that perceived social proximity and motivation toward learning canaffect the strength of peer effects. Our analysis shows that social proximity, as measured by the
Inclusion of Other in Self (IOS) scale (Aron et al., 1992), may enhance positive effects among
partners with similar skill levels and higher perseverance. This result is consistent with priorfindings indicating stronger peer effects among individuals with closer social connections (Bandiera
et al., 2010).
Moreover, when segmenting the sample based on the level of intrinsic motivation for learning theEnglish language, the findings demonstrated that less intrinsically motivated subjects experienced
substantial negative peer effects from low perseverance partners, while more motivated subjects did
not. This observation aligns with the self-determination theory, which posits that intrinsicallymotivated individuals rely less on external incentives or pressure, deriving satisfaction and joy fromtheir own endeavors (Ryan & Deci, 2020).
Study 1: Influence of Partners’ Perseverance on English Vocabulary Learning
Study 1 was designed to examine the clear causal influences of learning partners’ perseverance
on individuals’ own perseverance and performance, and how this influence varies with the partners'
skill levels, either being higher or similar to those of the subjects.
Before the main study, we separately conducted a study to recruit potential partners who werematched with the subjects in the main study. We recruited four potential partners, each possessing
different characteristic factors: two distinguished by gender (male and female) and two

WHEN LEARNING TOGETHER GOES WRONG

9distinguished by their level of English skills (high and middle).4 We initially asked them to study
six and then 95 English words. Additionally, we instructed them to send emojis from time to time toother subjects to motivate their learning. We used the data of high-skilled female and middle-skilled
female as potential partners in the main studies.5
MethodSubjects. In Study 1, we recruited 400 subjects through Lancers, Inc. (https://www.lancers.jp).
The criteria for our subjects were as follows; (1) Japanese residents, (2) currently studying English
and (3) having English skills less than the C1 level (less than a TOEIC score of 950). We excluded
26 subjects whose vocabulary level exceeded the scope of this experiment as described in thefollowing section. Consequently, we obtained a demographically diverse sample of 373 subjects intotal (48% female, 𝑀𝑎𝑔𝑒 = 38.8 years, age range: 18-72 years). Of these subjects, 52% are fully
employed, 14% hold part-time positions, 5% are students, and the remaining 29% fall into othercategories. The subjects received a fixed fee of 600 yen. The duration of the experiment varied
depending on how many English words they chose to learn, with the median duration of
approximately 48 minutes.
Experimental Design. Subjects were randomly assigned to one of the five conditions: single or
one of four different pair conditions. The subjects in the single condition did not have any learning
partners, while the subjects in the pair conditions had learning partners (whom we had recruited
before conducting this experiment as mentioned above). Furthermore, the characteristics of partners
were manipulated in the pair condition: English skills (high/similar to the subject), and theirperseverance level regarding study time (quitter/finisher).

4

Test of English for International Communication (TOEIC) score of 955 is considered indicative of as highEnglish skills and middle TOEIC score of 615 is considered indicative of middle English skills.
5
We used female as potential partners in this experiment. This is because subjects felt their peers more
beneficial in learning English when paired with female partners, compared to male partners, according to thequestionnaire in the pretest.

WHEN LEARNING TOGETHER GOES WRONG

10

Procedure. Subjects took part in the five parts of the experiment: pre-learning part, pre-study
questionnaire and pairing announcement part, mandatory learning part, voluntary learning part andtest part (see Table 1 for a summary of the procedure).
Pre-learning Part. In the pre-learning part, subjects were presented with English words andsimply answered whether they knew the words. They chose the option between “I know the word”
and “I did not know the word at all” (they were instructed to choose “I know the word” even if they
had some familiarity with the word). We selected 500 English words for the subjects from
Shimazu’s vocabulary book (Shimazu, 2021). The level of the selected words ranged from B1 to C2levels in Common European Framework of Reference for Languages. We excluded 26 subjects who
claimed to know more than 400 words in this part, as their English proficiency was estimated to behigher than the C1 level.
Pre-Study Questionnaire and Pairing Announcement Part. After the number of words that they
did not know reached 100, they proceeded to the pre-study questionnaire and pairing announcement
part. Here, they completed a demographic survey that inquired about various attributes, including
age, gender, education, marital status, and employment status. The survey also asked about theirprimary motivations for learning English as well as their self-assessed level of English proficiency,
which ranged from beginner (1) to expert (5).6 Additionally, they were instructed to choose theirown avatar like the ones in typical smartphone apps.

6

The options for the reasons for learning English include: for work, for travel, to communicate withinternational people, to help foreigners in need, to gain common sense, because it is cool, to enjoy foreign
movies and other media, for the sake of children, and other reasons. The subjects who selected 5 on the
English proficiency question were not allowed to continue the experiment as per the instructions.

11

WHEN LEARNING TOGETHER GOES WRONG

Table 1
Summary Procedure of the ExperimentSinglePair condition

condition
High-skilledPart

Finisher

1. Pre-learning

Quitter

Similar-skilled
Finisher

Quitter

Answered if they recognized given English words
Answered demographics and other related questions

2. Pre-study
questionnaire

No partnerand pairing

information

announcement

provided.

Received information on partner’s English skills
Informed that partner
answered 3 more words
correctly.

3. Mandatory
learning

Informed that partner
answered the same
number of words
correctly.

Studied 5 out of 100 unfamiliar English words from pre-learning.
Had the option to study English words with the partner.
Had the option

Could stop anytime after the first question.

to study English

Informed that

Informed

4. Voluntary

words alone.

their partnerthat theirlearning

Could stop

continued topartneranytime after the

study allstopped after

first question.

questions (upthe 6thto 95).

question.

5. TestInformed
that theirpartner
continued
to study allquestions(up to 95).

Informed
that theirpartner
stoppedafter the 6thquestion.

Took a vocabulary test on 30 words they identified as unfamiliar during prelearning.

WHEN LEARNING TOGETHER GOES WRONG

12

Immediately after the questions, subjects in the pair conditions were notified that they werepaired with their learning partner. They were also informed of the partner's characteristics,
including gender, a randomly generated name, and English proficiency skills (see Figure 1 for thescreenshot). At this point, we implemented our first manipulation of the partners’ skill. Subjects inthe similar-skilled condition were informed that the partners answered the same number of words
correctly as they did for the 10 questions in the pre-learning part. On the other hand, subjects in thehigh-skilled condition were informed that the partners answered 3 more words correctly than they
did for the 10 questions. We deliberately picked 10 questions in the pre-learning part to avoid
deception problems. For example, we selected 10 questions where the subjects in the high-skilledcondition correctly answered 5 questions, and 10 questions where their partner correctly answered 8
questions. Subjects in the single condition did not receive any information about partners.

Figure 1. The Information of the Partner in the Instructions (Similar-skilled Partner Condition).

Mandatory Learning Part. Subsequently, they proceeded to the mandatory learning part.
Following the methodology in Rosaz et al. (2016), this part was designed to let subjects tofamiliarize the procedure, as well as the level of difficulty and duration of our experimental task.
Here, they must learn five words out of 100 words that they answered “I did not know the word at

WHEN LEARNING TOGETHER GOES WRONG

13all” in the pre-learning part. In this part, each question displayed an English word above five
Japanese translation options (Figure 2).7 Subjects were asked to select one of the options that
correctly translated the given English word within 10 seconds. After that, the feedback was shownand they were asked to memorize it for five seconds. This process was repeated five times, resulting
in the study of five words in this part. There was no break while doing this part. If they did notchoose any option within the time period, they had a second chance to answer the same question
within 10 seconds. Failure to choose an option during this additional opportunity resulted in thetermination of their participation in the study.
Voluntary Learning Part. Upon completing the mandatory learning part, they went to thevoluntary learning part. The procedure here was akin to the mandatory learning part, but withnotable distinctions. Specifically, subjects had the option to cease their learning at any point afteranswering one question. They could study up to 95 additional words. To ensure comprehension ofthese conditions, subjects were required to answer a comprehension question, clarifying theirunderstanding that they could discontinue the learning task at their discretion after answering thefirst question. After they correctly answered this question, they were allowed to start learning.
In this part, the type of information received differed depending on whether subjects were in the
single or paired conditions. Those in the single condition continued their learning alone, as in the
mandatory section. On the other hand, subjects in the paired condition engaged in learning withinformation of their partners. They received emojis from their partner (refer to Figure 2) and had theopportunity to send emojis to their partner. This procedure aimed to foster a sense of learning with
their partners.

7

Multiple-choice questions are a prevalent method for assessing vocabulary proficiency. Presenting answer
choices in the first language, which is Japanese in our study, is efficient (Nation, 2001).

WHEN LEARNING TOGETHER GOES WRONG

14

At this stage, we introduced the second manipulation concerning partners' perseverance level(quitter / finisher). Subjects in the quitter condition were notified that their partner had ceased
studying after six questions. Conversely, those in the finisher condition were informed that thepartner continued to study questions.
Test Part. After the voluntary learning part, subjects transitioned to the test part. This partconsisted of a vocabulary test featuring 30 words that subjects had previously identified as “I did
not know the word at all” in the pre-learning part.
Following the test, subjects in the paired conditions responded to survey questions concerning
their perceptions of their partners. The survey questions encompassed several aspects, including:
How was the amount of words learned by the partner (1: very little – 10: very much)?; How do you
think your partner's level of English vocabulary compares to yours (1: much lower – 7: muchhigher)?; Do you think it is beneficial to have the partner on your learning (0: not beneficial at all –10: very beneficial)? and Inclusion of Other in Self Scale (IOS; Aron et al., 1992) measuring how
close the subjects felt with their partner (1: no overlap – 7: most overlap); How did the amount ofwords learned by the partner’s influence the amount of words learned by you (1: decreased a lot – 7:
increased a lot)? Lastly, we also asked how fun learning English words in this study was (with an
11-point scale).

WHEN LEARNING TOGETHER GOES WRONG

15

Figure 2. The Screenshot of an Example Question in the Voluntary Learning Part (Pair Condition).
In the upper right corner, the learning status of the partner (PTQOI84) and their sent emoji are displayed.
Subjects are required to select the correct Japanese translation of the presented English word from five given
options.

Results
Manipulation Check
The manipulations of the partners' skills and perseverance in this study were successful. Subjects
in the high-skilled condition believed their partner had significantly higher vocabulary levels (M =5.39, SD = 1.24) compared to those in the similar-skilled condition (M = 4.25, SD = 1.02; F (1,301) =76.50, p < .001, η2 = .20). Subjects in the finisher conditions perceived that their partner studied
more (M = 7.02, SD = 2.26) compared to those in the quitter conditions (M = 3.68, SD = 2.42;
F(1,301) = 153.77, p < .001, η2 = .34).
Descriptive Analysis
Table 2 shows the selected characteristics of the subjects for each treatment. The variableknown_words represents the number of words subjects knew in the pre-learning part, eng_level
denotes their self-assessed level of English proficiency, and the dummy variable female indicates if

16

WHEN LEARNING TOGETHER GOES WRONG

subjects are female. The variables between the treatments are not statistically significant indicatingthat the randomization works well (all p-values above 0.1).

Table 2
Descriptive Statistics (Study 1)
Similarquitter

Highquitter

Similarfinisher

Highfinisher

Single

80.00

86.81

90.48

81.01

83.94

(70.82)(70.10)(88.42)(65.17)(59.70)

1.80

1.87

1.89

2.00

1.89

(0.84)(0.88)(0.97)(0.89)(0.84)

38.64

37.96

38.84

40.51

38.27

(10.30)(11.46)(11.47)(10.06)(10.50)female

0.45

0.52

0.51

0.45

0.47

N

74

79

79

71

70

known_words

eng_levelage

Statistictest
F (4,368) =0.27

p-valueF (4,368) =0.48

p = .75

F (4,368) =0.61

p = .66

p = .90

χ (4) = 1.31 p = .86

Notes: Standard errors in parentheses. The variable known_words represents the number of words
subjects knew in the pre-learning part; eng_level denotes their self-assessed level of English
proficiency; and female indicates the proportion of female subjects.
The top of Figure 3 illustrates that the number of studied words in the voluntarily part acrossdifferent conditions. Subjects had their inherent motivation to engage with English vocabulary
learning. They learned 38.31 words on average in the voluntary learning part, which wassignificantly larger than 1 (t (372) = 18.67, p < .001). In addition, the average enjoyment rating forthis experiment was 7.52 (on a scale from 0 to 10), suggesting a high level of enjoyment.

WHEN LEARNING TOGETHER GOES WRONG

17

Figure 3. The Number of Studied Words in the Voluntarily Learning Part (Top) and the Number ofCorrect Words in the Test Part (Bottom) for Each Treatment (Study 1).
Means are represented by three distinct shapes: a circle for Finisher, a triangle for Quitter, and a diamond forSingle condition. Standard error bars are included.

Regression Analysis
Perseverance. We conducted our analysis with three different models employing OLS
regression. In the first model, we assessed the impact of learning partners' perseverance on thenumber of words studied, comparing partners with low and high perseverance against the single
condition baseline (see column 1 at the top of Table 3). The dummy variable quitters is assigned aWHEN LEARNING TOGETHER GOES WRONG

18

value of 1 if the partner’s perseverance is low and 0 otherwise, while finishers is set to 1 for highperseverance. In the second model, we conducted the analysis instead of using the dummy variablesof quitters and finishers, we utilized four treatment dummy variables: high-finisher, high-quitter,
similar-finisher, and similar-quitter, which reflect a combination of the partners' English skill
levels (high or similar) and their perseverance (quitter or finisher) (see column 2). In the thirdmodel, we incorporated demographic variables into the second model. Demographic variables
include age, gender, and college—defined by a dummy variable that takes the value of 1 if thesubject has a college degree—as well as a variable known_words for the number of known words inthe pre-learning part (see column 3).
Next, we repeated the analysis of these three models using Tobit regression to account for theconstraint that subjects could not study fewer than 1 or more than 95 words and could not correctly
answer fewer than 0 or more than 30 words (see columns 4, 5 and 6).
The initial OLS regression model (column 1) shows that the significantly negative coefficient ofquitters indicates that subjects studied approximately 11 words fewer, on average, when paired withthe partners of low perseverance, compared to the single condition (p = .05). The results from the
Tobit model (column 4) indicate a decrease of about 15 words when paired with such partners,
although this effect is nearly significant (p = .08). The other regression models (columns 2, 3, 5 and6) consistently revealed a significantly negative effect from the similar-quitter condition.
Specifically, the Tobit model incorporating demographic variables shows that subjects in thesimilar-quitter condition studied around 27 words fewer on average than those in the single
condition (column 6).
Our analysis also highlights several intriguing observations. First, the coefficient for high-quitterwas not significant, indicating that higher-skilled partners did not negatively affect subjects’
perseverance even when the partners exhibited less perseverance. Second, we found no evidence ofWHEN LEARNING TOGETHER GOES WRONG

19significant positive peer effects from high perseverance partners, indicating that higher
perseverance did not translate into positive peer effects. Additionally, the variable age had positive
impacts while known_words had negative impacts on the number of studied words (columns 3 and5 at the top of Table 3).
Performance. We also conducted the same regression analysis regarding the test scores (thebottom of Table 3). Most of the main findings were similar to the results of former analysis
regarding the number of studied words. The significant negative coefficient for quitters indicatesthat subjects’ test scores decreased by approximately 1.8 words on average when they were pairedwith the partners possessing low perseverance (columns 1 and 4). The subjects in the similar-quittercondition consistently exhibited a negative effect across the four regressions. In this condition, theaverage test score was about 3 words lower compared to the single condition (columns 2, 3, 5 and 6
at the bottom of Table 3).
We replicated the analysis utilizing ANOVA (see Appendix A) and applied both Generalized
Linear Models (GLM) with Poisson and Negative Binomial distributions as a robustness check.8
The overall findings are consistent with those from the main regression analysis.

8

We also utilized zero-truncated models due to the absence of zero counts in our dependent variables. The
results from all GLMs were consistent with those from our main analysis and are available upon request.

20

WHEN LEARNING TOGETHER GOES WRONG

Table 3
Regression Results (Study 1): The Effect of Conditions on Perseverance (Top) and Performance(Bottom)

quitters
finishers(1)
OLS-10.965*(5.522)
1.110(5.539)similar-quitter
high-quitter
similar-finisher
high-finisher

(2)
OLS

(3)
OLS

-17.033**(6.366)-5.281(6.268)0.972(6.268)1.263(6.431)-17.821**(6.236)-4.307(6.146)0.660(6.182)0.329(6.324)0.488**(0.182)-5.661(3.889)-5.143(4.339)-0.087**(0.027)37.209***(8.917)373

agefemalecollegeknown_words
Constant
N

42.357***(4.574)373

42.357***(4.564)
373

(4)
Tobit
-15.399(8.761)0.723(8.857)50.063***(7.313)
373

(5)
Tobit

(6)
Tobit

-25.589*(10.044)-5.733(9.893)0.649(10.009)0.811(10.256)-27.028**(9.779)-4.201(9.627)0.560(9.796)-0.866(10.015)0.733*(0.289)-10.472(6.129)-7.671(6.849)-0.126**(0.043)42.832**(14.121)373

50.001***(7.270)373

21WHEN LEARNING TOGETHER GOES WRONG

quitters
finishers(1)OLS-1.756*(0.887)-0.006(0.890)similar-quitter
high-quitter
similar-finisher
high-finisher

(2)
OLS

(3)
OLS

-2.924**(1.017)-0.662(1.001)-0.788(1.001)0.865(1.027)-2.891**(1.014)-0.704(0.999)-0.739(1.005)0.754(1.028)0.026(0.030)-0.310(0.632)0.692(0.705)0.008(0.004)17.557***(1.450)373

agefemalecollegeknown_words
Constant
N

19.586***(0.735)373

19.586***(0.729)
373

(4)
Tobit
-1.814*(0.895)-0.045(0.898)19.659***(0.742)
373

(5)
Tobit

(6)
Tobit

-2.996**(1.023)-0.703(1.007)-0.830(1.007)0.830(1.034)-2.960**(1.014)-0.745(1.000)-0.770(1.006)0.718(1.029)0.024(0.030)-0.321(0.632)0.743(0.705)0.009(0.004)17.643***(1.450)373

19.658***(0.734)373

Notes: The first three columns of the table report the results from ordinary least squares (OLS)
regressions, while the last three columns present the outcomes of Tobit regressions. The basecondition of these analyses is the Single condition. Standard errors in parentheses; *, ** and ***stand for statistical significance at the 5%, 1% and 0.1% level respectively.
Regression Analysis with IOS
In additional analysis, we examined the correlation between learning outcomes and IOS,
describing the psychological distance between the subjects and their partners. We expected thepositive correlation in cases where the partners possessed higher perseverance and a negative
correlation when partners exhibited lower perseverance because larger peer effects were found frompeople with closer social distance in the previous literature (e.g., Bandiera et al., 2010).

WHEN LEARNING TOGETHER GOES WRONG

22

We found a moderate level of negative correlation between the number of studied words and IOS
when the partners possessed lower perseverance: r = -0.26 (p = .03) in the similar-quitter condition;r = -0.22 (p = .05) in the high-quitter condition. In the similar-finisher condition, we observed amoderate level of positive correlation (r = 0.21; p = .06), but not in the high-finisher condition (r = 0.15; p = .20).
We regressed the number of studied words on the treatment dummy variables (using the similarquitter condition as the baseline treatment), IOS and their interaction variables. The results,
presented in the top of Table 4, consistently show significant positive coefficients for the similarfinisher and IOS interaction term, indicating a more positive correlation between IOS and thenumber of studied words in the similar-finisher condition compared to the similar-quitter condition.
These results held across four regression models: without control variables (1st column), with
control variables (2nd column), Tobit regression without control variables (3rd column), and Tobit
regression with control variables (4th column).
The correlation patterns between the test scores and IOS were similar to those observed between
the number of studied words and IOS.9 The bottom of Table 4 shows the same regressions in termsof the test scores. Again, we found the significant positive coefficients of the similar-finisher and
IOS interaction term in the regressions.

9

The results are as follows: In the similar-quitter condition, r = -0.21 (p = .07); in the high-quitter condition, r= -0.14 (p = .20); in the similar-finisher condition, r = 0.15 (p = .19); and in the high-finisher condition, r = 0.11 (p = .37).

23

WHEN LEARNING TOGETHER GOES WRONG

Table 4
Regression Results (Study 1): The Effect of Conditions and IOS on Perseverance (Top) andPerformance (Bottom)

high-quitter
similar-finisher
high-finisher
IOS
high-quitter#IOS
similar-finisher#IOS
high-finisher#IOS

(1)
OLS

(2)
OLS

(3)
Tobit

(4)
Tobit

9.487
(14.452)-17.989(14.422)11.558(14.696)-10.270(5.422)0.952(7.182)
16.538**(6.248)
5.173(6.530)7.099(14.208)-24.337(14.161)
10.957(14.242)-12.309*(5.334)3.455(7.114)
19.635**(6.122)5.730(6.364)-0.081**(0.028)34.239*(13.133)
Yes
303

24.339(22.440)-23.265(22.781)27.930(23.198)-10.475(8.320)-3.012(11.009)21.437*(9.766)1.605(10.154)

21.742(21.882)-32.710(22.196)27.017(22.334)-13.393(8.134)0.390(10.808)
26.243**(9.512)2.346(9.832)-0.117**(0.044)28.456(20.350)
Yes
303

known_words
Constant
Controls
N

44.338***(10.935)No
303

44.070*(16.975)
No
303

24

WHEN LEARNING TOGETHER GOES WRONG

high-quitter
similar-finisher
high-finisher
IOS
high-quitter#IOS
similar-finisher#IOS
high-finisher#IOS

(1)
OLS

(2)
OLS

(3)
Tobit

(4)
Tobit

1.423
(2.387)-2.531(2.382)2.286(2.427)-1.493(0.895)0.422(1.186)
2.195*(1.032)0.969(1.078)1.940(2.415)-2.464(2.407)2.616(2.421)-1.346(0.907)0.107(1.209)2.148*(1.041)0.725(1.082)0.008(0.005)16.975***(2.233)
Yes
303

1.513
(2.376)-2.561(2.370)2.348(2.416)-1.493(0.891)0.390(1.180)2.218*(1.027)0.959(1.073)2.066(2.388)-2.473(2.379)2.691(2.393)-1.335(0.896)0.054(1.196)
2.162*(1.029)0.705(1.069)0.008(0.005)16.940***(2.206)Yes
303

known_words
Constant
Controls
N

19.426***(1.806)No303

19.426***(1.797)
No
303

Notes: The first two columns of the table report the results from ordinary least squares (OLS)
regressions, while the last two columns present the outcomes of Tobit regressions. The basecondition of these analyses is the similar-quitter condition. In the second and fourth columns,
variables controlled for include age, gender (noted as female), and attainment of a college degree orhigher. Standard errors in parentheses; *, ** and *** stand for statistical significance at the 5%, 1%
and 0.1% level respectively.
Discussion
In summary, our findings decisively demonstrate a negative impact from partners with low levelsof perseverance, particularly when the skill levels were similar to the subjects. This negative
influence was not evident from higher-skilled partners. Contrarily, we found no evidence of thepositive effects from partners with higher perseverance levels. We entertained the possibility that

WHEN LEARNING TOGETHER GOES WRONG

25

the failure to identify the positive effects was due to a ceiling effect, suggesting that there was little
room for already motivated subjects to increase their study efforts. Indeed, we found the average
enjoyment on the experiment was pretty high (7.51 out of 10)
Our analysis regarding IOS implies that lower social proximity accounts for the absence ofoverall positive peer effects. Prior research has investigated the relationship between peer effects
and social distance (Bandiera et al., 2010; Bicchieri et al., 2022). Specifically, Bicchieri et al.
(2022) underscore the pivotal role of social proximity (e.g., higher IOS) on enhancing positive peer
effects in norm compliance behaviors. They demonstrated that without social proximity, there's adecline in norm adherence; however, in the presence of social proximity, norm erosion is averted as
subjects react to both compliant and non-compliant peers’ behaviors. This implies that the solely
negative peer effects observed in our primary analysis could stem from insufficient social proximity
toward partners. This idea is further supported by our data showing that the mean IOS score inpaired conditions was a mere 2.19 out of 7, indicating a low average level of felt social proximity topartners.
Moreover, our analysis suggested the significance of skill similarity of partners in fostering
positive peer effects. Our data indicated some evidence of a positive association between IOS andthe number of words studied in the similar-finisher condition. This suggests that having partners ofsimilar skill levels, combined with social proximity, is pivotal for positive peer influence. This
interpretation is corroborated by research showing the presence of positive peer effects among
subjects of similar abilities (Booij et al., 2017; Carrell et al., 2013).

Study 2: Sample with Lower Motivation in English Vocabulary Learning
In Study 1, we identified negative peer effects from low perseverance partners, especially when
the partner’s skills were similar to those of the subjects. Despite this, no overarching positive peer

WHEN LEARNING TOGETHER GOES WRONG

26

effects were detected. We considered the possibility that the failure to detect positive peer effects inStudy 1 could be attributed to a ceiling effect, which implies that the subjects who were alreadymotivated had little room to further increase their study efforts.
Therefore, in Study 2, we recruited subjects with a lower baseline of motivation to reexamine thepeer effects, specifically to investigate the potential positive effects from high perseverance
partners.
Subjects. We recruited 400 subjects for our experiment through Lancers. We excluded 3 subjects
declaring to know more than 400 words, as in Study 1, because their vocabulary level was too highfor this experiment. As a result, we obtained a demographically diverse sample of 397 subjects intotal (43% female, 𝑀𝑎𝑔𝑒 = 40.66 years, age range: 18-76 years). Of these subjects, 47% are fully
employed, 15% hold part-time positions, 2% are students, and the remaining 37% fall into othercategories. The participation requirements for this study were identical to those in Study 1, exceptthat subjects must not have engaged in English language study for more than six months.10 The
subjects received a fixed fee of 600 yen if they completed all the parts of the experiment. The
duration of the experiment varied depending on how many English words they wanted to learn, witha median duration of approximately 39 minutes.
Design and procedure. The procedure was identical to that of Study 1 except for one question.
Right before the question asking how fun the study was, we added the intrinsic motivation scale(Schmidt et al., 1996). Incorporating this scale aimed to validate the participation of subjects withweak motivation to learn English. The scale was a 6-point scale (1: Strongly disagree – 6: Strongly

10

Not engaging English study refers to not taking English lessons or classes, not studying independently withEnglish materials or apps, not watching English videos or podcasts, not learning by watching English movies
or TV dramas, not reading English books or blogs, and not placing oneself in an environment where English
is spoken.

WHEN LEARNING TOGETHER GOES WRONG

27

agree). The scale included four questions such as “I enjoy learning English very much”.11 We
calculated the average of these responses to create the variable motivation (𝛼 = 0.74).
Results and Discussion
Manipulation Check
The manipulations regarding the skills and perseverance of the partners in this study weresuccessful. Subjects in the high-skilled condition believed their partner had significantly higher
vocabulary levels (M = 5.51, SD = 1.41) compared to those in the similar-skilled partners (M =4.07, SD = 1.12; F (1,319) = 101.26, p < .001, η2 = .24). Subjects in the finisher conditions perceived
that their partner studied more (M = 6.70, SD = 2.38) compared to those in the quitter conditions (M= 3.75, SD = 2.66; F(1,319) = 108.09, p < .001, η2 = .25).
Descriptive Analysis
Table 5 shows the selected characteristics of the subjects for each treatment including variables
known_words, eng_level, age and female as in Study 1. The differences in these variables between
the treatments were not statistically significant, indicating the randomization worked well (all pvalues above 0.1).12 The mean value of motivation was 2.90 (the minimum possible value is 1 andthe maximum possible value is 6), which was less than the middle point of 3.5 (t (396) = 13.81; p <0.001), suggesting their motivation toward learning English was not high.

We excluded the one question “I wish I could learn English in an easier way, without going to class” fromthe original list of Schmidt et al. (1996) because not all the subjects were students.
12
As we expected, the degree of intrinsic motivation did not differ between treatments (ANOVA: F (4, 392) =0.73, p = .57), indicating the variable motivation described stable personality trait.
11

28

WHEN LEARNING TOGETHER GOES WRONG

Table 5
Descriptive Statistics (Study 2)
Highquitter

Similarfinisher

Highfinisher

singleStatistic
test

p-valueknown_words 37.97

33.48

31.15

35.79

32.11

F (4,392) =0.30

p = .88(43.34)(38.40)(57.88)(43.12)(40.23)

1.38

1.51

1.25

1.31

1.38

F (4,392) =1.94

p =.10(0.65)(0.71)(0.55)(0.57)(0.67)

2.98

2.88

2.78

2.87

2.98

F (4,392) =0.73

p = .57(0.98)(0.88)(0.66)(0.87)(0.89)

41.22

39.37

40.40

41.30

41.28

F (4,392) =0.53

p = .71(11.42)(10.42)(9.3)(12.29)(10.03)female

0.39

0.39

0.50

0.44

0.43

N

78

94

72

77

76

Similarquitter

eng_levelmotivationageχ (4) = 2.65 p = .62

Notes: Standard deviations in parentheses. The variable known_words represents the number ofwords subjects knew in the pre-learning part; eng_level denotes their self-assessed level of English
proficiency; motivation describes the average value of the intrinsic motivation scale (Schmidt et al.,1996); and female indicates the proportion of female subjects.
The characteristics of the sample were different between Studies 1 and 2 as expected (Table 6).
The variable known_words was smaller in Study 2 (t (615) = 11.66; p < 0.001) and the level ofEnglish proficiency was lower in Study 2 (t (674) = 9.12; p < 0.001). The subjects in Study 2
enjoyed the experiment less (t (759) = 4.93; p < 0.001). These findings implied the subjects inStudy 2 were less motivated to learn English.

29

WHEN LEARNING TOGETHER GOES WRONG

Table 6
Summary Statistics in Studies 1 and 2
Study 1

Study 2t-testp-value

84.60

34.13

11.66

p < .001(71.62)(44.52)

1.89

1.38

9.12

p < .001(0.89)(0.64)

7.51

6.70

4.93

p < .001(2.34)(2.23)# of studied words

42.36

38.20

0.65

p < .52(single treatment)(39.37)(37.72)# of correct words

19.59

17.14

2.45

p < .02(single treatment)(5.65)(6.39)

known_words

eng_level

enjoy

Notes: Standard deviations in parentheses. The variable known_words represents the number ofwords subjects knew in the pre-learning part; eng_level denotes their self-assessed level of English
proficiency; and enjoy measures the overall enjoyment subjects reported for learning English words
in the experiment.
Regression Analysis
Perseverance. We initially conducted our regression analysis using an OLS method to evaluate
the impact of learning partners' perseverance on the number of words studied, following the sameprocedure as in Study 1 (refer to the top of Table 7). The models were identical to those used inStudy 1, with the exception that motivation was included as a control variable in the models

WHEN LEARNING TOGETHER GOES WRONG

30

incorporating demographic variables. And then, as in Study 1, we repeated these three analyses
using Tobit regression methods (see columns 4, 5, and 6).
In column 1, the OLS model revealed a significantly negative coefficient for quitters, indicatingthat subjects studied approximately 10 words fewer on average when paired with low perseverance
partners, compared to the single condition (p = .04). The Tobit model showed a decrease of about
15 words when paired with such partners (p = .05). This substantial overall negative effect onperseverance is similar to the findings from Study 1.
Interestingly, the other regression models consistently demonstrated a significant negative effectfrom the high-quitter condition rather than the similar-quitter one, diverging from the results ofStudy 1. Specifically, the Tobit model including demographic variables indicated that subjects in
the high-quitter condition studied around 16 words fewer on average than those in the single
condition (column 5). However, the coefficient for similar-quitter was not significant, indicatingthat similar-skilled partners did not negatively affect subjects’ perseverance in Study 2.
As in Study 1, we found no evidence of significant positive peer effects from high perseverance
partners. Additionally, the variable age and motivation had positive impacts on the number ofstudied words (columns 3 and 5).
Performance. We also conducted a regression analysis on the test scores (the bottom of Table 3).
The main findings largely align with those from the analysis of the number of studied words.
Specifically, subjects in the high-quitter condition consistently demonstrated a negative effectacross the four regressions. In this condition, the average test score was about 1.8 words lower
compared to the single condition (columns 2, 3, 5 and 6). However, the coefficient for quitters was
negative but not significant (columns 1 and 4). We found no evidence of significant positive peer
effects from high perseverance partners on own performance. Additionally, motivation positively
influenced the test scores.

WHEN LEARNING TOGETHER GOES WRONG

31

We replicated the analysis utilizing ANOVA (see Appendix A) and applied both Generalized
Linear Models (GLM) with Poisson and Negative Binomial distributions as a robustness check.13
The overall findings are consistent with those from the main regression analysis.

Figure 4. The Number of Studied Words in the Voluntarily Learning Part (Top) and the Number ofCorrect Words in the Test Part (Bottom) for each Treatment (Study 2).
Means are represented by three distinct shapes: a circle for Finisher, a triangle for Quitter, and a diamond forSingle condition. Standard error bars are included.

13

We also utilized zero-truncated models due to the absence of zero counts in our dependent variables. The
results from all GLMs were consistent with those from our main analysis and are available upon request.

32

WHEN LEARNING TOGETHER GOES WRONG

Table 7
Regression Results (Study 2): The Effect of Conditions on Perseverance (Top) and Performance(Bottom)

quitters
finishers

(1)

(2)

(3)

(4)

(5)

(6)

OLS

OLS

OLS

Tobit

Tobit

Tobit

-10.325*-14.690*(5.035)(7.425)

2.903

3.448

(5.153)(7.643)similar-quitter
high-quitter
similar-finisher
high-finisher-6.467-6.302-9.625-9.049(5.895)(5.783)(8.655)(8.426)-13.527*-12.044*-18.856*-16.295*(5.642)(5.554)(8.285)(8.083)

2.150

3.637

2.232

4.728

(6.015)(5.892)(8.886)(8.639)

3.608

4.411

4.606

5.945

(5.913)(5.781)(8.786)(8.529)age
femalecollegeknown_words
motivation

0.539**

0.830***(0.168)(0.247)

4.966

7.321

(3.668)(5.374)

3.066

4.055

(3.781)(5.538)-0.061-0.088(0.042)(0.061)

6.739**

10.004**(2.119)Constant
N(3.143)38.197***38.197***-5.94843.885***43.866***-22.937(4.193)(4.195)(10.402)(6.212)(6.198)(15.367)

397

397

397

397

397

397

33

WHEN LEARNING TOGETHER GOES WRONG

quitters
finishers
similar-quitter
high-quitter
similar-finisher
high-finisher

(1)

(2)

(3)

(4)

(5)

(6)

OLS

OLS

OLS

Tobit

Tobit

Tobit

-1.232-1.208(0.833)(0.838)

0.701

0.704

(0.853)(0.857)-0.504-0.824-0.476-0.799(0.975)(0.946)(0.978)(0.943)-1.836*-1.899*-1.815-1.875*(0.933)(0.908)(0.936)(0.905)

0.577

0.927

0.553

0.906

(0.995)(0.964)(0.998)(0.961)

0.816

0.851

0.846

0.877

(0.978)(0.945)(0.981)(0.942)age
femalecollegeknown_words
motivation
Constant
N

0.053

0.052

(0.027)(0.027)-0.068-0.086(0.600)(0.598)1.688**

1.681**(0.618)(0.617)

0.010

0.010

(0.007)(0.007)1.324***

1.337***(0.347)(0.346)17.145***9.823***

17.145***9.847***(0.694)(1.701)397

397(0.696)397(1.696)397

Notes: The first three columns of the table report the results from ordinary least squares (OLS)
regressions, while the last three columns present the outcomes of Tobit regressions. The basecondition of these analyses is the Single condition. Standard errors in parentheses; *, ** and ***stand for statistical significance at the 5%, 1% and 0.1% level respectively.
Regression Analysis with Motivation
Next, we focused our analysis on motivation. The variable motivation was positively correlatedwith the number of studied words (r = 0.15; p = 0.003) and the test scores (r = 0.20; p < 0.001).

WHEN LEARNING TOGETHER GOES WRONG

34

To probe further, we divided the sample into two groups based on the median motivation value of
3. Initially, we conducted a Tobit regression analysis on the number of studied words, using
treatment dummy variables, demographic variables, and known_words as the explanatory variables(Table 8). Among the less motivated sample (column 1), the coefficients for high-quitter andsimilar-quitter were significantly negative. Contrarily, among the more motivated sample (column2), they were not statistically significant. This result suggests that negative peer effects primarily
emanate from the less motivated group.
A parallel approach was employed to analyze the test scores (columns 3 and 4), and we foundsimilar results with the one regarding perseverance. Among the less motivated sample, thecoefficient for high-quitter was significantly negative (p < 0.001), although the coefficient forsimilar-quitter was not statistically significant. Among the more motivated sample, the coefficientsfor both high-quitter and similar-quitter were not statistically significant.
Regression Analysis with IOS
In alignment with the approach in Study 1, we examined the relationship between learning
outcomes and social proximity measured by IOS scale. Consistent with our expectations, we founda moderate negative correlation between the number of studied words and IOS when the partners
possessed lower perseverance: r = -0.17 (p = .13) in the similar-quitter condition; r = -0.02 (p = .88)
in the high-quitter condition. Furthermore, we found a moderate positive correlation between thenumber of studied words and IOS when the partners possessed higher perseverance: r = 0.23 (p= .05) in the similar-finisher condition; r = 0.12 (p = .28) in the high-finisher condition, aligningwith our hypothesis as well. A similar correlation was observed between the test scores and IOS.14

14

The results are as follows: r = -0.13 (p = .28) in the similar-quitter condition; r = 0.11 (p = .31) in the highquitter condition; r = 0.27 (p = .02) in the similar-finisher condition; and r = 0.13 (p = .25) in the high-finisher
condition

WHEN LEARNING TOGETHER GOES WRONG

35

Table 8
Tobit Regression Results by Motivation-divided Group (Study 2): The Effect of Conditions on
Perseverance and Performance
(1)
(2)
(3)
(4)
DV =
DV =
DV =
DV =
Perseverance
Perseverance
Performance
PerformanceLess motivatedMore motivatedLess motivatedMore motivated**similar-quitter-32.992
6.982-1.461-0.335(11.924)(11.928)(1.395)(1.294)****high-quitter-42.146
2.777-3.436-0.385(11.292)(11.666)(1.313)(1.272)similar-finisher -12.693
12.8020.628
0.784(11.684)(12.937)(1.366)(1.388)high-finisher-16.20221.2901.315
0.351(12.149)(11.918)(1.405)(1.278)
*
**
age
0.819
0.966
0.043
0.073
(0.327)(0.368)(0.039)(0.039)*female-3.23919.100-0.6890.725(7.259)(7.919)(0.857)(0.847)college12.221-5.3971.850*
1.472(7.475)(8.120)(0.880)(0.874)
known_words
0.042-0.0950.025
0.010(0.114)(0.074)(0.013)(0.008)Constant
18.618-7.64013.595***13.054***(17.074)(18.299)(2.006)(1.975)
N
193
204
193
204
Notes: Standard errors in parentheses; *, ** and *** stand for statistical significance at the 5%, 1%

and 0.1% level respectively. Columns 1 and 3 include only the less intrinsically motivated sample(motivation < 3), while columns 2 and 4 contain only the more motivated sample (motivation ≥ 3).
The variable known_words represents the number of words subjects knew in the pre-learning part;and college denotes if the subject has a college degree.
To further investigate these relationships, we regressed the number of studied words on thetreatment dummy variables (with the similar-quitter condition as the baseline treatment), IOS andtheir interaction variables. The structure of these four regressions, presented at the top of Table 9,

WHEN LEARNING TOGETHER GOES WRONG

36

paralleled our previous analyses. We consistently found significant positive coefficients for theinteraction term between similar-finisher and IOS across all four regressions: without control
variables (column 1), with control variables (column 2), Tobit regression without control variables(column 3), and Tobit regression with control variables (column 4). This suggests stronger positive
correlation between IOS and the number of studied words in the similar-finisher condition,compared to the similar-quitter condition across the four regressions. The bottom of Table 9
displayed analogous regressions for the test scores. Again, we found the significant positivecoefficients for the interaction term between similar-finisher and IOS.
Discussion
Study 2 successfully recruited subjects who were less motivated and less skilled. In line with theresults of Study 1, Study 2 consistently demonstrated an overall negative impact from partners withlower levels of perseverance, and failed to find evidence supporting a positive impact from partners
with higher levels of perseverance. Additionally, as in Study 1, we observed a positive association
between IOS and the learning outcomes in the similar-finisher condition.
Interestingly, the negative effects in this study were observed from partners who were higherskilled, diverging from Study 1, where the negative effect emerged from partners with similar skill
levels. In addition, our nuanced analysis revealed that the subjects with lower intrinsic motivation
towards English learning were primarily affected by these negative effects.

37

WHEN LEARNING TOGETHER GOES WRONG

Table 9Regression Results (Study 2): The Effect of Conditions and IOS on Perseverance (Top) andPerformance (Bottom)

high-quitter
similar-finisher
high-finisher
IOS
high-quitter#IOS
similar-finisher#IOS
high-finisher#IOS(1)
OLS-15.658(10.663)-16.284(11.533)-7.990(11.279)-4.551(3.194)4.081(4.694)11.503*(4.603)8.175(4.358)known_words
Constant
ControlsN

41.182***(7.790)
No
321

(2)
OLS
-14.582(10.576)-15.082(11.510)-7.254(11.245)-4.635(3.162)
3.838(4.654)10.898*(4.572)
7.788(4.332)-0.044(0.045)18.615(11.307)
Yes
303

(3)
Tobit
-22.635(15.340)-25.178(16.897)-13.723(16.716)-5.960(4.586)6.541(6.720)17.204*(6.835)12.496(6.494)46.551***(11.237)
No
303

(4)
Tobit
-20.613(15.097)-22.917(16.734)-12.487(16.573)-5.995(4.507)
5.994(6.611)
16.115*(6.734)11.852(6.415)-0.061(0.065)12.448(16.347)
Yes
303

WHEN LEARNING TOGETHER GOES WRONG

38

(1)
OLS
-3.582*(1.749)-2.869(1.892)-1.265(1.851)-0.579(0.524)1.154(0.770)1.815*(0.755)1.161(0.715)

(2)
(3)
(4)
OLS
Tobit
Tobit
*
*
high-quitter-3.486-3.600-3.501*(1.719)(1.749)(1.707)
similar-finisher-2.359-2.964-2.448(1.871)(1.891)(1.858)high-finisher-0.409-1.255-0.407(1.828)(1.850)(1.816)
IOS-0.599-0.597-0.614(0.514)(0.524)(0.510)high-quitter#IOS
1.189
1.158
1.191
(0.756)(0.770)(0.751)
similar-finisher#IOS
1.779*
1.836*
1.798*(0.743)(0.754)(0.738)high-finisher#IOS
0.888
1.159
0.889
(0.704)(0.714)(0.699)*known_words
0.018
0.018*(0.007)(0.007)
Constant
17.844***13.950***17.908***14.070***(1.278)(1.838)(1.278)(1.826)
Controls
No
Yes
No
Yes
N
321
321
321
321
Notes: The first two columns of the table report the results from ordinary least squares (OLS)
regressions, while the last two columns present the outcomes of Tobit regressions. The basecondition of these analyses is the similar-quitter condition. In the second and fourth columns,
variables controlled for include age, gender (noted as female), and attainment of a college degree orhigher. Standard errors in parentheses; *, ** and *** stand for statistical significance at the 5%, 1%
and 0.1% level respectively.
General Discussion and Conclusions
We conducted two experiments, both resembling a typical online English learning environment.
These experiments scrutinize the clear causal effects of learning partners’ perseverance on
individuals’ perseverance as well as performance, and how these effects varied with the partners'

WHEN LEARNING TOGETHER GOES WRONG

39skill levels, either being higher or similar to the subjects. Our sample, consisting of a diverse
demographic of Japanese individuals, along with the realistic learning environments used in thestudies, provides substantial external validity.
In Study 1, the subjects were actively engaged in studying English. In Study 2, we retained theexperimental structure but enlisted subjects who had refrained from English study for a minimum ofsix months. A consistent finding across both studies was the substantial overall negative impact ofpartners characterized by lower perseverance levels. The number of words studied dropped byapproximately 11 to 15 words. In Study 1, there was also a decrease of about 1.8 points in testscores. We did not encounter evidence of positive impacts from partners with higher levels of
perseverance.
In Study 1, this negative influence was attributed to partners of similar skill levels, whereas inStudy 2, it emanated from higher-skilled partners.
Deepening our analysis in Study 2 to examine the intrinsic motivation towards English learning,
we discerned that the less intrinsically motivated sample was the primary contributor to thesenegative effects. This observation aligns with Tanaka's (2017) survey study, which focused on
students with low intrinsic motivation. She identified the presence of negative peer effects, as wellas the absence of positive peer effects, on one’s motivation in vocabulary learning.
Intriguingly, both studies provided some evidence suggesting that social proximity, measured by
IOS, could foster positive peer effects when partners possess similar skills and higher perseverance
levels. The results align with earlier research demonstrating pronounced peer effects amongindividuals sharing closer social distance (Bandiera et al., 2010).
Pronounced Negative Peer Effects and Absence of positive peer effects
Our findings demonstrate an asymmetry in positive and negative peer effects, where negative
influences from peers are more potent. The identification of negative peer effects on individual

WHEN LEARNING TOGETHER GOES WRONG

40

perseverance (e.g., Battaglini et al., 2017; Isler & Gächter, 2022; Thöni & Gächter, 2015), and theabsence of positive influences (e.g., Bicchieri et al., 2022; Georganas et al., 2015; Isler & Gächter,2022; Rauhut, 2013; Thöni & Gächter, 2015), are well-documented phenomena.
Particularly, Thöni and Gächter (2015) observed patterns similar to ours. They found that whensubjects observed a peer exerting less (hypothetical) effort given a certain wage, they substantiallydecreased their own effort in a gift-exchange experiment in the lab. However, observing a peerchoosing more effort did not consistently lead to an increase in their own effort. Isler and Gächter(2022)’s study on conforming behaviors suggested that negative peer effects may arise when a peer
unexpectedly violates a norm.
This interpretation of Isler and Gächter (2022) could also explain the observed asymmetry in ourstudy. Subjects in the single condition studied a significantly higher number of words on averagecompared to those whose partners exhibited low perseverance. Presumably expecting their partners
to study a comparable amount, subjects were markedly affected when faced with their partners'
unexpectedly low engagement. This discrepancy led to a more pronounced impact of negative
effects compared to positive ones.
Sources of Negative Peer Effects in Studies 1 and 2
Although we found overall negative peer effects in both studies, the effect varied with the peer's
skill level. Specifically, in Study 1, subjects were influenced by the similar-quitter treatment but not
the high-quitter treatment. Conversely, in Study 2, they were affected by the high-quitter treatment
but not the similar-quitter treatment. This section speculates on the reasons for these differences bydrawing comparisons with existing literature.
Negative effects from partners with similar skill levels were observed in Study 1, possibly due toperceived lower self-efficacy when partners discontinued their efforts. This interpretation aligns
with Schunk and DiBenedetto (2021), who noted that witnessing a similarly skilled peer fail canWHEN LEARNING TOGETHER GOES WRONG

41

reduce an individual’s self-efficacy. Schunk and Hanson (1985) claimed that self-efficacy is criticalin learning environments, influencing effort investment, persistence, and overall success.
Additionally, social learning theory underscores the tendency of individuals to mimic the
behaviors of similar peers (Hanna et al., 2013). Also, the peer effect economic model proposed byBattaglini et al. (2005) suggests that an individual’s perception of their own perseverance isnegatively affected when they observe similar others failing to handle temptations.
Another interpretation from Study 1 suggests that subjects, already possessing a certain level ofEnglish knowledge, may have perceived that high-quitters—those with even higher ability thanthemselves—stopped learning because they already had substantial knowledge. Drawing upon thefindings of Isler and Gächter (2022), it seems plausible that negative peer effects failed to occur,
because the act of quitting was not sufficiently surprising to the subjects.
In Study 2, negative effects emerged among subjects paired with high-skilled partners, who wereless proficient than those in Study 1. This pattern is consistent with previous research, whichsuggests that less successful individuals are more inclined to rely on social information and emulate
the behaviors of peers who are successful, attractive, prestigious, or of high status (Atkisson et al.,2012; Hanna et al., 2013; Henrich & Gil-White, 2001; Mesoudi, 2008). Individuals are more likely
to pay attention to and learn from those who are skilled at a task (Bandura, 1986), and to copy theirstrategies (Mesoudi & O'Brien, 2008; Mesoudi, 2008). Additionally, another research also shows
negative influences from high-skilled peers among lower-ability individuals (Feld & Zölitz, 2017).
Positive Perception Bias
Furthermore, our auxiliary analysis of the perception of partners' influences on perseverance,
obtained from the survey question, suggests that perceived influences does not strongly correlatewith subjects’ actual perseverance. In other words, subjects did not accurately estimate theirpartners’ influences on the number of words they studied (see Appendix B for the detailed analysis).

WHEN LEARNING TOGETHER GOES WRONG

42

Interestingly, the results also imply a positive perception bias. Specifically, among subjects pairedwith low perseverance peers, the majority believed they received no influence or even a positive
influence from their partners, despite pronounced negative effects actually being observed acrossboth studies. Conversely, among subjects paired with high perseverance partners, the majority
believed they received positive influences, although non-significant positive effects were actuallyobserved in our studies.
These findings imply that perception measures, commonly used in previous studies (e.g., Keefe,
1994; Steinberg & Silverberg, 1986; Urberg et al., 1990), do not accurately reflect subjects’ actual
behaviors and highlight the importance of directly measuring behaviors.
Implications
Our research primarily focused on the peer effects in online English learning due to the ease of
implementation. However, our findings can be applied broadly, thanks to the vast array of online
courses available in diverse domains, such as programming, art, science, business management, andeven online fitness or mindfulness courses like yoga and meditation. The impact of peer effects inthese various fields may diverge from our current findings, offering deeper insights into the natureof peer influences.
Furthermore, this study has several other potential applications, including exploring crosscultural peer effects and testing various interventions, such as setting default options to continue
studying or sharing more detailed information about learning partners, like their politicalpreferences or favorite movies. Our experimental design, implemented via Qualtrics, can facilitatesuch studies, and the file is available upon request.
To address the issue of attrition, online learning platforms have proactively developed socialfeatures to promote user engagement. Our study provides two practical suggestions for enhancing
user-matching algorithms to optimize motivation and engagement. First, caution is advised when

WHEN LEARNING TOGETHER GOES WRONG

43

pairing users with partners who display low perseverance, especially for users who are lessintrinsically motivated and hence more susceptible to negative peer influences. Second, matchingusers with socially proximate partners who are similarly skilled and display higher perseverance
levels can increase engagement and motivation. These user-matching strategies have the potential tobe effective not only in online educational settings but also in various other collaborative scenarios.

WHEN LEARNING TOGETHER GOES WRONG

44

Acknowledgements: The authors thank Satoshi Akutsu, Yukihiko Funaki, Noboyuki Hanaki,
Yoshio Kamijyo, Daniel-Navarro Martinez, Charles Noussair, Erica Okada, Jordi Quoidbach,
Tiffany Tsz Kwan Tse, Marie-Claire Villeval, Marc Willinger, and Takeshi Nishimura for helpfulcomments and suggestions. Special thanks to Koki Shimazu for his support in creating 500 English
vocabulary questions for the experiments and to Ryan Wong, a research assistant, for his help increating graphs.
Competing Interests Statement: The authors declare that they have no conflict of interest. The
experiment reported in this paper has been approved by the ethical committee at Hitotsubashi
University (No. 2021A011).
Financial Support Statement: This research has benefited from the financial support of (a)Grants-in-aid for Scientific Research No. 21K13257 from the Japan Society for the Promotion of
Science, and (b) Sasakawa Scientific Research Grant No. 2022-1032 from the Japan Science
Society.
Supplementary Materials: All the experimental instructions and raw data from the entire
experiments are available on the OSF website (https://osf.io/7qc35/).

WHEN LEARNING TOGETHER GOES WRONG

45

References
Angrist, J. D. (2014). The perils of peer effects. Labour Economics, 30, 98–108.
https://doi.org/10.1016/j.labeco.2014.05.008
Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the self scale and the structure ofinterpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596–612.
https://doi.org/10.1037/0022-3514.63.4.596
Atkisson, C., O’Brien, M. J., & Mesoudi, A. (2012). Adult learners in a novel environment use
prestige-biased social learning. Evolutionary Psychology, 10(3), 519–537.
https://doi.org/10.1177/147470491201000309
Bandiera, O., Barankay, I., & Rasul, I. (2010). Social incentives in the workplace. Review of
Economic Studies, 77(2), 417–458. https://doi.org/10.1111/j.1467-937X.2009.00574.x
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory (pp. xiii,
617). Prentice-Hall, Inc.
Battaglini, M., Bénabou, R., & Tirole, J. (2005). Self-control in peer groups. Journal of Economic
Theory, 123(2), 105–134. https://doi.org/10.1016/j.jet.2005.04.001
Battaglini, M., Díaz, C., & Patacchini, E. (2017). Self-control and peer groups: An empiricalanalysis. Journal of Economic Behavior & Organization, 134, 240–254.
https://doi.org/10.1016/j.jebo.2016.12.018
Beugnot, J., Fortin, B., Lacroix, G., & Villeval, M. C. (2019). Gender and peer effects on
performance in social networks. European Economic Review, 113, 207–224.
https://doi.org/10.1016/j.euroecorev.2019.01.002
Bicchieri, C., Dimant, E., Gächter, S., & Nosenzo, D. (2022). Social proximity and the erosion ofnorm compliance. Games and Economic Behavior, 132, 59–72.
https://doi.org/10.1016/j.geb.2021.11.012

WHEN LEARNING TOGETHER GOES WRONG

46

Booij, A. S., Leuven, E., & Oosterbeek, H. (2017). Ability peer effects in university: Evidence froma randomized experiment. The Review of Economic Studies, 84(2), 547–578.
https://doi.org/10.1093/restud/rdw045
Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. University of Chicago
Press.
Buechel, B., Mechtenberg, L., & Petersen, J. (2014). Peer effects and students’ self-control. MPRA
Paper, Article 53658. https://ideas.repec.org//p/pra/mprapa/53658.html
Buechel, B., Mechtenberg, L., & Petersen, J. (2018). If I can do it, so can you! Peer effects onperseverance. Journal of Economic Behavior & Organization, 155, 301–314.
https://doi.org/10.1016/j.jebo.2018.09.004
Bursztyn, L., & Jensen, R. (2017). Social image and economic behavior in the field: Identifying,understanding, and shaping social pressure. Annual Review of Economics, 9(1), 131–153.
https://doi.org/10.1146/annurev-economics-063016-103625
Carrell, S. E., Fullerton, R. L., & West, J. E. (2009). Does your cohort matter? Measuring peer
effects in college achievement. Journal of Labor Economics, 27(3), 439–464.
https://doi.org/10.1086/600143
Carrell, S. E., Sacerdote, B. I., & West, J. E. (2013). From natural variation to optimal policy? The
importance of endogenous peer group formation. Econometrica, 81(3), 855–882.
https://doi.org/10.3982/ECTA10168
Duflo, E., Dupas, P., & Kremer, M. (2011). Peer Effects, teacher incentives, and the impact of
tracking: Evidence from a randomized evaluation in Kenya. American Economic Review,101(5), 1739–1774. https://doi.org/10.1257/aer.101.5.1739

WHEN LEARNING TOGETHER GOES WRONG

47

Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: A qualitative study exploring
why learners drop out of MOOCs. Journal of Computing in Higher Education, 29(1), 133–
146. https://doi.org/10.1007/s12528-016-9127-8
Feld, J., & Zölitz, U. (2017). Understanding peer effects: On the nature, estimation, and channels ofpeer effects. Journal of Labor Economics, 35(2), 387–428. https://doi.org/10.1086/689472
Georganas, S., Tonin, M., & Vlassopoulos, M. (2015). Peer pressure and productivity: The role ofobserving and being observed. Journal of Economic Behavior & Organization, 117, 223–232. https://doi.org/10.1016/j.jebo.2015.06.014
Gerhards, L., & Gravert, C. (2020). Because of you I did not give up – Peer effects in perseverance.
Journal of Economic Psychology, 81, 102316. https://doi.org/10.1016/j.joep.2020.102316
Golsteyn, B. H. H., Non, A., & Zölitz, U. (2021). The impact of peer personality on academic
achievement. Journal of Political Economy, 129(4), 1052–1099.
https://doi.org/10.1086/712638
Hanna, R. C., Crittenden, V. L., & Crittenden, W. F. (2013). Social learning theory: A multicultural
study of influences on ethical behavior. Journal of Marketing Education, 35(1), 18–25.
https://doi.org/10.1177/0273475312474279
Henrich, J., & Gil-White, F. J. (2001). The evolution of prestige: Freely conferred deference as amechanism for enhancing the benefits of cultural transmission. Evolution and Human
Behavior, 22(3), 165–196. https://doi.org/10.1016/S1090-5138(00)00071-4
Isler, O., & Gächter, S. (2022). Conforming with peers in honesty and cooperation. Journal of
Economic Behavior & Organization, 195, 75–86.
https://doi.org/10.1016/j.jebo.2021.12.026

WHEN LEARNING TOGETHER GOES WRONG

48

Jaccard, J., Blanton, H., & Dodge, T. (2005). Peer influences on risk behavior: An analysis of theeffects of a close friend. Developmental Psychology, 41(1), 135–147.
https://doi.org/10.1037/0012-1649.41.1.135
Keefe, K. (1994). Perceptions of normative social pressure and attitudes toward alcohol use:
Changes during adolescence. Journal of Studies on Alcohol, 55(1), 46–54.
https://doi.org/10.15288/jsa.1994.55.46
Kruger, J., & Gilovich, T. (1999). “Naive cynicism” in everyday theories of responsibility
assessment: On biased assumptions of bias. Journal of Personality and Social Psychology,
76(5), 743–753. https://doi.org/10.1037/0022-3514.76.5.743
Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers &Education, 48(2), 185–204. https://doi.org/10.1016/j.compedu.2004.12.004
Lomi, A., Snijders, T. A. B., Steglich, C. E. G., & Torlo, V. J. (2011). Why are some more peer
than others? Evidence from a longitudinal study of social networks and individual academic
performance. Social Science Research, 40(6), 1506–1520.
https://doi.org/10.1016/j.ssresearch.2011.06.010
Lyle, D. S. (2007). Estimating and Interpreting Peer and Role Model Effects from Randomly
Assigned Social Groups at West Point. The Review of Economics and Statistics, 89(2), 289–
299.
Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The
Review of Economic Studies, 60(3), 531–542. https://doi.org/10.2307/2298123
Mas, A., & Moretti, E. (2009). Peers at work. American Economic Review, 99(1), 112–145.
https://doi.org/10.1257/aer.99.1.112
Mesoudi, A. (2008). An experimental simulation of the “copy-successful-individuals” culturallearning strategy: Adaptive landscapes, producer–scrounger dynamics, and informational

WHEN LEARNING TOGETHER GOES WRONG

49

access costs. Evolution and Human Behavior, 29(5), 350–363.
https://doi.org/10.1016/j.evolhumbehav.2008.04.005
Mesoudi, A., & O’Brien, M. J. (2008). The cultural transmission of great basin projectile-point
technology I: An experimental simulation. American Antiquity, 73(1), 3–28.
https://doi.org/10.1017/S0002731600041263
Narayanasamy, S. K., & Elçi, A. (2020). An effective prediction model for online course dropout
rate. International Journal of Distance Education Technologies (IJDET), 18(4), 94–110.
https://doi.org/10.4018/IJDET.2020100106
Nation, I. S. P. (Ed.). (2001). The Cambridge Applied Linguistics Series. In Learning vocabulary inanother language (3rd ed., pp. ii–iv). Cambridge University Press.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review ofGeneral Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175
Nisbett, R. E., & Wilson, T. D. (1977). The halo effect: Evidence for unconscious alteration of
judgments. Journal of Personality and Social Psychology, 35(4), 250–256.
https://doi.org/10.1037/0022-3514.35.4.250
Rauhut, H. (2013). Beliefs about lying and spreading of dishonesty: Undetected lies and theirconstructive and destructive social dynamics in dice experiments. PLOS ONE, 8(11),e77878. https://doi.org/10.1371/journal.pone.0077878
Rosaz, J., Slonim, R., & Villeval, M. C. (2016). Quitting and peer effects at work. Labour
Economics, 39, 55–67. https://doi.org/10.1016/j.labeco.2016.02.002
Ross, L., & Ward, A. (1996). Naive Realism in Everyday Life: Implications for Social Conflict and
Misunderstanding. In Values and Knowledge. Psychology Press.

WHEN LEARNING TOGETHER GOES WRONG

50

Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination
theory perspective: Definitions, theory, practices, and future directions. Contemporary
Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Sacerdote, B. (2014). Experimental and quasi-experimental analysis of peer effects: Two steps
forward? Annual Review of Economics, 6(1), 253–272. https://doi.org/10.1146/annureveconomics-071813-104217
Schmidt, R., Boraie, D., & Kassabgy, O. (1996). Foreign Language motivation: Internal structureand external connections. In Language learning motivation: Pathways to the new century(pp. 9–70). University of Hawaii, Second Language Teaching & Curriculum Center.
http://hdl.handle.net/10125/40769
Schunk, D. H., & DiBenedetto, M. K. (2021). Chapter four—Self-efficacy and human motivation.
In A. J. Elliot (Ed.), Advances in Motivation Science (Vol. 8, pp. 153–179). Elsevier.
https://doi.org/10.1016/bs.adms.2020.10.001
Schunk, D. H., & Hanson, A. R. (1985). Peer models: Influence on children’s self-efficacy andachievement. Journal of Educational Psychology, 77(3), 313–322.
https://doi.org/10.1037/0022-0663.77.3.313
Shimazu, K. (2021). Bijuaru de oboeru IELTS kihon eitango. Japan taimuzu shuppan.
Steinberg, L., & Silverberg, S. B. (1986). The vicissitudes of autonomy in early adolescence. Child
Development, 57(4), 841–851. https://doi.org/10.2307/1130361
Suls, J., Wan, C. K., & Sanders, G. S. (1988). False consensus and false uniqueness in estimating
the prevalence of health-protective behaviors. Journal of Applied Social Psychology, 18(1),66–79. https://doi.org/10.1111/j.1559-1816.1988.tb00006.x
Tanaka, M. (2017). Examining EFL vocabulary learning motivation in a demotivating learning
environment. System, 65, 130–138. https://doi.org/10.1016/j.system.2017.01.010

WHEN LEARNING TOGETHER GOES WRONG

51

Tello, S. F. (2007). An analysis of student persistence in online education: International Journal of
Information and Communication Technology Education, 3(3), 47–62.
https://doi.org/10.4018/jicte.2007070105
Thöni, C., & Gächter, S. (2015). Peer effects and social preferences in voluntary cooperation: A
theoretical and experimental analysis. Journal of Economic Psychology, 48, 72–88.
https://doi.org/10.1016/j.joep.2015.03.001
Urberg, K. A., Shyu, S. J., & Liang, J. (1990). Peer influence in adolescent cigarette smoking.
Addictive Behaviors, 15(3), 247–255. https://doi.org/10.1016/0306-4603(90)90067-8
Van Boven, L., Kamada, A., & Gilovich, T. (1999). The perceiver as perceived: Everyday intuitions
about the correspondence bias. Journal of Personality and Social Psychology, 77(6), 1188–
1199. https://doi.org/10.1037/0022-3514.77.6.1188
Vantage Market Research. (2023). Online Education/E-Learning Market—Global industry
assessment & forcast. Vantage Market Research. https://www.vantagemarketresearch.com
Vorauer, J. D., & Miller, D. T. (1997). Failure to recognize the effect of implicit social influence on
the presentation of self. Journal of Personality and Social Psychology, 73(2), 281–295.
https://doi.org/10.1037/0022-3514.73.2.281
Zabell, S. (2023, June 6). 4 reasons Duolingo is more fun with friends. Duolingo Blog.
https://blog.duolingo.com/friends-social-features/

WHEN LEARNING TOGETHER GOES WRONG

52

Appendix A: Analysis with ANONAFor the main analysis, we employed regression analysis. In this section, we replicate the analysis
utilizing ANOVA. It is important to note that, unlike Tobit regressions, ANOVA does not accountfor the constraints that subjects could not study fewer than 1 or more than 95 words, or correctly
answer fewer than 0 or more than 30 words. Nonetheless, the overall patterns are consistent with themain regression analysis, indicating that our results are robust.
Study 1
Perseverance. The main effect of experimental condition on the number of their studied words
was significant (analysis of variance (ANOVA): F (4,368) = 3.04, p = .02, η2 = .03). We further
conducted multiple comparisons with Holm’s sequentially rejective Bonferroni procedure. As
shown in Figure 3, there were negative effects from the partners with low perseverance. Subjects inthe similar-quitter condition (M = 25.32, SD = 32.13) studied fewer words compared to those in the
single condition (M = 42.36, SD = 39.37), indicating the negative peer effect (t (368) = 2.68, p= .05). The number of studied words in the similar-quitter condition was also significantly lower
than the similar-finisher (M = 43.33, SD = 40.62; t (368) = 2.91, p = .04) and high-finisher
conditions (M = 43.62, SD = 40.45; t (368) = 2.88, p = .04). There was no statistically significant
difference between those in single and high-quitter condition (M = 37.08, SD = 37.72; t (368) =0.84, p > .99). On the other hand, positive effects stemming from peers with high perseverance were
not found; the number in the single condition was not different from that in either the similarfinisher (t (368) = 0.16, p > .99) or high-finisher condition (t (368) = 0.20, p > .99).
Performance. The results of ANOVA revealed the statistically significant main effect ofexperimental condition on the test scores (F (4,368) = 3.86, p = .004, η2 = .04). Negative peer effects
were observed, while positive peer effects were not. Specifically, the similar-quitter condition
yielded a lower average test scores (M = 16.66, SD = 5.71) compared to the single condition (M =WHEN LEARNING TOGETHER GOES WRONG

53

19.59, SD = 5.65; t (368) = 2.88, p = .03), although no significant differences were found between
the high-quitter (M = 18.92, SD = 6.66) and single conditions (t (368) = 0.66, p > .99). This patternof results confirms that negative peer effects emanate exclusively from similarly skilled partners.
Further, the absence of positive peer effects was confirmed, as the test scores in the similar-finisher
condition (M = 18.80, SD = 6.39) and in the high-finisher condition (M = 20.45, SD = 5.92) did notstatistically differ from the single condition (t (368) = 0.79, p > .99; t (368) = 0.84, p > .99)
Study 2
Perseverance. We found a statistically significant main effect of the conditions on the number ofstudied words was significant (analysis of variance (ANOVA): F (4, 392) = 3.22, p = .01, η2 = .03).
We further conducted multiple comparisons with Holm’s sequentially rejective Bonferroni
procedure as in Study 1. As shown in Figure 4, there appears negative effects from the partners withlow perseverance due to a noticeable difference of 13.53 words between that in high-quitter andsingle conditions. Nonetheless, the number in the high-quitter condition (M = 24.67, SD = 31.92)
and that in the similar-quitter condition (M = 31.73, SD = 34.22) did not significantly differ fromthat in the single condition (M = 38.20, SD = 37.72; t (392) = 2.40, p = .10 and t (392) = 1.10, p= .99). Note that we did find that the number was significantly smaller in the high-quitter condition
compared to the similar-finisher (t (392) = 2.74, p = .04) or high-finisher conditions (t (392) = 3.05,
p = .02), indicating the negative impacts from the high-skilled partners quitting midway comparedto finishers. On the other hand, no positive peer effects were found as in Study 1. The number ofstudied words in both the similar-finisher (M = 40.35, SD = 38.67) and the high-finisher condition(M = 41.81, SD = 40.79) was not different from the single condition (t (392) = 0.36, p = .99 and t(392) = 0.61, p = .99).
Performance. The results of ANOVA revealed the statistically significant main effect ofexperimental condition on test scores (F (4, 392) = 2.62, p = .03). Figure 4 indicates negative effects,

WHEN LEARNING TOGETHER GOES WRONG

54

characterized by a noticeable difference of 1.84 words, constituting approximately 6% of allquestions, between the high-quitter and single conditions. Yet, the test scores in the high-quittercondition (M = 15.31, SD = 5.92) as well as that in the similar-quitter condition (M = 16.64, SD =5.97) did not significantly differ from that in the single condition (M = 17.14, SD = 6.39; t (392) =1.97, p = .30 and t (392) = 0.52, p = .99). Importantly, lower performance was indeed observed
when the high-quitter condition was compared to the high-finisher condition because the test scoresin the high-quitter condition was lower compared to that in the high-finisher condition (t (392) =2.85, p = .05). As in Study 1, no positive peer effects were found; the test scores in both the similarfinisher (M = 17.72, SD = 5.89) and the high-finisher condition (M = 17.96, SD = 6.07) was notstatistically different from the single condition (t (392) = 0.58, p = .99 and t (392) = 0.83, p = .99).

WHEN LEARNING TOGETHER GOES WRONG

55

Appendix B: Perception of the Influences from Partners
Across both studies, we consistently found an overall negative impact from partners with lower
perseverance. However, we did not find evidence of positive impacts from partners with higher
levels of perseverance. This raises an important question: Did subjects accurately perceive theinfluences from their partners, or was their perception systematically biased? Addressing thisquestion could offer deeper insights into the absence of overall positive peer effects.
According to the literature, common measures of peer effects, such as self-reports on the extent
to which individuals believe their behavior results from peer effects (e.g., Keefe, 1994; Steinberg &Silverberg, 1986; Urberg et al., 1990), may not be reliable. This unreliability stems fromindividuals’ inability to accurately evaluate the influences of their peers. A related concept is naïve
realism (Ross & Ward, 1996), where people tend to believe they see the world exactly as it is,
without bias. They think their attitudes and beliefs arise from an unbiased view of facts, yet theyoften underestimate their own biases (Kruger & Gilovich, 1999; Nisbett & Wilson, 1977; Van
Boven et al., 1999).
Individuals tend to underestimate the influences from peers. For example, subjects failed toperceive the effect of peers’ self-descriptions on their own self-descriptions, while observers
indicated that they were indeed influenced by peers’ self-descriptions (Vorauer & Miller, 1997).
However, individuals may also overestimate the extent of peer effects to justify their decisions andpast behavior (Jaccard et al., 2005; Suls et al., 1988).
Therefore, we conducted an analysis to explore if the perception of the influences from partnersaligns with the actual results of perseverance. To the best of our knowledge, no studies haveexamined such perception bias in both positive and negative peer effects.

WHEN LEARNING TOGETHER GOES WRONG

56

Study 1
Surprisingly, we did not find a significant correlation between the number of studied words andthe perception of the influences except in the similar-finisher condition (r = .058, p = .62 in thesimilar-quitter; r = .095, p = .41 in the high-quitter; r = .43, p < .001 in the similar-finisher; r = .14,
p = .23 in the high-finisher). This indicates that the perception of the partners’ influences, especiallynegative influences, does not strongly reflect the actual perseverance.
From the 7-point scale question regarding the perception of the partner’s influence on the amountof words learned by subjects, we created the variable partner_influence. This variable takes thevalue of 1 if the answer on the scale is more than 4 (positive influence), 0 if the answer is 4 (noinfluence), and -1 if the answer is less than 4 (negative influence). Table A1 summarizes each valueof partner_influence for each treatment. The results show notable differences from the findings in
our regression analysis. The perceptions of negative influence from quitters did not appear to bestrong. For example, an equal proportion of subjects in the high-quitter condition perceived apositive influence (24.05%) and a negative influence (24.05%) on their number of words studied.
On the other hand, the perceptions of positive influence from finisher appeared to be strong. For
example, 51.90% of subjects in the similar-finisher condition perceived a positive influence in their
number of words studied, compared to only 3.08% who perceived a negative influence.
Study 2
Although the tendency was slightly weaker than in Study 1, Study 2 still exhibited a similar
pattern where the perception of the influences did not strongly correlate with the number of studied
words.

57

WHEN LEARNING TOGETHER GOES WRONG

Table A1
Perception of Partners Influences (Study 1)

partner_influence
Negative
No influence
Positive
Total

similar-quitter

high-quitter

similarfinisher

high-finisher

all treatments

23(31.08%)26(35.14%)25(33.78%)74

19(24.05%)41(51.90%)19(24.05%)79

3(3.80%)
35(44.30%)41(51.90%)79

10(14.08%)20(28.17%)41(57.75%)71

55(18.15%)122(40.26%)126(41.58%)
303

Specifically, we found a positive correlation between the number of studied words and theperception of the influences in the similar-quitter and the similar-finisher treatments (r = .28, p= .01 in the similar-quitter; r = .46, p < .001 in the similar-finisher). However, this correlation was
not present in the other treatments (r = .14, p = .21 in the high-finisher; r = .13, p = .20 in the highquitter). These findings suggest that the perception of the partners’ influences does not reflect theactual perseverance well.
Table A2 summarizes each value of partner_influence for each treatment as in Study 1. The
overall results are consistent with those of Study 1. The perceptions of negative influence fromquitters did not appear to be strong. For example, a similar proportion of subjects in the high-quittercondition perceived a positive influence (34.04%) and a negative influence (30.85%) in their
number of words studied. Conversely, the perceptions of positive influence from finisher appeared
to be strong. For example, 51.95% of subjects in the high-finisher condition perceived a positive
influence in their number of words studied, compared to only 6.49% who perceived a negative
influence.

58

WHEN LEARNING TOGETHER GOES WRONG

Table A2
Perception of Partners Influences (Study 2)
similar-quitter

high-quitter

similarfinisher

high-finisher

all treatments

26(33.33%)31(39.74%)21(26.92%)78

29(30.85%)33(35.11%)32(34.04%)94

5(6.94%)
33(45.83%)34(47.22%)72

5(6.49%)
32(41.56%)40(51.95%)77

65(20.25%)129(40.19%)127(39.56%)
321

partner_influence
Negative
No influence
Positive
Total

Discussion
Our analysis of the perception of partners' influences on perseverance, obtained from the survey
responses, suggests that these perceptions do not strongly correlate with actual perseverance. In
other words, subjects misjudged their partner’s influences on the number of words they studied.
Interestingly, the results also imply a positive perception bias. Specifically, among subjects pairedwith low perseverance peers, the majority believed they perceived no influence or even a positive
influence from their partners, despite pronounced negative effects actually being observed acrossboth studies. In contrast, among those paired with high perseverance partners, the majority reported
positive influences, although non-significant positive effects were actually observed in our studies.
These findings imply that perception measures, commonly used in previous studies (e.g., Keefe,
1994; Steinberg & Silverberg, 1986; Urberg et al., 1990), do not accurately reflect subjects’ actual
behaviors and highlight the importance of directly measuring behaviors.