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
Koki Shimazu

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,*, Shuma Iwatani2, Koki Shimazu3

1

Graduate School of International Corporate Strategy (ICS), Hitotsubashi University, 2-1-2

Hitotsubashi, Chiyoda-ku, Tokyo 101-8439, Japan. Email: syamamoto@ics.hub.hit-u.ac.jp
Phone: +81342123004 Fax: +81342123006*Corresponding author

2

Faculty of Sociology, Kwansei Gakuin University, 1-155 Uegaharaichibancho,
Nishinomiya, Hyogo, Japan 662-0891, Email: iwatani.shuma@kwansei.ac.jp

3

Taktoia&Co., Daishin Building 501, 2-9 Kanda Nishikicho, Chiyoda-ku, Tokyo 1010054, Japan. Email: kokishimazu@taktopia.com

Acknowledgements: The authors thank Atsushi Mizumoto, Charles Noussair, Daniel Banki,
Daniel Navarro-Martinez, Daniel Read, Elif Tosun, Jordi Quoidbach, Noboyuki Hanaki, Takeshi
Nishimura, and Yukihiko Funaki for helpful comments and suggestions. This work was supportedby the Joint Usage/Research Center, the Institute of Social and Economic Research (ISER), Japan
Society for the Promotion of Science (grant 21K13257) and Sasakawa Scientific Research Grant(grant 2022-1032).

Declarations of interest: None.

WHEN LEARNING TOGETHER GOES WRONG

2When Learning Together Goes Wrong: Negative Peer Effects in Online Learning

Abstract
This research examined the impacts of peer skill levels and perseverance through two experiments
resembling online learning platforms. Study 1 recruited current English learners, while Study 2
involved participants who had not engaged in studying for more than six months. The results in bothexperiments revealed negative rather than positive peer effects. The participants ceased studying
earlier and displayed reduced performance when learning with peers possessing lower perseverance,compared to when studying alone. This pattern was observed for similarly-skilled peers in Study 1and higher-skilled peers in Study 2. Further analysis indicated that the negative peer effects
predominantly originated from participants 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. Our findings suggest that the strategic pairing of learners with appropriate
partners is crucial for diminishing negative peer effects and enhancing positive peer influences.
Keywords: peer effects, perseverance, performance, online learning

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When 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 catering to a vast array oflearning needs ranging from programming (e.g., Codecademy) and language (e.g., Duolingo) to
obtaining certifications such as CFP (e.g., Udemy), have experienced explosive growth in recent
years.
The online learning market, known for its limitless potential and extensive reach, has been
expanding at an unprecedented rate. As of 2022, the industry was valued at $198.2 billion, and is
projected to reach a value of 602.0 billion by 2030, according to the market research company
Vantage Market Research, thereby reinforcing its growing popularity and acceptance worldwide.
The rising number of users engaging in these platforms attests to this growth. For instance,
language learning platform Duolingo reported 49.5 million monthly active users 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). A considerable portion of their users tend to quit midway. Indeed,
research suggests that student attrition rates on online courses are generally higher than those in
traditional classroom-based courses (Levy, 2007; Tello, 2007).
The trend of users abandoning their courses before completion undermines the potential of theseplatforms and poses 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 set up a feature of “leaderboards”; a function of monitoring how much

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4

learners progress each week, compared to other learners around the world. The users can ascend theleaderboard through active engagement with the platform and can gauge their standings relative tousers they follow. Duolingo claims that learners who follow other users on Duolingo are 5.6 times
more likely to finish their course (Zabell, 2023). Another intriguing feature to enhance users’ online
learning experience is “Friends Quest.” Every week, users are randomly paired up with one of their
Duolingo friends. They are assigned a challenge such as completing a certain number of lessons.
Once they complete the challenge in five days, they can win a reward in Duolingo.
As illustrated in the examples above, online learning platforms have been striving to strengthen
the engagement of users by encouraging the users to interact with other users. Numerous previous
papers have demonstrated that productivity or duration of work are positively influenced by thepresence of peers or collaboration with others. Particularly, collaboration and monitoring appear tocontribute positive peer effects from the results of several research pieces. Kandel & Lazear (1992)
and Baron & Kreps (1999) found that mutual monitoring and peer pressure can lead to increased
productivity and Falk & Ichino (2006) have demonstrated that individuals tend to spend more timeon tasks when working in pairs than when working alone, highlighting the potential role of peer
effects in improving work engagement. Furthermore, Mas & Moretti (2009) found beneficial
productivity spillovers when highly productive staff members were incorporated into a work shift.
One critical factor influencing peer effects seems to be the abilities of the peers. Some researchsuggest that better peers can indeed be beneficial (Hoxby & Weingarth, 2006). For example, being
observed by higher-skilled peers — quantified in terms of the average number of items scanned per
second over a ten-minute period — can increase workers' productivity due to concerns about selfimage (Mas & Moretti, 2009; Bursztyn & Jensen, 2017).
On the contrary, other studies have demonstrated that having higher-achieving peers cansometimes have no influence or even be detrimental. For instance, Van Veldhuizen, Oosterbeek andWHEN LEARNING TOGETHER GOES WRONG

5

Sonnemans (2018) found that being observed by a more productive peer did not increaseindividual’s output. Additionally, Feld and Zölitz (2017) noted that low-achieving students mightexperience adverse effects when in the company of high-achieving peers. Gill & Prowse (2012)
found that individuals are likely to decrease their effort when faced with a competitor's highperformance. This decrease is attributed to disappointment aversion, where agents experience
psychological loss due to deviations from their expectations.
Adding to the complexity of the matter, a review conducted by Sacerdote (2014) found thatroughly half of the analyses did not uncover any statistically significant peer effects fromclassmates’ background ability. This suggests that the influence of peer ability on peer effects is a
topic that warrants further exploration and nuanced understanding.
Another important determinant of peer effects appears to be the level of peers’ perseverance.
Research indicates that students who are grouped with more persistent peers achieve higher scoresin exams (Golsteyn et al., 2021). Similarly, Gerhards & Gravert (2020) found observing peer
behavior significantly enhanced participants' perseverance in a task. Another study has showed that
positive peer effects are observed when paired with a peer demonstrating high levels of self-control,
while negative peer effects manifest when paired with a peer exhibiting low self-control (Battaglini
et al., 2017). Furthermore, sharing information of peers’ exerting perseverance can contribute
positive peer effects (Buechel et al., 2014). Buechel et al. (2018) examined the influence of peer’s
perseverance on performance in a controlled laboratory setting. The results suggested that themagnitude of peer effects was significantly influenced by how successfully achieving individuals
communicated their accomplishments. Positive outcomes were noted when these individuals
conveyed the potential success of others, whereas negative outcomes emerged when they framed
their success as exceptional.

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Drawing on the aforementioned studies, our study aimed to explore the impact of peer skill levels
and perseverance on peer effects, and to examine how these two factors jointly influence learnerperformance and perseverance. Our experiment was designed to mirror realistic online learning
environments possessing intrinsic value, while controlling for other variables. Notably, to the bestof our knowledge, no studies have yet explored these combined effects on learning outcomes.
We formulate hypotheses regarding the influence of skill levels among peers, specifically how
peers of similar and high skill levels affect behavior. First of all, we consider the situation when
individuals possess skill levels similar to those of their peers. We hypothesize that, in this situation,
the duration of time committed to studying is likely to be more closely aligned with that of theirpeers, as opposed to when studying is undertaken independently. As a result, the impact of thesepeers' level of perseverance becomes more pronounced. This hypothesis finds its grounding in thefindings of Rosaz et al. (2016), who observed that the quitting time for tasks tends to align moreclosely with that of their partners through communication.
Rosaz et al. (2016) posited that this effect is due to a reduction in social distances between thepartners. This concept of reduced social distances may also elucidate the findings of Bandiera et al.
(2010). Their study revealed that less proficient workers were more productive when a friend wasworking nearby, whereas no such productivity boost was observed when the nearby individual was
not a friend. This social closeness appears to be associated with the pairing of peers with similarskills, as evidenced by that students with similar skill levels are significantly more likely to choose
similar others as friends and advisors (Lomi et al., 2011).
We further explored the peer effect in situations where partners possess higher skill levels, notingthat the outcomes could be either positive or negative, as the existing literature does not offer clear
predictions. Hoxby and Weingarth (2005) demonstrated the positive peer effects from high-skilledpeers, implying that one's study time may more closely align with that of these peers. Additionally,

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research indicates that being observed by a higher-skilled peer increases worker productivity due toconcerns about self-image (Mas & Moretti, 2009; Bursztyn & Jensen, 2017). In contrast, Villeval(2020) posited that negative peer effects could occur when individuals perceive their lower-thanexpected skill level from their high skilled partners, which could foster feelings of demoralizationor shame.
We recruited Japanese residents and conducted novel experiments. Unlike several previous
studies that experimentally measured peer effects using real effort tasks with little or no intrinsic
value (e.g., Beugnot et al., 2019; Buechel et al., 2014, 2018; Georganas et al., 2015; Gerhards &Gravert, 2020), we provided participants with the opportunities to learn English vocabulary online
which resemble a typical English online learning environment. Japan serves as a suitable context forthis research due to its large market for English language education.1 We chose English vocabulary
as the focus of the experiment because of its likely intrinsic value to such Japanese participants andits ease of implementation. In the experiment, we compared the amount of effort exerted in studying
and test scores between the participants studying alone (single condition) and the others studying
with a partner (pair condition). This approach allowed us to gauge the influence of peer effects onparticipants' perseverance (the amount of effort) as well as their performance. Participants werepaired with pre-recruited learning partners. The partners’ skill levels and perseverance (defined as
the duration for which they continue to learn English vocabulary) were measured prior to ourexperiments. Based on their pre-recorded outputs, we manipulated partners’ levels of skills andperseverance, rendering these variables exogenous; thus, we can clearly estimate their impacts for

1 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 within the country) and Test of English forInternational Communication (TOEIC). In 2022 alone, over 4 million individuals took the Eiken test, whilemore than 2 million participated in the TOEIC exam (including “TOEIC Listening & Reading Test” and“TOEIC Speaking & Writing Tests”), figures that underscore the significant demand for English language
proficiency in Japan.

WHEN LEARNING TOGETHER GOES WRONG

8each effect and the interaction effect. Consequently, such online learning configurations offer
considerable practical implications because our design resembles many online learning platforms.
Our novel design, which can be applied to many other online learning studies, enables us to clearly
estimate causal effects from peers.
We conducted two studies. In Study 1, we recruited participants who were currently studying
English. In Study 2, we recruited participants who had not engaged in studying more than six
months. The participants in Study 2 were less-skilled and did not enjoy studying compared to theparticipants in Study 1. In both experiments, we observed persistent negative peer effects stemmingfrom partners with lower levels of perseverance. If partners abandoned learning midway, both thenumber of words studied and the test scores were lower compared to the outcomes when studying
independently. This pattern held true when participants were paired with peers of similar skill levels(Study 1) and with higher-skilled peers (Study 2). Interestingly, when segmenting the sample basedon the level of intrinsic motivation for learning the English language, the findings indicated that thenegative peer effects predominantly originated from participants with lower levels of motivation.
Less intrinsically motivated participants suffered the negative peer effects to a greater degree.
Although we did not find overall positive peer effects, closer psychological distances werepositively correlated with the number of studied words and the test scores if the partner had similarskills and higher perseverance.
Study 1: Influence of Partner Skills and Perseverance on English Vocabulary Learning
Study 1 was designed to test peer effects from learning partners on an English vocabulary
learning setting. We investigated how the levels of skills and perseverance of learning partners
influenced the peer effects.
Before conducting the main study, we separately conducted a study to recruit potential partners
who were matched with the participants in the main study. We recruited four potential partners,

WHEN LEARNING TOGETHER GOES WRONG

9each possessing different characteristic factors: two distinguished by gender (male and female) andtwo distinguished by their level of English skills (high and middle).2 We asked them to study a totalof 100 English words. Additionally, we instructed them to send emojis from time to time to otherparticipants to motivate their learning. We used the data of high-skilled female and middle-skilled
female as potential partners in the main studies.3
MethodParticipants. In Study 1, we recruited 400 participants through Lancers, Inc.
(https://www.lancers.jp). We excluded 26 participants who claimed to know more than 400 words
in the pre-learning part, which we will detail later, as their vocabulary level exceeded the scope of
this experiment. Consequently, we obtained a demographically diverse sample of 373 participantsin total (48% female, 𝑀𝑎𝑔𝑒 = 38.8 years, age range: 18-72 years). Of these participants, 52% arefully employed, 14% hold part-time positions, 5% are students, and the remaining 29% fall into
other categories. The criteria of our participants 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). The participants received a fixed fee of 600 yen. The median duration of theexperiment was around 48 minutes, but it varied depending on how many English words theywanted to learn.
Design and procedure. The participants were assigned to pair or single conditions. The
participants in the pair condition had learning partners (whom we had recruited before conducting
this experiment as mentioned above), whereas the participants in the single condition did not have2

Test of English for International Communication (TOEIC) score of 955 is considered as high English skills
and middle TOEIC score of 615 is considered as middle English skills
3
We used female as potential partners in this experiment. This is because participants felt their peers more
beneficial in learning English when paired with female partners, compared to when paired with male partners,
according to the questionnaire in the pretest.

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any partners. Furthermore, the characteristics of partners were manipulated in the pair condition:
English skills (high/similar to the participant), and their study time (quitter/finisher). Therefore, wehave 5 conditions in total.
Participants 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). In the pre-learning part, participants were
presented with English words and they simply answered whether they knew the words (they chosethe option between “I know the word” and “I did not know the word at all”. They were instructed tochoose “I know the word” even if they had some familiarity with the word). We selected 500
English words for the participants from a vocabulary book (Shimazu, 2021). The level of theselected words ranged from B1 to C2 levels in Common European Framework of Reference for
Languages.
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 thatinquired about various attributes, including age, gender, education, marital status, and employment
status. The survey also asked about their primary motivations for learning English as well as theirself-assessed level of English proficiency.4 Additionally, they were instructed to choose their own
avatar like the ones in typical smartphone apps.
Insert Table 1 about here.
Immediately following the questions, participants in the pair conditions were notified that they
were paired with their learning partner (such information was not provided to the participants in the4

The options for the reasons for learning English include: for work, for travel, for 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 others. The English proficiency levels range frombeginner (1) to expert (5). The participants who selected 5 in this question were not allowed to continue theexperiment as per the instructions.

WHEN LEARNING TOGETHER GOES WRONG

11single condition). They were also informed of the partner's characteristics, including gender, name(comprised of randomly generated characters), and English proficiency skills (See Figure 1 for thescreenshot). At this point, we implemented our first manipulation of the partners’ skill while
avoiding deception problems. Participants in the similar-skilled condition were informed that thepartners answered the same number of words correctly as they did for the 10 questions in the prelearning part. On the other hand, participants in the high-skilled condition were informed that thepartners answered 3 words more correctly than they did for the 10 questions. We deliberately
picked 10 questions in the pre-learning part. For example, we selected 10 questions where theparticipants in the high-skilled condition correctly answered 5 questions, and 10 questions where
their partner correctly answered 8 questions.
Insert Figure 1 about here.
Subsequently, they proceeded to the mandatory learning part. Following the methodology in
Rosaz et al. (2016), this part was designed to let participants to familiarize the procedure, as well as
the level of difficulty and duration of our experimental task. Here, they must learn 5 words out of
100 words that they answered “I did not know the word at all” in the pre-learning part. In this part,questions were presented wherein an English word appeared above five Japanese translation options(Figure 2).5 Participants were asked to select one of the options that correctly translated the given
English word within 10 seconds. After that, the correct answers were shown and they were asked toremember it for 5 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 not choose any option within thetime period, they had a second chance to answer the same question within 10 seconds. Failure to5

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

12choose an option during this additional opportunity resulted in the termination of their participationin the study.
Upon completing the mandatory learning part, they went to the voluntary learning part. The
procedure here was akin to the mandatory learning part, but with notable distinctions. Specifically,
participants had the option to cease their learning at any point after answering one question. They
could study up to 95 additional words. To ensure comprehension of these conditions, participants
were required to answer a comprehension question, clarifying their understanding that they could
discontinue the learning task at their discretion after answering the first question. After theycorrectly answered this question, they were allowed to start learning.
In this part, the procedure differed depending on whether participants 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, participants in the paired condition engaged in learning with informationof their partners. They received emojis from their partner (refer to Figure 2) and had the opportunity
to send emojis to their partner. This procedure aimed to foster a sense of learning with theirpartners.
At this stage, we introduced the second manipulation concerning partners' perseverance level.
Participants in the quitter condition were notified that their partner had ceased studying after six
questions. Conversely, those in the finisher condition were informed that the partner continued to
study questions.
After the voluntary learning part, participants transitioned to the test part. This part consisted of a
vocabulary test featuring 30 words that participants had previously identified as “I did not know theword at all” in the pre-learning part. Following the test, participants in the paired conditions
responded to survey questions concerning their perceptions of their partners. The survey questions
encompassed several aspects by asking following questions: How was the amount of words learned

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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: much higher)?; How did the amount of words
learned by the partner’s influence the amount of words learned by you (1: decreased a lot – 7:
increased a lot)?; 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 participants felt with their partner (1: no overlap – 7: most overlap). Lastly, we asked
how fun learning English words in this application was (with an 11-point scale).
Insert Figure 2 about here.
Results
Table 2 shows the selected characteristics of the participants for each treatment. The variableknown_words represents the number of words participants knew in the pre-learning part, eng_level
denotes their self-assessed level of English proficiency, and female indicates the proportion of
female participants. The variables between the treatments are not statistically significant indicatingthat the randomization works well (all p-values above 0.1).
Insert Table 2 about here.
The manipulations regarding the skills and perseverance of the partners in this study weresuccessful. Participants 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 partners (M =4.25, SD = 1.02; F (1,301) = 76.50, p < .001, η2 = .20). Participants in the finisher conditions perceived
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).
Graphical analysis with ANOVA
The top of Figure 3 illustrates that the number of studied words in the voluntarily part acrossdifferent conditions. Participants seem to have their inherent motivation to engage with English

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vocabulary learning. They learned 38.31 words on average in the voluntary learning part, which
was significantly larger than 1 (t (372) = 18.67, p <.001). In addition, the average enjoyment rating
for this experiment was 7.52 (on a scale from 0 to 10), suggesting a high level of enjoyment.
Next, we found 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 who abandoned their learning
efforts in the voluntary learning part midway (quitters). Participants in the similar-quitter condition(M = 25.32, SD = 32.13) studied a smaller number of 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 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 andhigh-quitter condition (M = 37.08, SD = 37.72; t (368) = 0.84, p > .99). On the other hand, positiveeffects stemming from peers who completed the entire learning tasks were not found; the number inthe single condition was not different from that in either the similar-finisher (t (368) = 0.16, p > .99)
or high-finisher condition (t (368) = 0.20, p > .99).
The bottom of Figure 3 displays that the number of correct answers in the test part acrossdifferent conditions. The primary outcomes mirrored those of the analysis pertaining to the numberof studied words. The results of ANOVA revealed the statistically significant main effect ofexperimental condition on the number of their correct answers (F (4,368) = 3.86, p = .004, η2 = .04).
Negative peer effects were observed, while positive peer effects were not. Specifically, the similarquitter condition yielded a lower average number of correct answers (M = 16.66, SD = 5.71)
compared to the single condition (M = 19.59, SD = 5.65; t (368) = 2.88, p = .03), although no

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15

significant differences were found between the high-quitter (M = 18.92, SD = 6.66) and single
conditions (t (368) = 0.66, p > .99). This pattern of results confirms that negative peer effects
emanate exclusively from similarly skilled partners. Additionally, a notable difference was also
observed between the similar-quitter and high-finisher conditions (t (368) = 3.74, p = .002),
implying that participants paired with quitters performed worse than those paired with finishers.
Further, the absence of positive peer effects was confirmed, as the number of correct answers in thesimilar-finisher condition (M = 18.80, SD = 6.39) and in the high-finisher condition (M = 20.45, SD= 5.92) did not statistically differ from the single condition (t (368) = 0.79, p > .99; t (368) = 0.84, p> .99)
Insert Figure 3 about here.
Regression analysis
Next, we conducted regression analysis to further confirm the above analysis. First, we conducted
an OLS regression of the number of words studied on treatment dummy variables, using the single
condition as the baseline (see column 1 at the top of Table 3). Second, we incorporated
demographic variables such as age, gender, and college—defined by a dummy variable that takes
the value of 1 if the participant has a college degree—as well as a variable known_words for thenumber of known words in the pre-learning part (see column 2). We repeated the analysis using
Tobit regression to account for the constraint that participants could not study fewer than 1 or morethan 95 words and correctly answer fewer than 0 or more than 30 words (see columns 3 and 4). The
results remained consistent with the prior analyses conducted above, revealing that the similarquitter condition had a significantly negative effect across all four regression models. The othertreatment dummy variables of finishers had no significant impacts, indicating that the partners withhigher perseverance had no impact on peer effects.

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We also conducted the same regression analysis regarding the number of correct words in the test
part (the bottom of Table 3). Most of the main findings were same as the results of former analysis
regarding the number of studied words. The similar-quitter condition had a significantly negative
effect on the number across the four regressions.
Insert Table 3 about here.
In additional analysis, we examined the correlation between learning outcomes and IOS,
describing the psychological distance between the participants and their partners. We expected thepositive correlation in cases where the partners completed the tasks (finishers) and a negative
correlation when partners quit the tasks midway (quitters) because larger peer effects were foundfrom people with closer social distance in the previous literature (e.g., Bandiera et al., 2010).
We found a moderate level of correlation between the number of studied words and IOS: r = 0.26 (p = .03) in the similar-quitter condition; r = -0.22 (p = .05) in the high-quitter condition; r =0.21 (p = .06) in the similar-finisher condition; and r = -0.15 (p = .20) in the high-finisher condition.
A similar correlation was observed between the number of correct words and IOS.6 We regressed
the number of studied words on the treatment dummy variables (the similar-quitter condition as thebaseline treatment), IOS and their interaction variables.
The results, presented in the top of Table 4, consistently show significant positive coefficients forthe similar-finisher and IOS interaction term, indicating a more positive correlation between IOS
and the number of studied words in the similar-finisher condition compared to the similar-quittercondition. These results held across four regression models: without control variables (1st column),
with control variables and the variable known_words (2nd column), Tobit regression without

6

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).

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control variables (3rd column), and Tobit regression with control variables and the variableknown_words (4th column).
The bottom of Table 4 shows the same regressions in terms of the number of correct words.
Again, we found the significant positive coefficients of the similar-finisher and IOS interaction termin the regressions.
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 participants. This negative
influence was not evident from higher-skilled partners. Contrarily, we found no evidence of thepositive effects from partners with higher perseverance levels.
Our analysis regarding IOS suggests that lower social proximity accounts for the observed
overall negative 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
participants 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 indicated 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 ofWHEN LEARNING TOGETHER GOES WRONG

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similar skill levels, combined with social proximity, is pivotal for positive peer influences. This
interpretation is corroborated by research showing the presence of positive peer effects among
participants of similar abilities (Booij et al., 2017; Carrell et al., 2013).
Insert Table 4 about here.
Study 2: Sample with Lower Motivation in English Vocabulary Learning
In Study 1, we identified negative peer effects exclusively when the partner’s skills were similar
to those of the participants. Despite this, no overarching positive peer effects were detected. We
entertained the possibility that the failure to identify positive peer effects in Study 1 was due to aceiling effect, suggesting that there was little room for already motivated participants to increase
their study efforts.
Thus, we engaged participants with a lower baseline of motivation to delve deeper into the effectof learning partner skills and perseverance in Study 2.
Participants. We recruited 400 participants for our experiment via Lancers. We excluded 3
participants declaring to know more than 400 words, as in Study 1, because their vocabulary level
was too high for this experiment. As a result, we obtained a demographically diverse sample of 397
participants in total (43% female, 𝑀𝑎𝑔𝑒 = 40.66 years, age range: 18-76 years). Of theseparticipants, 47% are fully employed, 15% hold part-time positions, 2% are students, and theremaining 37% fall into other categories. The requirements for participation in this study wereidentical to those in Study 1, with the only distinction being that the participants must not haveengaged in English language study for more than six months.7 The participants received a fixed fee

7

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

19

of 600 yen if they completed all the parts of the experiment. The median duration of the experiment
was around 39 minutes, but it varied depending on how many English words they wanted to learn.
Design and procedure. The procedure was exactly the same as in 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 served to validate the enrollment ofparticipants with weak motivation to learn English. The scale was a 6-point scale (1: Strongly
disagree – 6: Strongly agree). The scale included four questions such as “I enjoy learning English
very much”.8 We took the average value of these questions to create the variable “motivation”(𝛼 = 0.74).
Results and discussion. Table 5 shows the selected characteristics of the participants for eachtreatment including variables known_words, eng_level, age, gender as in Study 1. The variables
between the treatments were not statistically significant indicating the randomization worked well(all p-values above 0.1).9 The mean value of motivation was 2.90 (the minimum possible value is 1
and the 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.
Insert Table 5 about here.
The manipulations regarding the skills and perseverance of the partners in this study weresuccessful. Participants 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). Participants in the finisher conditions

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 participants were students.
9
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.
8

WHEN LEARNING TOGETHER GOES WRONG

20

perceived their partner studied more (M = 6.70, SD = 2.38) compared to those in the quitterconditions (M = 3.75, SD = 2.66; F(1,319) = 108.09, p < .001, η2 = .25).
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 participants in Study 2
enjoyed the experiment less (t (759) = 4.93; p < 0.001). These findings implied the participants inStudy 2 were less motivated to learn English.
Insert Table 6 about here.
Graphical analysis with ANOVA
The top of Figure 4 shows that the number of studied words in the voluntarily part between theexperimental conditions. We found a statistically significant main effect of the conditions on thenumber of their studied 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 can be seen in Figure 4, there appears negative effects from the partners
who abandoned their learning efforts in the voluntary learning part midway (quitters) due to a
noticeable difference of 13.53 words between that in high-quitter and single conditions.
Nonetheless, the number in the high-quitter condition (M = 24.67, SD = 31.92) and that in thesimilar-quitter condition (M = 31.73, SD = 34.22) did not significantly differ from that 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 thesimilar-finisher (t (392) = 2.74, p = .04) or higher-finisher conditions (t (392) = 3.05, p = .02),
indicating the negative impacts from the high-skilled partners quitting midway compared tofinishers. 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

WHEN LEARNING TOGETHER GOES WRONG

21(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).
The bottom of Figure 4 displays the number of correct answers in the test part across different
conditions. The primary outcomes mirrored those of the analysis pertaining to the number ofstudied words. The results of ANOVA revealed the statistically significant main effect ofexperimental condition on the number of their correct answers (F (4, 392) = 2.62, p = .03). Figure 4
indicates negative effects, characterized by a noticeable difference of 1.84 words, constituting
approximately 6% of all questions, between the high-quitter and single conditions. Yet, the numberof correct words in the high-quitter condition (M = 15.31, SD = 5.92) as well as that in the similarquitter 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 highfinisher condition because the number of correct words in the high-quitter condition was lower
compared to that in the high-finisher condition (t (392) = 2.85, p = .05). On the other hand, no
positive peer effects were found as in Study 1; the number of correct answers 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).
Insert Figure 4 about here.
Regression analysis
In our subsequent analysis, we conducted regression method to further validate our findings,
following the same approach as in Study 1. Initially, we used OLS regression to model the numberof studied words based on treatment dummy variables (see column 1 at the top of Table 7).
Subsequently, we incorporated demographic variables such as age, gender, and educational level,
along with the variable known_words (see column 2). Finally, we included motivation as an

WHEN LEARNING TOGETHER GOES WRONG

22

additional independent variable (see column 3). We repeated these three analyses using Tobit
regression methods (see columns 4, 5, and 6). The coefficient of the high-quitter condition had asignificantly negative effect on the number across all the above regressions. However, the othertreatment dummy variables had no significant impacts, indicating no positive peer effects. The
variable age and motivation had positive impacts on the number of studied words.
We also conducted a regression analysis regarding the number of correct words in the test part(the bottom of Table 7). The main results were the same with the results regarding the number ofstudied words. Specifically, the high-quitter condition had a significantly negative effect across all
six models (the coefficient was marginally significant for only one model). Additionally, motivation
positively influenced the number of correct words.
Insert Table 7 about here.
Next, we conducted analysis focusing on the motivation. The variable motivation was positively
correlated with the number of studied words (r = 0.15; p = 0.003) and correct words (r = 0.20; p <0.001).
To probe further, we partitioned the sample into two groups based on the median motivation
value of 3. Tobit regression analyses were conducted to examine the number of studied words,
utilizing treatment dummy variables, demographic variables, and known_words as the explanatory
variables (Table 8). This analysis was carried out separately for the less intrinsically motivatedsample (column 1) and the more motivated sample (column 2). A parallel approach was employed
to analyze the number of correct words (columns 3 and 4). The coefficients for high_quitter andsimilar_quitter were significantly negative among the less motivated sample, while they were notstatistically significant among the more motivated sample. This result suggests that negative peer
effects primarily emanate from the less motivated group. In terms of the number of correct words
within the less motivated sample, the coefficient for high_quitter was significantly negative (p <WHEN LEARNING TOGETHER GOES WRONG

230.001), although the coefficient for similar_quitter was not statistically significant. Similar to theresults regarding the number of studied words, negative effects on performance were not observedin the more motivated sample, as the coefficients for both high_quitter and similar_quitter were notstatistically significant.
Insert Table 8 about here.
In alignment with the approach in Study 1, we examined the relationship between social
proximity, as measured by IOS scale and learning outcomes. We found a moderate level ofcorrelation between the number of studied words and IOS: r = -0.17 (p = .13) in the similar-quittercondition; r = -0.02 (p = .88) in the high-quitter condition; r = 0.23 (p = .05) in the similar-finisher
condition; and r = 0.12 (p = .28) in the high-finisher condition. A similar correlation was observed
between the number of correct words and IOS.10 To further investigate these relationships, we
regressed the number of studied words on the treatment dummy variables (with the similar-quittercondition as the baseline treatment), IOS and their interaction variables. The structure of these four
regressions, presented at the top of Table 9, paralleled our previous analyses. We consistently foundsignificant positive coefficients for the interaction term between similar-finisher and IOS across all
four regressions: without control variables (1st column), with control variables and the variableknown_words (2nd column), Tobit regression without control variables (3rd column), and Tobit
regression with control variables and the variable known_words (4th column). This suggests morepositive 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 number of correct words. Again, we found the significant
positive coefficients for the interaction term between similar-finisher and IOS.

10

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

24

In summary, Study 2 successfully recruited less motivated and less skilled participants, and it
consistently demonstrated a negative impact from partners with lower levels of perseverance.
Notably, these negative effects were observed from higher-skilled partners, rather than from thosewith similar skill levels. This result differs from the findings of Study 1, which demonstrated
negative effects from similar-skilled partners. Further, our nuanced analysis revealed that theparticipants with lower intrinsic motivation towards English learning were primarily affected bythese negative effects. In line with Study 1, we did not find evidence supporting a positive impact
from partners exhibiting higher levels of perseverance. Also, we observed a positive association
between IOS and the number of words studied in the similar-finisher condition as in Study 1.
Insert Table 9 about here.
General discussion and Conclusions
We conducted two experiments, both resembling a typical online English learning environment
to scrutinize the influence of learning partners’ skills and perseverance on peer effects. Our sample
consists of a diverse demographic of Japanese individuals, ranging from older, employedparticipants to younger students; therefore, the results should demonstrate substantial external
validity.
Furthermore, the two types of the manipulated characteristics of the learning partners are
exogenous, allowing for an accurate estimation of the causal effects rooted in these characteristics.
Investigating peer effects presents inherent challenges, primarily due to identification problems
associated with measuring peer effects (Manski, 1993; Sacerdote, 2014).11

For example, when examining the impact of the other classmates’ average score on an individual student's
score, the student's score and the average score are determined simultaneously. Thus, there's a two-way
relationship between them: the student's score influences the class average, and the class average may also
influence the student's score. Estimating the precise degree of peer influence presents a significant challenge
due to the inherent endogeneity.
11

WHEN LEARNING TOGETHER GOES WRONG

25

In Study 1, the recruited participants were actively engaged in studying English. In Study 2, we
retained the experimental structure but enlisted participants who had refrained from English study
for a minimum of six months. A consistent finding across both studies was the negative impact ofpartners characterized by lower perseverance levels. In Study 1, this negative influence wasattributed to partners of similar skill levels, whereas in Study 2, it emanated from higher-skilled
partners. However, across both investigations, we did not encounter evidence of positive impacts
from partners with higher levels of perseverance.
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 a previous survey study by Tanaka (2017). She
identified the presence of negative peer effects, as well as the absence of positive peer effects, on
one’s motivation in a demotivating learning environment characterized by low intrinsic motivation
and a pervasive low perceived competence in vocabulary learning.
Intriguingly, both studies provided some evidence suggesting that social proximity measured by
IOS could foster positive 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), and the enhanced peer effects
observed when students of similar abilities were grouped together (Booij et al., 2017; Carrell et al.,2013).
Interpretations of negative peer effects
Our findings corroborate an asymmetry in both positive and negative peer effects, with negative
influences from peers appearing to be more potent. The identification of negative peer effects on
individual perseverance, or the absence of positive influences, is not an isolated finding in thisstudy. Battaglini et al. (2005), as well as Battaglini et al., (2017), have documented the negative

WHEN LEARNING TOGETHER GOES WRONG

26

impacts stemming from peers exhibiting low self-control. Georganas et al. (2015) reported thatneither observation of peers' behaviors nor being observed by peers results in positive peer effects.12
This pattern has also been observed in various contexts such as cooperation, dishonesty, andprosocial behaviors (Bicchieri et al., 2022; Isler & Gächter, 2022; Rauhut, 2013; Thöni & Gächter,2015). Thöni and Gächter (2015) found that when participants observed a peer exerting less(hypothetical) effort given a certain wage, they substantially decreased their own effort in a giftexchange experiment in the lab. However, observing a peer choosing more effort did notconsistently lead to an increase in their own effort. Isler and Gächter (2022) suggested that negative
peer effects may arise when a peer, seen as representative of one's group, unexpectedly violates anorm.
In our study, strong negative peer effects on learning perceptions could manifest among
participants whose partners unexpectedly quit learning early during the voluntary learning part.
Although we found negative peer effects in both studies, the degree to which they manifested wasdependent on the peer's skill level. Specifically, in Study 1, participants were not influenced by thehigh-quitter treatment, while in Study 2, they were affected by the high-quitter treatment. Suchvariations could not be sufficiently accounted for by the aforementioned studies, which have notexplored how the magnitude of peer effects differs based on the skill level of peers.

12

In contrast, Gerhards and Gravert (2020) noted that observing peer behavior significantly enhanced
participants' perseverance in a task, as measured by their skipping behavior in an anagram task (word puzzle).
In this task, skipping resulted in a monetary penalty, but waiting 90 seconds offered undertake an alternative
task without any monetary cost. The divergence in findings from our study likely stems from fundamental
differences in experimental design. Firstly, their study's anagram task inherently differs from our study's focus
on English vocabulary learning in an app-like setting, especially in terms of intrinsic value. Moreover, thesolvability of some anagram tasks varied, unlike the universal accessibility of English word study in our
research. Secondly, monetary incentives motivated their participants, in contrast to the intrinsic motivation of
our participants in learning English. Thirdly, the option to skip a task in their study complicates theinterpretation of perseverance. Participants might have perceived peers’ skipping behaviors not merely due toa lack of perseverance but probably as viewing the penalty as negligible, the wait as overly lengthy, or thepeers less skilled. In contrast, quitting behaviors in our study more clearly indicate a loss of interest in
learning.

WHEN LEARNING TOGETHER GOES WRONG

27

Given the absence of definitive interpretations from existing theories and prior research, we offer
speculative insights into our findings. In Study 2, the participants, who had less English knowledge,
may have felt intimidated or demotivated when paired with higher-skilled partners. When suchpartners ceased their learning, participants might interpret this as an implicit message that the task
was too challenging even for those with higher skill levels, thereby justifying their own decision to
discontinue learning.
Furthermore, participants in Study 2 may have harbored underlying doubts about their English
competence, perceiving the task excessively difficult. Witnessing a higher-skilled partner ceaselearning could reinforce these beliefs, (c.f., confirmation bias; Nickerson, 1998) thus contributing totheir decision to cease learning.
Conversely, in Study 1, the participants, possessing a certain level of English knowledge, seem tohave interpreted that “high-quitters”—those with even higher ability than themselves—stopped
learning because they already had substantial knowledge. Drawing upon the findings of Isler and
Gächter (2022), it seems plausible that negative peer effects failed to occur, possibly because the actof quitting was not sufficiently surprising to the participants.
Implications
Our research has primarily focused on the peer effects in online English learning due to the easeof implementing such studies. However, our findings can be applied broadly, thanks to the vastarray of online courses available in diverse domains, such as programming, art, science, business
management, and even online fitness or mindfulness courses like yoga and meditation. The impactof peer effects in these various fields may diverge from our current findings, offering deeper
insights into the nature of peer influences. Consequently, these areas of investigation will serve as
the basis of our future research.

WHEN LEARNING TOGETHER GOES WRONG

28

To address the issue of attrition, online learning platforms like Duolingo have proactively
developed social features to promote users’ engagement. Our study provides two practicalsuggestions for enhancing user-matching algorithms to optimize motivation and engagement. First,
caution is advised when pairing users with partners who display low perseverance, especially forusers who are less intrinsically motivated and hence more susceptible to negative peer influences.
Second, our data indicated that increased engagement and motivation could be achieved bymatching users with socially proximate partners who are similarly skilled and display higher
perseverance levels. These user-matching strategies have the potential to be effective not only in
online educational settings but also in various other collaborative scenarios.

WHEN LEARNING TOGETHER GOES WRONG

29

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Table 1
Summary Procedure of the ExperimentSinglePair condition

condition
High-skilled

Part
1. Pre-learning

Finisher

Quitter

Similar-skilled
Finisher

Quitter

Answered if they recognized given English words (common).
Answered demographics and other related questions (common).

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 (common).
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 (common).

35

WHEN LEARNING TOGETHER GOES WRONG

Table 2
Descriptive Statistics (Study 1)
Similarquitter

Highquitter

Similarfinisher

Highfinisher

SingleStatistic
test

p-value

80.00

86.81

90.48

81.01

83.94

F (4,368) =0.27

p = .90(70.82)(70.10)(88.42)(65.17)(59.70)

1.80

1.87

1.89

2.00

1.89

F (4,368) =0.48

p = .75(0.84)(0.88)(0.97)(0.89)(0.84)

38.64

37.96

38.84

40.51

38.27

F (4,368) =0.61

p = .66(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_levelageχ (4) = 1.31 p = .86

Notes: Standard errors in parentheses. The variable known_words represents the number of words
participants knew in the pre-learning part; eng_level denotes their self-assessed level of English
proficiency; and female indicates the proportion of female participants.

36

WHEN LEARNING TOGETHER GOES WRONG

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

high_quitter

similar_finisher

high_finisher

(1)

(2)

(3)

(4)

OLS

OLS

Tobit

Tobit

-17.033**-17.821**-25.589*-27.028**(6.366)(6.236)(10.044)(9.779)-5.281-4.307-5.733-4.201(6.268)(6.146)(9.893)(9.627)

0.972

0.660

0.649

0.560

(6.268)(6.182)(10.009)(9.796)

1.263

0.329

0.811

-0.866(6.431)(6.324)(10.256)(10.015)

age

femalecollegeknown_words

Constant

N

0.488**

0.733*(0.182)(0.289)-5.661-10.472(3.889)(6.129)-5.143-7.671(4.339)(6.849)-0.087**-0.126**(0.027)(0.043)42.357***37.209***50.001***42.832**(4.564)(8.917)(7.270)(14.121)

373

373

373

373

37

WHEN LEARNING TOGETHER GOES WRONG

similar_quitter

high_quitter

similar_finisher

high_finisher

(1)

(2)

(3)

(4)

OLS

OLS

Tobit

Tobit

-2.924**-2.891**-2.996**-2.960**(1.017)(1.014)(1.023)(1.014)-0.662-0.704-0.703-0.745(1.001)(0.999)(1.007)(1.000)-0.788-0.739-0.830-0.770(1.001)(1.005)(1.007)(1.006)

0.865

0.754

0.830

0.718

(1.027)(1.028)(1.034)(1.029)age

femalecollegeknown_words

Constant

N

0.026

0.024

(0.030)(0.030)-0.310-0.321(0.632)(0.632)

0.692

0.743

(0.705)(0.705)

0.008

0.009

(0.004)(0.004)19.586***17.557***19.658***17.643***(0.729)(1.450)(0.734)(1.450)

373

373

373

373

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 Single condition. Standard errors in parentheses; *, ** and ***stand for statistical significance at the 5%, 1% and 0.1% level respectively.

38

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)

(2)

(3)

(4)

OLS

OLS

Tobit

Tobit

9.487

7.099

24.33921.742(14.452)(14.208)(22.440)(21.882)-17.989-24.337-23.265-32.710(14.422)(14.161)(22.781)(22.196)

11.55810.95727.93027.017(14.696)(14.242)(23.198)(22.334)-10.270-12.309*-10.475-13.393(5.422)(5.334)(8.320)(8.134)

0.952

3.455

-3.0120.390(7.182)(7.114)(11.009)(10.808)

16.538**19.635**21.437*26.243**(6.248)(6.122)(9.766)(9.512)

5.173

5.730

1.605

2.346

(6.530)(6.364)(10.154)(9.832)

known_words-0.081**-0.117**(0.028)(0.044)44.338***34.239*44.070*28.456(10.935)(13.133)(16.975)(20.350)

Controls

No

Yes

No

Yes

N

303

303

303

303

Constant

39

WHEN LEARNING TOGETHER GOES WRONG

high_quitter

similar_finisher

high_finisher

IOS

high_quitter#IOS

similar_finisher#IOS

high_finisher#IOS

(1)

(2)

(3)

(4)

OLS

OLS

Tobit

Tobit

1.423

1.940

1.513

2.066

(2.387)(2.415)(2.376)(2.388)-2.531-2.464-2.561-2.473(2.382)(2.407)(2.370)(2.379)

2.286

2.616

2.348

2.691

(2.427)(2.421)(2.416)(2.393)-1.493-1.346-1.493-1.335(0.895)(0.907)(0.891)(0.896)

0.422

0.107

0.390

0.054

(1.186)(1.209)(1.180)(1.196)

2.195*

2.148*

2.218*

2.162*(1.032)(1.041)(1.027)(1.029)

0.969

0.725

0.959

0.705

(1.078)(1.082)(1.073)(1.069)known_words

0.008

0.008

(0.005)(0.005)19.426***16.975***19.426***16.940***(1.806)(2.233)(1.797)(2.206)

Controls

No

Yes

No

Yes

N

303

303

303

303

Constant

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 Single condition. In the second and fourth columns, variables

WHEN LEARNING TOGETHER GOES WRONG

40

controlled for include age, gender (noted as female), and attainment of a college degree or higher.
Standard errors in parentheses; *, ** and *** stand for statistical significance at the 5%, 1% and0.1% level respectively.

41

WHEN LEARNING TOGETHER GOES WRONG

Table 5
Descriptive Statistics (Study 2)
Similarquitter

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

eng_levelmotivationageχ (4) = 2.65 p = .62

Notes: Standard deviations in parentheses. The variable known_words represents the number ofwords participants knew in the pre-learning part; eng_level denotes their self-assessed level ofEnglish proficiency; motivation describes the average value of the intrinsic motivation scale(Schmidt et al., 1996); and female indicates the proportion of female participants.

42

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 participants knew in the pre-learning part; eng_level denotes their self-assessed level ofEnglish proficiency; and enjoy measures the overall enjoyment participants reported for learning
English words in the experiment.

43

WHEN LEARNING TOGETHER GOES WRONG

Table 7
Regression Results (Study 2): The Effect of Conditions on Perseverance (Top) and Performance(Bottom)
(1)

(2)

(3)

(4)

(5)

(6)

OLS

OLS

OLS

Tobit

Tobit

Tobit

-6.467-6.448-6.302-9.625-9.610-9.049(5.895)(5.850)(5.783)(8.655)(8.540)(8.426)-13.527*-12.786*-12.044*-18.856*-17.699*-16.295*(5.642)(5.614)(5.554)(8.285)(8.185)(8.083)similar_finisher 2.150

2.247

3.637

2.232

2.491

4.728

(6.015)(5.945)(5.892)(8.886)(8.731)(8.639)

3.608

3.543

4.411

4.606

4.528

5.945

(5.913)(5.842)(5.781)(8.786)(8.633)(8.529)0.538**

0.539**

0.830***

0.830***(0.170)(0.168)(0.250)(0.247)

5.748

4.966

8.414

7.321

(3.702)(3.668)(5.436)(5.374)

3.450

3.066

4.656

4.055

(3.824)(3.781)(5.607)(5.538)-0.039-0.061-0.056-0.088(0.042)(0.042)(0.061)(0.061)

similar_quitter

high_quitter

high_finisher

agefemalecollegeknown_words

motivation

Constant

38.197***12.9486.739**

10.004**(2.119)(3.143)-5.94843.866***

5.325-22.937

44

WHEN LEARNING TOGETHER GOES WRONG

N

similar_quitter

high_quitter(4.195)(8.638)(10.402)(6.198)(12.659)(15.367)

397

397

397

397

397

397

(1)

(2)

(3)

(4)

(5)

(6)

OLS

OLS

OLS

Tobit

Tobit

Tobit

-0.504-0.853-0.824-0.476-0.827-0.799(0.975)(0.962)(0.946)(0.978)(0.960)(0.943)-1.836*-2.044*-1.899*-1.815-2.025*-1.875*(0.933)(0.923)(0.908)(0.936)(0.921)(0.905)

0.654

0.927

0.553

0.629

0.906

(0.995)(0.978)(0.964)(0.998)(0.976)(0.961)

0.816

0.681

0.851

0.846

0.708

0.877

(0.978)(0.961)(0.945)(0.981)(0.959)(0.942)

0.053

0.053

0.051

0.052

(0.028)(0.027)(0.028)(0.027)0.085-0.0680.071-0.086(0.609)(0.600)(0.608)(0.598)1.764**

1.688**

1.755**

1.681**(0.629)(0.618)(0.628)(0.617)0.015*

0.010

0.015*

0.010(0.007)(0.007)(0.007)(0.007)

similar_finisher 0.577

high_finisher

agefemalecollegeknown_words

motivation

1.324***

1.337***

45

WHEN LEARNING TOGETHER GOES WRONG(0.347)
Constant

N(0.346)17.145***13.537***9.823***

17.145***13.598***9.847***(0.694)(1.421)(1.701)(0.696)(1.418)(1.696)

397

397

397

397

397

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.

46

WHEN LEARNING TOGETHER GOES WRONG

Table 8
Tobit Regression Results by Motivation-divided Group (Study 2): The Effect of Conditions on
Perseverance and Performance(1)

similar_quitter

high_quitter

similar_finisher

high_finisher

agefemalecollegeknown_words

Constant

N

(2)

(3)

(4)

DV =
Perseverance

DV =Perseverance

DV =Performance

DV =PerformanceLess motivatedMore motivatedLess motivatedMore motivated-32.992**

6.982-1.461-0.335(11.924)(11.928)(1.395)(1.294)-42.146***

2.777-3.436*-0.385(11.292)(11.666)(1.313)(1.272)-12.69312.802

0.628

0.784

(11.684)(12.937)(1.366)(1.388)-16.20221.290

1.315

0.351

(12.149)(11.918)(1.405)(1.278)0.819*

0.966**

0.043

0.073

(0.327)(0.368)(0.039)(0.039)-3.23919.100*-0.6890.725(7.259)(7.919)(0.857)(0.847)12.221-5.3971.850*

1.472(7.475)(8.120)(0.880)(0.874)0.042-0.095

0.025

0.010

(0.114)(0.074)(0.013)(0.008)18.618-7.64013.595***13.054***(17.074)(18.299)(2.006)(1.975)

193

204

193

204

WHEN LEARNING TOGETHER GOES WRONG

47

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 participants knew in the pre-learningpart; and college denotes if the participant has a college degree.

48

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)

(2)

(3)

(4)

OLS

OLS

Tobit

Tobit

-15.658-14.582-22.635-20.613(10.663)(10.576)(15.340)(15.097)-16.284-15.082-25.178-22.917(11.533)(11.510)(16.897)(16.734)-7.990-7.254-13.723-12.487(11.279)(11.245)(16.716)(16.573)-4.551-4.635-5.960-5.995(3.194)(3.162)(4.586)(4.507)

4.081

3.838

6.541

5.994

(4.694)(4.654)(6.720)(6.611)11.503*10.898*17.204*16.115*(4.603)(4.572)(6.835)(6.734)

8.175

7.788

12.49611.852(4.358)(4.332)(6.494)(6.415)known_words

Constant

Controls-0.044-0.061(0.045)(0.065)41.182***18.61546.551***

12.448(7.790)(11.307)(11.237)(16.347)

No

Yes

No

Yes

49

WHEN LEARNING TOGETHER GOES WRONG

N

high_quitter

similar_finisher

high_finisher

IOS

high_quitter#IOS

similar_finisher#IOS

high_finisher#IOS

321

303

303

303

(1)

(2)

(3)

(4)

OLS

OLS

Tobit

Tobit

-3.582*-3.486*-3.600*-3.501*(1.749)(1.719)(1.749)(1.707)-2.869-2.359-2.964-2.448(1.892)(1.871)(1.891)(1.858)-1.265-0.409-1.255-0.407(1.851)(1.828)(1.850)(1.816)-0.579-0.599-0.597-0.614(0.524)(0.514)(0.524)(0.510)

1.154

1.189

1.158

1.191

(0.770)(0.756)(0.770)(0.751)1.815*

1.779*

1.836*

1.798*(0.755)(0.743)(0.754)(0.738)

1.161

0.888

1.159

0.889

(0.715)(0.704)(0.714)(0.699)known_words

Constant

Controls

0.018*

0.018*(0.007)(0.007)17.844***13.950***17.908***14.070***(1.278)(1.838)(1.278)(1.826)

No

Yes

No

Yes

50

WHEN LEARNING TOGETHER GOES WRONG

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 Single condition. In the second and fourth columns, variables
controlled for include age, gender (noted as female), and attainment of a college degree or higher.
Standard errors in parentheses; *, ** and *** stand for statistical significance at the 5%, 1% and0.1% level respectively.

WHEN LEARNING TOGETHER GOES WRONG

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

51

WHEN LEARNING TOGETHER GOES WRONG

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.
Participants are required to select the correct Japanese translation of the presented English word from fivegiven options.

52

WHEN LEARNING TOGETHER GOES WRONG

53

WHEN LEARNING TOGETHER GOES WRONG

54

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.

WHEN LEARNING TOGETHER GOES WRONG

55

WHEN LEARNING TOGETHER GOES WRONG

56

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.