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. 2025 Feb 18;95(4):1063–1079. doi: 10.1111/bjep.12754

Investigating peer influence on collaborative group members' motivation through the lens of socially shared regulation of learning

Hanna Järvenoja 1,✉, Tiina Törmänen 1, Emma Lehtoaho 1, Marjo Turunen 1, Jasmiina Suoraniemi 1, Justin Edwards 1
PMCID: PMC12590931  PMID: 39966963

Abstract

Background

Social context and peers significantly impact students' motivation, especially in collaborative learning settings. However, there is limited evidence on how students strategically influence each other's motivation through socially shared regulation of learning (SSRL).

Aims

This study examined secondary school students' SSRL during collaborative learning, focusing on how groups regulate motivation and how these regulation processes influence individual situational motivation through peer interactions.

Sample

The participants were 95 secondary school students (13–16 years) performing a collaborative science task in 31 groups.

Methods

Collaborative learning was videotaped to capture motivation regulation from social interactions. Four times during the task, individual perceptions of peer influence on motivation and motivation regulation were collected with situational self‐reports, and individual stimulated‐recall interviews were conducted after the task.

Results

The results showed that motivation regulation is embedded within broader SSRL processes. When motivation regulation coincided more likely with cognitive regulation, students perceived significantly higher peer influence on motivation. In interviews, students highlighted cognitive and social aspects of SSRL as crucial for their situational motivation but did not hardly recognize any direct motivation regulation strategies.

Conclusions

This study contributes to the methodological advancements for studying motivation as situation‐ and context‐specific, emphasizing the use of different data channels to capture the dynamic interplay between the individual‐ and group‐level aspects throughout the learning process. For educational practice, this study supports the claim that peer interactions, particularly in collaborative learning, play a crucial role in individual students' motivation.

Keywords: collaborative learning, motivation, motivation regulation, peer, socially shared regulation of learning

INTRODUCTION

In the 21st century, motivation research has increasingly paid attention to the situational and context‐specific nature of motivation (Pekrun & Marsh, 2022; Volet & Järvelä, 2001). A growing body of evidence suggests that various factors influence motivation within individuals, leading to fluctuations in situational motivational beliefs not only from one context or situation to another but also within a single learning situation (Järvenoja et al., 2023; Moeller et al., 2024; Törmänen et al., 2024). This complexity challenges researchers to consider the influence of social interactions on motivation from a more multifaceted perspective rather than viewing them as merely an external factor affecting individual motivation. Nevertheless, there is still a gap in research on how motivation forms and fluctuates within the context of collaborative learning, where group members are closely interacting with each other. Collaborative learning contexts provide a unique venue to study how peer interactions shape students' situational motivation.

Collaborative learning is a process in which two or more students work together to enhance their learning. Collaborative learning can occur through various learning activities, such as solving problems together, co‐constructing knowledge or learning new concepts. The aim is to achieve shared goals through mutual interaction, shared decision‐making and joint construction of knowledge (Dillenbourg, 1999). Unlike cooperative learning, which often involves dividing tasks among group members to be completed individually and then combined, collaborative learning emphasizes interdependence, co‐construction of ideas and reliance on each other's knowledge to address challenges and complete tasks (Stahl & Hakkarainen, 2021).

Collaborative learning environments present distinct opportunities for understanding motivation (Järvelä et al., 2010). In these settings, it is crucial to recognize both individual and group perspectives to capture the full spectrum of motivational dynamics: Individual students bring their internal motivational beliefs and personal histories to the group context, where these beliefs shape and are shaped by the interaction with other group members. This interaction between the group members, in turn, influences also the motivational states of the group as a whole (Bakhtiar et al., 2018). As the collaborative learning process unfolds, this interplay can significantly impact individual group members' situational motivation on the one hand and the group's shared motivational conditions on the other hand.

In this study, we approach motivation from the Socially Shared Regulation of Learning (SSRL) perspective. This perspective considers shared regulation of learning processes as an essential part of collaborative learning, enabling students to jointly address challenges and work towards shared goals, even in the face of difficulties. SSRL provides a framework for examining how peers can strategically influence each other's motivation through social interactions during collaboration (Hadwin et al., 2018). By focusing on SSRL, we aim to understand the mechanisms through which motivation can be regulated collectively in a group setting. This perspective allows us to explore how peer interactions within collaborative learning contexts impact students' situational motivation. By combining the analysis of socially shared regulation of motivation occurring in social interactions with individuals' subjective interpretations of the collaborative learning situation, we address the continuum between individual and group perspectives without overemphasizing one over the other in forming situational motivation.

THEORETICAL FRAMEWORK

Motivation in collaborative learning

Learning motivation refers to learners' beliefs about their ability to succeed in learning tasks (self‐efficacy), the value they place on these tasks (value), the goals they set for themselves within the learning context (achievement goals) and their persistence and effort in the face of challenges (Eccles et al., 1983; Elliot, 1999; Pajares & Schunk, 2002). Motivational beliefs are organized in hierarchies and are formed in a complex interplay of cognitive and emotional factors involving, for example, self‐perceptions of abilities, interest and goal orientations (Weiner, 1990; Wigfield & Koenka, 2020). Motivation is also metacognitive, requiring reflection and regulation of one's motivational status (Winne & Hadwin, 2008; Zimmerman & Schunk, 2001). Hence, motivation can be defined as a driving force initiating, directing and sustaining students' engagement in learning activities. It influences the intensity, quality and duration of learning efforts, namely engagement (Rogat et al., 2022).

Different core theoretical frameworks agree that motivation is bidirectional and dynamic, influenced significantly by situational and contextual factors (Nolen, 2020; Wigfield & Koenka, 2020). For example, the Situated Expectancy‐Value Theory (SEVT) extends the Expectancy‐Value Theory (EVT) by emphasizing the influence of contextual factors on motivation (Eccles & Wigfield, 2020). Whereas EVT emphasizes expectancies, values and costs as critical components influencing motivation (Eccles et al., 1983), SEVT considers social, cultural and environmental factors that shape motivational orientations through learners' beliefs, values and costs within specific learning contexts and situations. These factors can include, for example, peer interactions, classroom culture and social expectations from teachers or external sources.

Motivation theories addressing situatedness and the contextual nature of motivation, such as SEVT, are beneficial for studying motivation in collaborative learning contexts. Prior research in collaborative learning settings has shown that motivation significantly impacts group dynamics and learning outcomes, highlighting the importance of addressing both individual and group perspectives in motivation research (Järvenoja et al., 2018; Volet & Järvelä, 2001). In collaborative learning settings, where students work together to achieve common goals, motivation drives group members' willingness to participate, contribute, and persist in group activities (Rogat et al., 2013). Motivated learners are more likely to actively engage in collaborative tasks, share their ideas and collaborate effectively with their peers (Järvelä et al., 2010). Motivation influences how group members value collaboration, commit to the group's joint goals, and their willingness to invest time and effort in achieving them (Järvenoja et al., 2018). Students motivated to succeed in a collaborative task are more likely to contribute their best efforts and persevere through challenges. However, learning motivation also encompasses the perceived situation‐specific costs associated with the task (Eccles & Wigfield, 2020). One critical source of costs is caused by the efforts that learners need to invest in monitoring and controlling their learning process and progress, namely regulation of learning.

Combining the situated motivation perspective with research on SSRL provides a promising approach for research to unpack the role of motivation and its regulation within collaborative groups' joint learning process (Järvenoja et al., 2023). While the situated motivation perspective provides an understanding of individual motivational conditions, the SSRL approach allows for a nuanced understanding of how motivational dynamics unfold in real‐time in groups' social interactions, highlighting the interplay between individual beliefs and group‐level interaction processes. Interconnecting group members' beliefs and appraisals of the situation with theory on SSRL provides a gateway to consider the reason for and effects of motivation regulation from a coherent theoretical perspective. Accordingly, we will dive into motivation regulation in SSRL in more detail next.

Motivation regulation in peer interactions

According to Hadwin et al. (2018), SSRL refers to group members' collective and synchronized efforts to coordinate, monitor, and control various aspects of the learning process within a collaborative context. SSRL involves metacognitive controlling and deliberate strategic adaptation of cognition, motivation, emotions and behaviour among group members throughout collaborative activities, including planning, task enactment and reflection. In SSRL, group members negotiate, align, and adapt their perspectives and actions to achieve shared learning goals (Hadwin et al., 2018). From the viewpoint of COPES model (Winne & Hadwin, 1998), motivation is seen as a part of learners' internal conditions influencing learners' engagement and approach to collaborative learning. Motivation shapes individuals' commitment to the group's goals, their level of engagement in collaborative activities, persistence in the face of challenges and their proactive involvement in their peers' learning processes (Choi et al., 2023; Järvenoja et al., 2019; Rogat et al., 2013). Motivational beliefs can affect students' planning of collaborative learning actions, their choices on how to approach tasks and their predictions and choices on adjusting operations to enhance performance as the task progresses. Additionally, motivation is vital for evaluation as it provides the rationale for selecting standards that learners use to judge collaborative learning products. Moreover, motivation contributes to the regulation of emotions, as group members' motivational beliefs and emotional states interact dynamically to influence their collaborative interactions and decision‐making processes. In SSRL, motivation can be seen to play a dual role; it is a critical internal condition influencing several aspects of learners' regulation behaviour, but it is also a target for collaborative groups' regulation (Järvenoja et al., 2023).

Motivation regulation refers to targeted efforts to manage and adjust learners' motivational states to enhance their engagement and persistence in learning tasks (Wolters, 2003). This involves strategies such as setting personal goals, self‐monitoring progress and employing self‐reinforcement, but it can also involve cognitive and affective tactics to maintain or increase motivation (Wolters, 2003). Within SSRL, motivation regulation is extended to the group level, when group members collaboratively manage and influence each other's motivational states to optimize motivated collaboration (Järvenoja et al., 2023). Socially shared motivation regulation can be involved in all phases of SSRL, such as highlighting specific goals in shared goal setting when collaborative group work is planned or when monitoring and controlling the group's efficacy beliefs or situational interest.

When multiple collaborative partners bring together their motivational conditions, adapting them becomes central to fostering motivated collaboration (Bakhtiar & Hadwin, 2020). However, little is known about the situationally reactive motivational factors that form the core of motivational conditions that can be regulated and adapted during collaborative interactions. Few studies have aimed to investigate motivation and its variation in situ. For instance, Dietrich et al. (2017) investigated how the different SEVT components are associated with effort when measured repeatedly within and across study lessons. Their study evidenced that varying levels of effort are associated with pre‐service students' current task value (Dietrich et al., 2017). Zhou and Winne (2012) found that self‐reported achievement goals did not correlate with students' actual behaviours, as indicated by log‐file traces. Moreover, the traces of motivated behaviours were a stronger predictor of student achievement than self‐reported goals (Zhou & Winne, 2012). These studies offer indications about the motivational constructs that are reactive to situational factors and, thus, could be influenced through SSRL to change the group's motivational conditions for collaboration. However, although these studies adopted a situated perspective on motivation and related it to learning process factors, they did not consider learners' actual motivation regulation behaviours. On the other hand, studies that approach motivation from a regulated learning perspective see the central role of motivation and its regulation in SRL but tend to rely on static measures, typically asking learners to rate a motivational construct in a less situated manner (Koivuniemi et al., 2021). Consequently, we still know very little about the associations and mechanisms by which SRL influences motivation in a situation, and vice versa, how motivation influences the activation of SRL strategies. Even less is known about how motivation regulation functions as a part of SSRL occurring in groups.

Accordingly, in this study, we took an exploratory approach to investigate the interconnections between learners' situational motivation, specifically SEVT components, and SSRL in collaborative learning. We explicitly acknowledge that motivation is a subjective, internal mental state that creates conditions for learners' choices during the learning process. For example, task value can serve as a catalyst for engaging in SSRL that aims to guarantee equal participation. In situ, challenges or changes in motivational conditions can trigger group members to activate SSRL (Järvenoja et al., 2019). However, collaborative groups also need solid motivational foundations and a secure socio‐emotional atmosphere to maintain joint work (Bakhtiar et al., 2018) and to engage in SSRL in the face of challenges (Törmänen et al., 2023). Thus, there is a need to continuously establish, maintain, and strengthen motivational conditions during the collaborative learning process through socially shared motivation regulation.

AIM AND RESEARCH QUESTIONS

This study aimed to explore secondary school students' socially shared motivation regulation during a collaborative science task. It seeks to identify how students employ motivation regulation to sustain or restore favourable motivational conditions for learning together and how they perceive their peers' influence on their motivation concerning task progress.

The research questions are as follows:

  1. How frequently do groups regulate motivation as part of their socially shared regulation of learning during different phases of a collaborative learning task?

  2. How do the students perceive the influence of the other group members on their situational motivation based on (a) the repeated self‐reports on motivation regulation and (b) the stimulated recall interviews?

METHODS

Participants

The data set is part of a multimodal process data collection (Metadata available in: Dataset ‐ etsin.fairdata.fi). The participants were 95 eighth‐grade students from three upper secondary school classrooms. The age of the participants was between 13 and 16 years (mean [SD], 14.17 [.585] years). Five students did not fill in their age. Forty students specified themselves as females (42.11%), 51 as males (53.68%), none as other and four did not document their gender. The students were assigned by their teacher into 31 groups (five groups of two, 19 groups of three and seven groups of four). The students in the same groups came from the same classroom and had prior experience collaborating. In group formation, the teachers considered social relationships within the classroom, but no systematic grouping was made based on factors such as prior knowledge. Before data collection, informed consent for participating was requested from the participants, their legal guardians and the city. Additionally, the university's Ethics Committee of Human Sciences granted an ethical statement for the research. All the students participated in the collaborative work, independent of whether they consented to the study. Those who chose not to take part in data collection were grouped separately for collaborative activities, and no data were collected. To maintain the anonymity of the participants, their names were replaced with pseudonyms.

Data collection and methods

The collaborative learning task was conducted in a classroom‐like environment within the university's research lab as a part of a multidisciplinary learning period integrated into the school curriculum, including a day at the university. The task, designed in collaboration with the school's science teachers, involved creating a functional water filter using everyday supplies. It comprised four phases, each intended to engage students in applying their prior knowledge collaboratively in practice. Before the task, each group member was assigned to be an expert in one of the task phases. If the expert was missing from the group (e.g., the group had only two members) or the group did not recall how to perform the task phase, an advice card with instructions was provided. The distinct phases were designed to reflect real‐world processes in water purification and investigate how students apply theoretical knowledge in practical settings while examining their motivation and regulation during less engaging sub‐tasks.

In the first phase, air sparging, students shook a water bottle filled with dirty water for 30 s and poured it between two beakers ten times. This phase aimed to mimic real‐world processes that initiate water purification. The second phase, precipitation, required students to add the correct amount of potassium alum to the dirty water and mix it for 5 min. This phase simulated the chemical reactions used in water treatment to aggregate and remove impurities. In the third phase, observation, students observed the mixture and documented the changes every 5 min. This phase was deliberately designed to be slow and less engaging to challenge students to maintain motivation during the monotonous learning phase, thus potentially creating a need for motivation regulation. In the final phase, filtration, students put together the water filter and cleaned it with two litres of water. They then poured the dirty water mixed with potassium alum through the filter. After filtration, students photographed the dirty and filtered water and the constructed filter for further analysis back at school. The entire collaborative task lasted 90 minutes. The mean durations for each phase were as follows: air sparging 7 min, precipitation 10 min, observation 12 min and filtration 29 min. Collaborative work was video recorded, with audio captured using group and individual microphones. During the task, each group member repeatedly evaluated how the group as a whole influenced their motivation during the preceding task phase using a 0‐to‐100 sliding scale, ranging from ‘Not at all’ (0) to ‘Fully’ (100). This single‐item self‐report questionnaire aimed to capture students' perceptions of group‐level regulation behaviours, including both co‐regulation and socially shared regulation of motivation, as reflected in their individual experiences. The instructions prompted students to consider the collaborative nature and group‐level dynamics of the activity in their responses. As motivation is ultimately a subjective experience, both socially shared regulation, which aims to restore, maintain or strengthen motivated learning within a group, as well as co‐regulation, which is directed at influencing group members' motivation in the situation, may ultimately impact individual group members' motivation. The evaluations were conducted at four‐time points after each of the task phases. Additionally, a researcher observed the learning situation throughout the task. Immediately following the collaborative task, stimulated recall interviews were conducted to gain a deeper understanding of how the students perceived the influence of their peers on their motivation during the collaboration. Each participant was individually interviewed by the researcher who had observed the group task. The interviews, lasting between 10 and 20 minutes, included a stimulus phase where students were presented with visualizations derived from repeated self‐reported data (see Figure 1). These visualizations illustrated the trends in the students' evaluations of how they perceived their group members influenced their motivation, indicating the role of SSRL and CoRL for situational motivation. The visualizations showed consistent high or low levels and any significant changes in the students' interpretations. If notable shifts (>10) in the students' interpretations were observed between different task phases, the interviewer probed further to understand the reasons behind these changes. A threshold of 50 was used to differentiate between high and low levels, providing a clear basis for discussion about the impact of SSRL on the student's motivation during the collaborative learning task.

FIGURE 1.

FIGURE 1

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Examples of visualizations used as stimuli in the interviews. The first example shows a case with no notable shifts in a student's situated motivation reports. In contrast, the second example illustrates an increase in reported motivation from the first to the second task phase.

Data analysis

Qualitative video coding

From the video recordings, the SSRL episodes where the students were together controlling their cognition or motivation were identified using the Observer XT software (Noldus Information Technology). An SSRL episode was identified as a segment of interaction where two or more group members actively controlled their joint cognitive learning processes, motivation or emotions. An episode started when a group member initiated a regulatory behaviour and ended when the focus shifted from regulation to other interactions. The coding included three categories for the regulation target: cognition, motivation and both targets. Controlling cognition was coded when the group members joint regulation efforts were targeted to influence their current task understanding, goals and plans, or study tactics and strategies (e.g., ‘Ella, would you take a look at the advice card, and see if we had to do just that?’). Controlling motivation was coded when the group members attempted to influence the group's current emotional or motivational conditions (e.g., ‘Come on girls, we can do this!’). Controlling both targets was coded when the regulation episodes included controlling both cognition and motivation simultaneously, or these actions followed one another (e.g., ‘We don't use it (the advice card), the aim is to use a zero card, we need to get a good grade’). Interrater reliability for video coding was assessed using Cohen's kappa, achieving a good agreement (κ = .73).

Statistical analyses for the video and repeated self‐report data

The occurrence of cognition and motivation regulation in the four task phases was examined with a chi‐squared test of independence. Significant relationships were further explored with significant z scores from adjusted residuals with alpha levels of .05 (z > 1.96) and .01 (z > 2.58). Linear mixed effects models (LMMs) were utilized to analyse how the other group members influenced students' motivation in the different task phases using repeated self‐report data. In particular, the LMMs were used to analyse the effect of the task phase on students' appraisals of motivation regulation. LMMs can be used to model data with hierarchical structures, as in this case, the data include repeated observations of the same participants working in small groups (Meteyard & Davies, 2020). The models can include the fixed effects of independent variables and individual differences in the baseline level of each fixed effect at each hierarchical level by using random intercepts (Baayen et al., 2008). They can also account for variations in the magnitude of each fixed effect on each hierarchical level through random slopes (Baayen et al., 2008). The lme4 package version 1.1‐26 (Bates et al., 2015) in R version 4.4.0 (R Core Team, 2020) was used to fit the models. Following best practices presented by Barr et al. (2013), we started with the maximal random effect structure, including task phase as a fixed effect (treated as a factor, using Phase 1 as the reference value) and random slopes and intercepts for group and student (nested within the group). Then, the complexity of a given model was iteratively reduced until models could converge (Barr et al., 2013). The final model included a fixed effect for phase and random intercepts for group and student. Table 2, which reports the fixed effect of Phase, also reports the full model syntax and statistical summary of the random effect of the final model to improve reproducibility.

TABLE 2.

Summary of fixed and random effects for motivation regulation—Linear mixed effects model.

Model: Motivation regulation = phase + (1|group) + (1|ID)
Fixed effects Unstandardized β SE β t Statistic p
Intercept 64.09 3.80 16.85 .000***
Phase 2 −.72 2.19 −.33 .74
Phase 3 2.57 2.19 1.17 .24
Phase 4 6.56 2.19 2.99 .003**
Random effects
Groups Variance SD
Student (intercept) 418.4 20.46
Group (intercept) 233.5 15.28
Residual 228.0 15.10
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**

p < .01.

***

p < .001.

Stimulated recall interviews

Finally, the transcribed stimulated recall interviews were analysed with inductive content analysis to complement the statistical analyses qualitatively and to provide insights into students' retrospective interpretations of how other group members influenced their motivation in practice. The analysis began by dividing the interview responses into two categories based on the change in the situated self‐report ratings. The categories were students' high or increasing responses explaining why other group members succeeded in motivating them considerably (more) than in the previous phases and low and decreasing responses explaining other group members' role for low or decreasing interpretations. Next, the student's responses related to high or increasing responses, which were considered to indicate successful socially shared motivation regulation, were inductively coded into sub‐categories explaining qualitatively different ways or strategies the other group members influenced the motivation in the situation. The responses could be placed into multiple relevant categories if they included several reasons. Altogether, five categories emerged.

RESULTS

How do the groups regulate motivation as a part of their SSRL during a collaborative learning task?

In general, the groups' collaboration included 1174 episodes, where the group members activated regulation of cognition (f = 792), motivation (f = 218) or both targets (f = 164). That is, overall motivation regulation was rare compared to the regulation of cognition. When the occurrence of regulation in the four task phases was examined (Table 1), it was discovered that there were significant differences in how regulation emerged concerning the task progress (ꭓ 2(6) = 21.864, V = .096, f = 1174, p = .001). Significant relationships were further explored with significant z scores from adjusted residuals with alpha levels of .05 (z > 1.96) and .01 (z > 2.58). Regulation of cognition was activated more than expected at the beginning of the task in Phase 1 (z = 2.6, p < .01) and at the middle of the task in Phase 3 (z = 2.9, p < .01), but less than expected at the end of the task in Phase 4 (z = −2.5, p < .05). In turn, regulation of motivation appeared more than expected in Phase 2 (z = 2.3, p < .05) and less than expected in Phase 3 (z = −2.0, p < .05). In the same regulation episode, regulation could be targeted to both cognition and motivation, which occurred more than expected at the last part of the task in Phase 4 (z = 2.8, p < .01) and less than expected at the beginning of the task in Phase 1 (z = −2.2, p < .05).

TABLE 1.

Frequencies of the different targets for regulation and adjusted residuals with the different task phases.

Target Task phase Codes in all
Phase 1 Phase 2 Phase 3 Phase 4
f % z f % z f % z f % z f
Cognition 88 78.6 2.6** 124 62.6 −1.6 148 76.3 2.9** 432 64.5 −2.5* 792
Motivation 16 14.3 −1.2 48 24.2 2.3* 26 13.4 −2.0* 128 19.1 .5 218
Both 8 7.14 −2.2* 26 13.1 −.4 20 10.3 −1.6 110 16.4 2.8** 164
Total 112 100.0 198 100.0 194 100.0 670 100.0 1174
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*

p < .05.

**

p < .01.

How do the students perceive the influence of the other group members on their situational motivation based on the repeated self‐reports on motivation regulation?

Overall, the students (N = 95, f = 380) reported that the other group members motivated them moderately (M = 65.13, SD = 29.64, min = .00, max = 100.00). A significant effect of the task phase (Unstandardized β = 64.09, SE β = 3.80, t = 16.85, p = .00) was found, suggesting that the students' appraisals varied across the task phases. Phases 2 and 3 did not show significantly different ratings from Phase 1 (Unstandardized β = −.72, SE β = 2.19, t = −.33, p = .744 and Unstandardized β = 2.57, SE β = 2.19, t = 1.17 p = .242, respectively), while the rating of Phase 4 was significantly different from the rating of Phase 1 (Unstandardized β = 6.56, SE β = 2.19, t = 2.99, p = .003). That is, the students perceived that the other group members were able to motivate them significantly more in Phase 4 (M = 69.59, SD = 28.37) than in Phase 1 (M = 63.03, SD = 29.13). The random effects indicated that most of the variance was attributed to the individual students (418.4, SD = 20.46). However, the group also explained some variance (233.5, SD = 15.28). The residual variance, representing unexplained variability within individuals, was estimated at 228.0 (SD = 15.10). This model was compared to a null model predicting motivation regulation from only group and individual differences. A chi‐squared difference test revealed that our model had a significantly improved fit compared to the null model χdiff2(3) = 13.34, p = .004.

How do the students perceive the influence of the other group members on their situational motivation based on the stimulated‐recall interviews?

To gain deeper insights into the students' perceptions of the role of peers for their situational motivation, we explored their overall elaborations on the influence of other group members. Two distinct categories emerged in the qualitative inductive content analysis: direct motivation regulation strategies and indirect ways for motivation regulation. Overall, the students mentioned only a few indications of direct motivation regulation strategies aiming to make a strategic change to motivation in the situation. In turn, descriptions were more prevalent concerning indirect ways of motivation regulation, that is, social interactions aimed at establishing, maintaining, and strengthening motivational foundations and a secure socio‐emotional atmosphere. These indirect ways were enacted without immediate situational challenges to maintain and promote the group's current motivational state.

Specifically, as presented in Table 3, the direct motivation regulation strategies described by the students in the interviews included efficacy management (f = 10) and interest enhancement (f = 2). Efficacy management, as a socially shared motivation regulation strategy, involved descriptions of encouragement in situations where the group or some members began to doubt their ability to perform well. These explanations of strategy use also involved descriptions of situations where efficacy was regulated to complete the water purification task, as well as praising and providing positive feedback when challenging situations were overcome. These type of strategies are demonstrated in interview responses like, ‘I gained confidence from others to keep working’ and ‘Team members provided support to each other when the task didn't progress perfectly’. Interest enhancement, mentioned only two times by the students, pointed directly to the strategic aim of influencing situational interest to support motivated work. Students did not describe other motivation regulation strategies commonly identified in prior research on SRL. Accordingly, these two types combined solely formed the category of direct motivation regulation strategies (f = 12).

TABLE 3.

The frequencies of direct and indirect motivation strategies.

Motivation regulation strategies Frequency (f)
Direct motivation regulation strategies 12
Efficacy management 10
Interest enhancement 2
Indirect motivation regulation strategies 111
Equal participation 51
Building or maintaining a positive socio‐emotional atmosphere 40
Functional social interaction 20
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Contrary to the student's inability to recall hardly any use of direct motivation regulation strategies, their description of indirect motivation regulation were versatile, resulting in three subcategories that described how processes involved in SSRL, such as reciprocal, smooth social interactions and functioning group work, influenced situational motivation (Table 3). Indirect motivation regulation strategies involved descriptions of equal participation (f = 51), where students described how active participation and engagement of all the group members positively influenced their motivation. For example, one student mentioned, ‘It motivated me that everyone participated in the task’. These descriptions were related to functional ways of working, sharing responsibilities and helping each other. Another subcategory, Building or maintaining a positive socio‐emotional atmosphere (f = 40), involved acknowledgments of positive emotions, team spirit and an encouraging atmosphere, as illustrated by a student's comment: ‘The group had fun and a good vibe’. Furthermore, functional social interaction (f = 20) included explanations of good communication among the group members as noted in the following statement by a student describing communication during collaboration: ‘it was easy to communicate with group members’. These descriptions collectively comprised the indirect ways for motivation regulation (f = 111).

DISCUSSION

Peers influence students' motivation in secondary school (Daumiller & Hemi, 2025; Hemi et al., 2023; Kilday & Ryan, 2022). This influence is particularly highlighted in collaborative learning settings, which inherently fosters peer interdependence (Järvelä et al., 2021). This study explored secondary school students' socially shared motivation regulation during a collaborative science task. Employing two distinct perspectives and drawing on three data sources, the analysis shed light on actualized motivation regulation among peer interactions during collaborative learning and its connection with the peer influence experienced by the students in the situation. First, motivation regulation on a group level was examined in relation to overall SSRL manifested across the task phases. Subsequently, the students' self‐reported appraisals on how well their group members succeeded in motivating them during the task were analysed. Finally, the stimulated recall interview data were utilized to gain deeper insight into peer influence on the student's motivation and the nature of motivation regulation identified by the students. Together, these findings offer an understanding of motivation regulation in collaborative learning and as part of SSRL, encompassing both peer interactions and individual students' perceptions.

It has been found that motivation regulation is less common than cognitive regulation. This is consistent with previous studies that have shown cognitive regulation to be more prevalent in collaborative learning scenarios (Haataja et al., 2022; Koivuniemi et al., 2018). However, the relative rarity of motivation regulation does not mean it is insignificant. Despite being less frequent, instances of motivation regulation are argued to be crucial for sustaining learning, group engagement and task persistence (Järvenoja et al., 2023). Previous studies have shown that when students engage in motivation regulation at the beginning of a task, it helps to create a positive social and emotional atmosphere that supports groups' sustained shared regulation of learning in later phases and helps the groups to maintain motivation (Törmänen et al., 2023). Nonetheless, this study suggests that secondary school students may have difficulties engaging in motivation regulation early on. When examining how motivation regulation occurred across different task phases, it was found that cognitive regulation was more likely to occur in the initial phase. While this is understandable, as the initial phases of the task typically involve planning and developing a shared task understanding through sustained shared regulation of learning (Hadwin et al., 2018), the lack of motivation regulation is still concerning, as previous research has shown that motivation can fluctuate in both directions (Moeller et al., 2022). Engagement in controlling motivation can lead to higher motivation, but disengagement may lead to reduced motivational commitment (Rogat et al., 2022).

It is important to recognize that treating SSRL targeting motivation and cognition as separate processes can lead to a biased understanding of motivation regulation in collaboration. Collaborative learning involves complex cognitive, motivational and emotional regulation processes that are intertwined and challenging to separate, particularly in authentic learning interactions (Hadwin et al., 2018). Recent studies indicate that these processes are highly interconnected, especially when observed in collaborative learning interactions (Huang & Lajoie, 2023; Järvenoja et al., 2020; Zabolotna et al., 2025). This study found that motivation regulation was more impactful when aligned with cognitive regulation, particularly in the final stage of the task. This suggests that situational demands, such as the need to successfully complete a task, significantly influence how students perceive and use motivational strategies. Furthermore, the results indicate that when motivation and cognitive regulation aligned, students were more likely to perceive the role of their peers as more influential in motivating them, further highlighting that effective SSRL involves various regulation targets in reciprocal interaction.

The students did not recognize many direct motivation regulation strategies in the interviews, although socially shared motivation regulation could be observed from the videotaped social interactions. This might partly explain why motivation regulation seems to appear rarely compared to cognitive regulation: activation of SSRL requires metacognitive awareness of the need for regulation as well as the ability to initiate appropriate strategies according to situational needs (Haataja et al., 2022; Järvenoja et al., 2019). The results of this study imply that secondary school students might lack strategic information on motivation regulation as a part of their learning skills. Instead of the direct motivation regulation strategies, the students highlighted good communication, group dynamics and social relationships, all critical for SSRL and successful collaborative learning (Er et al., 2021; Kreijns et al., 2022; Vogel et al., 2016). Prior research supports this by showing that students who receive support and positive interactions from peers are more likely to exhibit higher levels of motivation and engagement in learning activities (Er et al., 2021; Shao et al., 2024). Positive peer relationships also enhance motivation by providing support, encouragement, and a sense of belonging (Martin & Dowson, 2009). In this study, we refer to these characteristics as indirect ways for motivation regulation, suggesting that these characteristics emerge from a combination of motivational, emotional, cognitive, and social factors, continuously forming as a part of the SSRL cycle (Bakhtiar & Hadwin, 2020; Hadwin et al., 2018). However, more research is needed to unpack how these characteristics of group interactions are built through SSRL. For example, it remains unknown whether the lack of common task understanding at the beginning of the task hinders students from establishing motivational grounds for collaboration or recognizing their joint motivational challenges and how this is mitigated through SSRL, including motivation regulation. If this is the case, overall support for goal setting and building shared understanding, which is shown to be effective for learning and collaboration, could be enhanced with motivational support for the collaborative groups, not just with direct prompting of goal setting (Bakhtiar et al., 2018; Janssen & Kirschner, 2020; Järvelä et al., 2016; Volet et al., 2009).

Finally, the results of this study suggest that examining momentary and fluctuating states of motivation can provide valuable insights into how motivation forms and evolves on a micro‐level as learning unfolds. This is particularly evident in this study, which focused on understanding how sharing regulation responsibilities with peers influences students' situational motivation during collaborative learning. By asking students to evaluate their peers' influence on their motivation at multiple time points during the task, the study captured situational motivational perceptions shaped by immediate group dynamics, emotional states, and task‐specific challenges. This approach allowed an exploration of how real‐time group‐level regulation episodes relate to micro‐level fluctuations in motivational conditions (e.g., Bakhtiar & Hadwin, 2020; Järvenoja et al., 2023; Järvelä & Hadwin, 2024).

Although theories on the regulation of learning often emphasize motivation's role in planning and reflection phases, motivation also functions during the execution phase of regulated learning (Pintrich, 2000; Zimmerman & Schunk, 2008). The findings of this study illuminate this less investigated phase by focusing on the momentary motivational shifts and their relationship with regulatory behaviours during the execution phase of the task. While post‐task reflections might provide more accurate judgements of motivation when considering task completion, goal attainment and attributions related to success or failure (Pintrich, 2000; Weiner, 1985), understanding fluctuations in context offers insights into the mechanisms through which group interactions shape motivation in real time. These micro‐level processes are critical for creating conditions that sustain engagement on the fly (Rogat et al., 2022).

However, motivation is inherently complex and assessing it only on one granularity level, in here during an ongoing collaborative task, does not reflect its broader dynamics (Järvenoja et al., 2018; Kaplan & Flum, 2009). It should be noted that this study does not consider, nor does it attempt to consider, the cumulative or long‐term effects of motivation regulation on motivation. Furthermore, it does not capture students' attributions regarding learning success or their overall collaborative learning experience. It remains for future research to explore how students' judgements of motivation evolve over time by comparing in‐task evaluations, post‐task reflections and longitudinal changes across collaborative tasks. Additionally, integrating assessments of learning outcomes as a reference point for motivational judgements and attributions could provide a more comprehensive understanding of how peers influence motivation and how joint regulation in collaborative groups could contribute to longer term motivational development.

This study also has limitations that should be considered. First, while we found statistically significant results, it is vital to acknowledge the low effect size observed concerning how regulation emerged in relation to task progress. Future studies should provide more evidence on the role of motivation regulation in the progress of collaborative learning before drawing further interpretations. That is, the variation in regulation between the task phases in this study can be very context‐specific and, thus, might not have generalizable significance for educational practice. Also, we did not analyse the different motivation regulation strategies from the video‐recorded peer interactions and, therefore, could not compare how well they aligned with the students' self‐report and interview responses. While most of the explained variance in peer influence was attributed to the individual students, the group also explained some variance. This may be due to various factors, including group composition in terms of size (2–4 students per group), general motivation towards science, motivation regulation skills, and prior knowledge—factors that were not addressed in the present study. Additionally, while it is justified to assume that the self‐report item used in this study captured both co‐regulation and socially shared regulation of motivation, its phrasing may have led students to focus more on the former. The instructions aimed to mitigate this by emphasizing the group's collaborative nature. However, as we still know very little about motivation regulation strategies at the group level, the students' perceptions provide essential insights on observing motivation regulation in social interactions as well: the results imply that the socially shared motivation regulation strategies could be more multidimensional than self‐regulation strategies previously defined, also encompassing dimensions of cognitive and emotion regulation. Future studies could explore this viewpoint in more detail. Moreover, the influence of the amount of actualized group‐level motivation regulation on the students' repeated self‐report responses was not directly analysed because the frequency of motivation regulation could not be used in the analysis without acknowledging the different lengths of the phases and the number of potential challenges the groups faced during their learning process. This could have led to an invalid assumption of unmotivated collaboration: if the groups progressed smoothly without significant challenges, motivation regulation might not be frequently necessary.

To conclude, socially shared motivation regulation and socio‐emotional interactions can adapt and shape students' motivational conditions during collaborative learning (Bakhtiar et al., 2018), but so can cognitive and social factors of (S)SRL (Feraco et al., 2023). That is, not only the direct attempts at motivation regulation can improve or restore students' motivation, but also many other vital processes involved in SSRL, such as reciprocal communication, negotiation and shared engagement. This was evident in the students' appraisals, where they associated many cognitive and social aspects of the collaborative task with their high or increasing situational motivation. Surprisingly, while peers' influence on establishing and maintaining learning commitment is one of the most critical factors for secondary school students' motivation, we still know very little about how motivation regulation functions during collaboration. We call for more research in the future to unpack how motivation regulation is shared across different phases of collaborative learning and to identify the challenges that prevent groups from supporting each other's motivation during specific phases of the regulated learning cycle.

CONCLUSION

This study showed that, consistent with the subjective nature of motivation, most of the variation in the students' appraisals of their peers' influence on their motivation was attributed to the individual student (Schunk & DiBenedetto, 2020). However, the results also showed that peers within collaborative groups played a meaningful role in students' situational motivation. Thus, this study highlights balanced attention to both individual and group levels when studying motivation and its regulation in collaborative groups. Motivation should be understood in context, where individual students' internal conditions influence their appraisals in learning situations and their decisions on how to take part in regulating those conditions with others (Järvelä et al., 2010). In turn, through both cognitive and motivational aspects of SSRL, peers can influence an individual's internal motivational conditions and adapt the group's overall motivational state. This study contributes to the methodological development for studying motivation as situation‐ and context‐specific, emphasizing using different data channels to capture the dynamic interplay between the individual and group‐level aspects throughout the learning process (Daumiller & Hemi, 2025).

For educational practice, this study supports the claim that peer interactions, particularly in collaborative learning, play a crucial role in individual students' motivation. The results suggest that along with specific motivation regulation strategies, positive socio‐emotional interactions, active participation, good communication and shared engagement in collaborative learning processes can increase students' motivation during the learning process. Since more stable motivational beliefs are built at the situational level (Eccles & Wigfield, 2020), facilitative peer interactions in collaborative learning could potentially shape students' long‐term motivational trajectories. Collaborative learning could be particularly influential for students with low motivation when they can learn and interact with their highly motivated peers. While the results of this study suggest that the students themselves may naturally focus more on the cognitive aspects of collaborative learning, effective collaboration requires deliberate efforts to address and regulate motivation to support cognitive processes (Hadwin et al., 2018). This underscores the importance of raising students' awareness of motivation regulation strategies in educational practice to enhance SSRL, improve overall group performance and collaborative learning outcomes and provide motivating collaborative learning experiences.

AUTHOR CONTRIBUTIONS

Hanna Järvenoja: Conceptualization; funding acquisition; investigation; writing – original draft; writing – review and editing; supervision; resources; project administration; methodology. Tiina Törmänen: Conceptualization; investigation; methodology; validation; formal analysis; writing – review and editing. Emma Lehtoaho: Visualization; formal analysis. Marjo Turunen: Investigation; writing – review and editing. Jasmiina Suoraniemi: Writing – review and editing; data curation. Justin Edwards: Formal analysis.

CONFLICT OF INTEREST STATEMENT

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

ACKNOWLEDGEMENTS

We would like to thank the schoolteachers, the principal, and the participating students for their collaboration and for making this research possible. This work was supported by the University of Oulu and the Research Council of Finland project [348765] and Profi 7 [352788]. Data collection was carried out with the support of LeaF Research Infrastructure (https://www.oulu.fi/leaf‐eng/), University of Oulu, Finland. Open access publishing facilitated by Oulun yliopisto, as part of the Wiley ‐ FinELib agreement.

Järvenoja, H. , Törmänen, T. , Lehtoaho, E. , Turunen, M. , Suoraniemi, J. , & Edwards, J. (2025). Investigating peer influence on collaborative group members' motivation through the lens of socially shared regulation of learning. British Journal of Educational Psychology, 95, 1063–1079. 10.1111/bjep.12754

[Correction added on 24 October 2025, after first online publication: The copyright line was changed.]

DATA AVAILABILITY STATEMENT

The meta data that support the findings of this study are available at https://etsin.fairdata.fi/dataset/b297b907‐3103‐405c‐b767‐37ae576f5f3a.

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Associated Data

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Data Availability Statement

The meta data that support the findings of this study are available at https://etsin.fairdata.fi/dataset/b297b907‐3103‐405c‐b767‐37ae576f5f3a.

Articles from The British Journal of Educational Psychology are provided here courtesy of Wiley

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