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Abstract

This study aims to enhance socio-emotional awareness and regulatory processes in small ensemble rehearsals through the design and implementation of a macro-level collaboration script. Developed using design-based research principles, the script is grounded in the cyclical model of regulated learning ( Hadwin et al., 2018; Zimmerman & Campillo, 2003) and incorporates prompts adapted from Näykki et al. (2017) for ensemble contexts. Five student ensembles from a higher music education institution in Norway participated in the study. Ensembles using the script (n = 3) were compared with baseline ensembles without it (n = 2). Data from video recordings and focus group discussions were analyzed using qualitative content analysis. The findings indicate that the script effectively supports socially shared regulation of learning by fostering collaborative planning, shared task and emotional monitoring, and reflective dialogue. Scripted ensembles consistently engaged in all regulated learning phases (planning, monitoring, and reflection) and exhibited greater socio-emotional awareness through more equitable participation. Notably, both conditions spent less than half of the rehearsal time on actual performance, highlighting the central role of verbal interaction in ensemble rehearsals. These findings suggest the collaboration script serves as an effective scaffold for enhancing socio-emotional dynamics and group-level regulation, offering valuable insights for improving student-led ensemble rehearsals. Further research is needed, however, to refine its design, particularly in balancing structured guidance with the fluid, creative dynamics essential to musical collaborations.

Keywords

Emotion regulation macroscripts rehearsals small music ensembles socially shared regulation of learning

Introduction

The organizational structure of a small ensemble within higher music education (HME) mirrors other collaborative learning environments, where students work toward shared goals by exchanging diverse perspectives and expertise (Pennill & Breslin, 2021; Rumiantsev et al., 2023). Similar to these environments, ensemble rehearsals are underpinned by collective goals, interdependence, and co-construction of knowledge (Gaunt & Treacy, 2020; Gilboa & Tal-Shmotkin, 2012; Järvenoja et al., 2024). As Gaunt and Treacy (2020) note, “teamwork and collaborative learning are ubiquitous in the arts, central to what is often termed ‘ensemble’ activity” in which musicians utilize a comprehensive “set of kinaesthetic, cognitive, imaginative, and emotional skills” (p. 421).

Unlike traditional classroom settings that tend to rely on direct teacher involvement, ensemble rehearsals often require students to engage in self-directed group work. Over an academic semester, rehearsals provide opportunities for students to develop cohesive musical interpretations, address performance challenges, and learn through peer interactions (Ginsborg, 2017; King, 2021; Nielsen & Johansen, 2021). These interactions are shaped not only by verbal communication but also by non-verbal cues such as gestures and glances that influence performance dynamics (Biasutti et al., 2016; Bishop et al., 2023; King & Gritten, 2017; McCaleb, 2017; Pennill & Timmers, 2022).

A defining characteristic of small ensembles is their self-managed structure, which is inherently imbued with a strong socio-emotional component (Hakkarainen, 2016; Kokotsaki, 2007; Timmers et al., 2021). In contrast to large ensembles with designated leadership, self-managed groups negotiate internal dynamics by balancing individual autonomy with collective goals; this necessitates a higher degree of consensus building and effective communication (Gilboa & Tal-Shmotkin, 2012; Pennill & Breslin, 2021; Volpe et al., 2016). Although small ensemble participation fosters musicianship, technical skills, and social belonging (Sætre & Zhukov, 2022; Slette, 2019; Tahirbegi, 2023b), these benefits do not automatically guarantee effective collaboration (Brandler & Peynircioglu, 2015; Creech & Hallam, 2017). The absence of formal leadership and the reliance on peer interactions make small ensembles particularly vulnerable to challenges such as misaligned expectations, conflicting personalities, and priorities (Davidson & King, 2004; Hill et al., 2018; Tahirbegi, 2023b). Although some disagreements can foster group cohesion and stimulate creativity (e.g., Murnighan & Conlon, 1991), without effective strategies to manage these dynamics, such conflicts can disrupt collaboration and hinder productivity.

Previous studies have identified various ways to facilitate small ensemble work; however, most focus primarily on the teacher's role—often depicting the teacher as, for example, a gatekeeper who controls the flow of rehearsals or a fellow traveler who collaborates alongside students (Creech & Hallam, 2017). Because many ensemble rehearsals occur without continuous teacher involvement, there is a heightened need for self-directed facilitation. However, little is known about how students can autonomously use scaffolding tools to navigate rehearsals and collaborate effectively on their own.

To address this gap, it is pertinent to draw on insights from the broader literature on collaborative learning, which highlights the importance of structured support mechanisms in fostering productive group interactions (Järvenoja et al., 2020). One key construct is regulated learning, defined as “intentional, goal-directed metacognitive activities where learners and groups strategically control their cognitive, behavioural, and affective actions within dynamic social interactions” (Miller & Hadwin, 2015, p. 574). Applying this framework to ensemble rehearsals offers a promising avenue for enhancing various aspects of student-led rehearsals. The current study presents a theoretically guided collaboration script designed to support regulated learning and socio-emotional awareness in small ensemble rehearsals, providing a novel framework for enhancing student-led collaborations.

Small Group Dynamics: The Intersection of Regulation and Emotions

Small group work in educational settings often serves as a catalyst for emotional expression, shaping both individual self-perceptions and group dynamics (Bakhtiar et al., 2018). Such groups have even been described as epicenters of emotional activity (Van Kleef & Fischer, 2016), a characterization that resonates strongly with ensemble settings, where artistic identity is deeply intertwined with the sense of self (Hargreaves et al., 2018). In these contexts, the intensely personal nature of musical collaboration amplifies socio-emotional dynamics, complicating the negotiation between individual agency and group cohesion (Gilboa & Tal-Shmotkin, 2012; Schiavio et al., 2024). As a result, sustaining collaboration in small ensembles, requires not only cognitive regulation but also attending to interpersonal and emotional dynamics (Tahirbegi, 2023b).

Emotions influence collaboration both before and during group activities (Järvenoja et al., 2024) and they are expressed through socio-emotional interactions (Mänty et al., 2020). Positive interactions, such as encouragement and inclusion have been found to facilitate collaboration (Järvenoja et al., 2019; Rogat & Adams-Wiggins, 2015), whereas negative interactions, like judgment, may hinder it (Linnenbrink-Garcia et al., 2011; Näykki et al., 2014). Emotion regulation (ER) is essential for productive collaboration, involving not only the ability to manage one's own emotional responses but also attunement to the emotional states of peers and the collective needs of the group (Järvenoja et al., 2024). In collaborative settings, metacognitive processes support ER by helping students detect when emotional disruptions, particularly negative affect, interfere with task focus or group cohesion (Sobocinski et al., 2020). Crucially, the shared recognition of emotionally salient moments often serves as a trigger for group-level regulation to emerge (Järvenoja et al., 2024; Mänty et al., 2023; Törmänen et al., 2023). These dynamics underscore the importance of equipping learners with the skills to monitor, interpret, and adapt to socio-emotional cues in real time, laying the groundwork for emotionally attuned and productive collaborative learning (Järvenoja et al., 2024).

Modes of Regulation

Researchers have identified at least three types of regulation in collaborative learning contexts: self-regulation, co-regulation, and socially shared regulation of learning (Hadwin et al., 2018; Järvenoja et al., 2024).

Self-regulation of learning involves individuals actively regulating their cognition, behavior, motivation, and emotions during group tasks, thus taking responsibility for their engagement and learning. It is characterized by its metacognitive and agentic nature (Hadwin et al., 2018; Zimmerman & Campillo, 2003). Co-regulation of learning is a dynamic process where regulation is facilitated by interactions and resources, such as peers, tools, and the learning environment, promoting individual self-regulation or transitioning towards shared regulation. It encourages strategic planning, execution, reflection, and adaptation (Bakhtiar & Hadwin, 2022; Hadwin et al., 2018). Lastly, socially shared regulation of learning occurs when groups collectively manage the task through negotiated adjustments to their cognitive, behavioral, motivational, and emotional states (Järvenoja et al., 2015). This form of regulation is transactive, highly metacognitive, and collectively agentic, with regulation jointly constructed by group members (Hadwin et al., 2018).

Although these regulatory processes are critical in collaborative learning contexts, students often lack the skills to apply them effectively in practice (Järvenoja et al., 2024; Miller & Hadwin, 2015; Törmänen et al., 2023). Inadequate regulation can lead to conflicts and lead to a decline in the group's socio-emotional climate (e.g., Koivuniemi et al., 2018; Mänty et al., 2023). While instructional tools supporting regulation have shown promise in broader educational contexts (e.g., Järvenoja et al., 2020; Miller & Hadwin, 2015; Näykki et al., 2021), their application in HME remains limited—a gap the current study seeks to address.

Supporting Collaboration with Scripts

Collaboration can be enhanced through scripts that support learners in acquiring domain knowledge and regulatory skills (Miller & Hadwin, 2024). These scripts offer structured guidance for task engagement and help reduce potential disruptions during group work (Weinberger et al., 2005). Earlier scripts mainly targeted cognitive processes, such as fostering argumentation skills (Scheuer et al., 2014), more recent approaches increasingly incorporate prompts that support the regulation of affective aspects of group work (e.g., Järvenoja et al., 2020; Näykki et al., 2021; Rojas et al., 2022).

Collaboration scripts vary in design, educational objectives, and granularity (Kobbe et al., 2007). Micro-scripts provide detailed, step-by-step instructions using targeted prompts (e.g., “My theory is…”) to guide specific learning activities and outcomes. Macroscripts, in contrast, serve as flexible frameworks that broadly shape interactions and communication patterns while emphasizing the learning process over predetermined outcomes (Wang et al., 2017). This adaptability makes macroscripts particularly well-suited for dynamic learning environments like small ensemble rehearsals.

Structured approaches that resemble collaboration scripts have been used to support self-regulation of learning in music education (e.g., McPherson et al., 2019; Miksza et al., 2018; Miksza & Brenner, 2023; Osborne et al., 2021). For example, the self-regulation microanalysis approach is implemented to heighten learners’ metacognitive monitoring and planning through real-time reflections (Cleary & Callan, 2018). Research in instrumental practice has leveraged such methodologies: McPherson et al. (2019), for example, used interviews and practice diaries to identify self-regulation profiles among piano students, while Miksza & Brenner (2023) and Osborne et al. (2021) demonstrated that practice diaries enhance metacognitive monitoring and regulation through goal setting.

While these studies focus on individual processes, the current study extends these principles to collaborative contexts. It introduces a novel adaptation of a collaboration script to small ensemble contexts, addressing both individual and shared regulation within rehearsals. The following research questions guide the inquiry:

RQ1: How do ensembles engage in collaborative interactions during rehearsals?

RQ1.1: In what ways did ensembles differ in their collaborative interactions during rehearsals?

RQ1.2: In what ways were socio-emotional aspects evident in ensembles’ collaborative interactions during rehearsals?

RQ2: How did the macroscript influence group members’ collaborative interactions related to aspects of socially shared regulation of learning?

Methodology

This study employs design-based research (DBR) principles to design, implement, and evaluate a macroscript intervention aimed at enhancing regulated learning and socio-emotional awareness in small ensemble rehearsals. As a meta-methodology, design-based research integrates design and research methods through iterative cycles of design, enactment, analysis, and refinement, generating practical interventions and theoretical models grounded in authentic learning contexts (Collective, 2003; Sandoval & Bell, 2004).

The macroscript intervention builds on a larger project investigating ensemble rehearsals, with a specific focus on emotion regulation, collaboration, and small ensemble dynamics. Preliminary findings from the first phase of this project highlighted the need for structured support to foster regulated learning and socio-emotional awareness during student-led rehearsals (see Tahirbegi, 2023a, 2023b). In response, the macroscript was designed to support key phases of regulated learning—planning, monitoring, and reflection—while providing an adaptable framework that would allow students to address socio-emotional dynamics as they emerge within the rehearsal process.

The development of the macroscript was guided by the DBR framework outlined by Easterday et al. (2018), following a recursive process of focusing, understanding, defining, conceiving, building, testing, and presenting. Rather than adhering to a rigid sequence, these phases were applied flexibly to accommodate the dynamic nature of ensemble rehearsals. Table 1 provides an overview of the macroscript's development, evaluation, and refinement as part of this design-based intervention.

Table 1.

Phases of design adapted from Easterday et al. (2018).

DBR step	Description
Focus	Addressed the need for a theoretically guided macroscript to support regulated learning and socio-emotional awareness in student-led small ensemble rehearsals.
Understand	Qualitative studies (Tahirbegi, 2023a, 2023b) analyzed rehearsal contexts to identify areas where a scaffolded intervention could enhance regulated learning. Existing solutions from related domains (e.g., Miksza et al., 2018; Näykki et al., 2017) were also examined.
Define	Established the goal of supporting regulated learning through prompts encouraging task analysis, goal setting, and strategy development, alongside fostering socio-emotional awareness.
Conceive	Designed the macroscript using both digital (iPad-based prompts via Nettskjema) and analog tools (large notepads for shared planning) to support collaborative processes in rehearsals.
Build	Developed intervention materials, including adapting the online form for iPads and preparing large notepads. Ensured accessibility and integration with the learning environment.
Test	Conducted a pilot with a volunteer ensemble, revised the macroscript based on feedback, and implemented it with three ensembles, compared to two baseline groups. Both formative (pilot) and summative (baseline comparison) assessments were conducted.
Present	Reported findings on the macroscript's impact, highlighting differences between scripted and baseline ensembles. Future research may explore further refinements.

A Note on the Pilot Phase

An initial pilot phase tested and refined the macroscript. A Western classical trio integrated the original script into their rehearsal and provided feedback on its practicality and usability. They noted that the script’s middle section was too long, disrupting collaborative flow. Additionally, there was a concern about not fully understanding what constituted a socio-emotional challenge. Based on this feedback, a brief explanatory definition was included, and the middle section was shortened with a dropdown menu.

Participants and Context

Five chamber music ensembles from a HME institution in Norway participated in this study: three engaged with the macroscript intervention, while two served as baseline comparison groups. All ensembles performed Western classical repertoire and were newly formed through self-selection at the start of the academic year in late August. They rehearsed weekly in preparation for semester-end performances.

Participants’ prior experience with reflective rehearsal techniques was not explicitly controlled or assessed. Rather, the macroscript was specifically designed as a structured intervention to scaffold key regulatory processes (e.g., goal setting, monitoring, and reflection) and to encourage deliberate socio-emotional engagement. This design allowed ensembles with potentially varied levels of prior reflective experience to benefit uniformly from a structured approach.

Script Ensembles: Data collection occurred between October–November 2023. Rehearsals lasted 85–120 min each. The researcher provided initial instructions for using the macroscript (via iPads and notepads), left the room to allow autonomous rehearsal, and returned midway and at the end for additional script-related guidance (see Table 2).

Non-Script (Baseline) Ensembles: Data from October 2022 were repurposed. Rehearsals lasted approximately 60 min, with the researcher setting up cameras and leaving the room for undisturbed rehearsals. A total of 7.5 h of rehearsal video data was collected, supplemented by macroscript entries from iPads and insights from focus group discussions.

Table 2.

Macroscript intervention overview for a single rehearsal session.

Script phase	Description
Phase 1 Plan	In the initial phase, each student individually addresses the prompts using the provided iPads. Once all students have completed their responses, they convene with their ensemble to share insights and collaboratively plan the rehearsal. Together, they document the rehearsal goals and additional notes on the large notepad provided.
Rehearsal begins once the planning is done
Phase 2 Spot-check	The ensemble takes a brief pause to complete the spot-check prompts using the iPads. Afterward, they resume rehearsal when they want.
Rehearsal resumes
Phase 3 Reflection	At the end of the rehearsal, ensemble members fill out with the final prompts outlined in the macroscripts, as part of their reflection phase.

Macroscript for Regulated Learning in Small Ensembles

Based on the regulated learning framework, the macroscript was structured into three phases: Plan, Spot-check, and Reflect (see Appendix I). The prompts were inspired by Näykki et al.'s (2017) approach. Each phase is detailed in Table 3 and its application illustrated in Figure 1.

Figure 1.

Example images from script rehearsals.

Table 3.

Macroscript, presented in three script phases.

Script sequence	Plan	Spot-check (Monitor)	Reflect
Prompts	What do we want to achieve today? (e.g., first movement; run-through etc.)	How are you doing compared to what you set out to do?	How would you evaluate today's rehearsal?
	How do you feel about playing this piece together?	What kinds of feelings does this rehearsal evoke in you?	How did you make your challenges work at the end?
		What kinds of socio-emotional challenges are you currently experiencing?	What helped or prevented moving forward in the task?
			How do you feel about the rehearsal climate?
			How do you feel about the interactions between the ensemble members?

The Plan phase aimed to prompt goal setting and enhance socio-emotional awareness within groups. This phase involved two main tasks: individual idea generation, followed by group discussion to compile ideas and establish rehearsal goals (see Figure 1). The Spot-check guided groups in monitoring their progress towards these goals by incorporating two components: self/group-control and self/group-observation, encouraging students to make necessary adjustments, particularly regarding the socio-emotional climate. The Reflect phase prompted students to self-assess their rehearsal experience and share reflections with peers, fostering greater awareness of both individual and group dynamics. Rehearsals followed a consistent structure while allowing flexibility in duration to accommodate students’ needs, ensuring that the macroscript could be integrated effectively within diverse rehearsal contexts.

Qualitative Content Analysis of Video Data

The video data were analyzed using qualitative content analysis, which systematically codes and identifies patterns within the data (Hsieh & Shannon, 2005). Unlike thematic analysis, qualitative content analysis allows for the quasi-quantification of interaction frequencies while preserving the richness of qualitative context (Vaismoradi et al., 2016). This made it particularly suitable for comparing interaction patterns across ensembles in this study.

We adopted a summative content analysis framework, which involved identifying and quantifying interaction categories before interpreting their contextual significance (Hsieh & Shannon, 2005). Guided by Erickson (2006), we treated the video data as a resource for meaning-making, with analytical decisions informed by our research questions. This inductive process began with broad observations, progressively narrowing to specific patterns related to group dynamics and regulatory processes.

Rehearsals were segmented through open coding, allowing key interaction categories (e.g., “play” and “musical problem-solving”) to emerge directly from the data (Erickson, 2006). Interaction categories were then organized to reflect patterns in rehearsal dynamics. Based on these categories, we developed interaction profiles for each ensemble, detailing the nature, frequency, and duration of specific interaction types through time-coded segments (see Appendix III for sample analysis samples). This enabled both within-case analysis of individual ensembles and cross-case comparisons between script and non-script groups (Stake, 2013). Importantly, this approach enabled the quantification of interaction patterns while maintaining the depth of qualitative interpretation (Hsieh & Shannon, 2005; Vaismoradi et al., 2016). The subsequent analysis focused on script-related episodes, examining how the macroscript influenced the group-level regulatory processes.

Analysis of Focus Group Discussions

To complement the video data, we conducted focus group discussions to explore participants’ experiences with the macroscript and its perceived impact on collaboration and socio-emotional dynamics. Facilitated by the first author, these sessions encouraged spontaneous reflections and interactive dialogue, leveraging the group setting to elicit diverse perspectives (Kvale & Brinkmann, 2015). Focus groups were held within 3–4 days after rehearsals to capture participants’ fresh recollections of their rehearsal. Discussions covered topics such as ensemble dynamics, collaboration, emotional regulation, and the integration of the macroscript into rehearsal practices. Each session lasted 40–60 min and was both video- and audio-recorded. While the discussions contributed to a broader research project, the analysis presented here focuses specifically on macroscript-related experiences (see Appendix II for the interview guide).

The focus group data were analyzed using a directed qualitative content analysis approach (Hsieh & Shannon, 2005), which allows for both validating and extending the theoretical framework underpinning the macroscript (Cobb et al., 2003). This approach was chosen to examine how the macroscript supported socio-emotional awareness and regulatory processes, guided by established theories on emotion regulation and collaborative learning.

The analytical process involved: (1) verbatim transcription of the focus group discussions; (2) iterative coding, starting with broad categories informed by the intervention's goals (e.g., group affect awareness, self-awareness, emotional regulation); (3) refinement of categories to capture nuanced participant experiences, particularly regarding how the macroscript influenced interpersonal dynamics and collaborative strategies.

In addition to addressing the study's primary research questions, insights from the focus group data will inform future iterations of the macroscripts, contributing to the ongoing refinement of the design (Collective, 2003).

Findings

In response to the RQ1 (How do ensembles engage in collaborative interactions during rehearsals?), the analysis identified seven qualitatively distinct types of interaction, which were then organized into three overarching categories. These categories include regulatory phases linked to the macroscript (plan, spot-check, and reflection), regulatory reactions to challenges (challenge with emotional monitoring, challenge with musical problem solving, and solution/adaptation), and active performance phase (play) (see Table 4).

Table 4.

Interaction categories emerging from data.

Focus of interaction	Interaction type	Definition	Example of the interaction from data
Carry out activities related to a phase of self-regulated learning cycle	Plan	The initial planning phase where goals, priorities, and tasks are discussed and set for the rehearsal session.	Case 1: Quartet Ella: What do you want to accomplish in Wolf? Max: I think we should work on dynamics mainly. Try to be as piano as possible. Ella: Do we all agree on this? [starts writing down on the planning sheet] Sara: Yes, blend more. Noam: Yes, blend and find a compromise between how soft Ella can go and… Ella: Yes, I like that. […] Sara: I think we should actually also think more about the development, the shape, the arching. Noam: Yes, I wrote that down too. Work on the differences in dynamics. Ella: Okay, so the overall shape. Sara: Yes, absolutely!
	Spot-check (Monitor)	A mid-point break during the rehearsal prompted by the script, where group members engage in active monitoring of their rehearsal progress and externalize their reflections on the process.	Case 3: Trio Tara: Oh, yeah… so now we do the second part [of the script], yeah? […]. It is like, after this, I need therapy [laughing]. Socio-emotional challenges… [reading the parts of the form out loud] Mia: Yeah, that's confusing. Tara: I really don’t know what we should say… Mia: Maybe something like communication, that is connected to that. Tara: Maybe Tom has an idea and can make it a bit clearer for us. Tom: It could be us coming from different backgrounds and like how we… Mia: Communication. I think it is more or less that. Tom: Okay, more or less, but also how we react to each other. If you play a wrong note, or if I play a wrong note, then we feel, “oh, no, no, no, I am sorry.” That can be kind of interaction. If you feel, oh no, “I am just messing it up for the others.” Tara: Yeah, I see. Or if you feel that someone is messing it all up for me… […]. Actually, I think the key we have with each other, as I am thinking about it now, is that we really respect each other well. Tom: Exactly, yeah. Tara: This is like, hmm… like, we respect that we are all individual musicians, and we have different opinions. This makes it so much easier to work with in this group. It is like, no one is downgrading someone. Mia: Yeah, each person is unique. Tom: That's a perfect point, I think. It's very well said.
	Reflection	The closing discussion at the end of the session, where group members summarize and evaluate the rehearsal's progress, and plan next steps.	Case 2: Trio Aya: [reading out the prompts out loud] How did you make your challenges work? Communication! Nadia: Yes! Aya: I think we were quite successful today. Nadia: Yes, we have gotten quite far. Aya: [haha] now I influenced you with my opinions [no further discussion].
Regulatory reactions to a challenge faced during the rehearsal session	Challenge followed by emotional monitoring	The group pauses to address a challenge, to which some group members express negative emotional responses such as frustration. The interaction centres on identifying challenge and its emotional impact but does not yet implement solutions.	Case 1: Quartet Ella: Ah, that is so hard. Sara: Yes, it is exhausting. Ella: I feel like my whole body gets so tense. Can we do it from 55 again?
	Challenge followed by a musical problem solving	The group pauses to address a challenge, focusing on musical elements. The interaction centres on brainstorming solutions but stops short of actual implementation.	Case 5: Trio Ari: I know here it says that half note equals 92, but I checked with the metronome, it was 80. Maybe it doesn’t have to be that fast either. Jay: I think we don’t need to stress. Ari: [to May] So take as much time as you need. May: Okay. Should we take it from the beginning?
	Solution/Adaptation	Group members concrete implementation of a decision, adjustment of behaviour or change in emotional expression after concrete efforts of addressing the challenge. The focus is on enacting or applying solutions to resolve the identified (challenging issue).	Case 4: Trio Paul: It is getting there, isn’t it? Suzy: Yeah, it is! Paul: Guys, should we do the whole thing? Suzy: Yes, if you don’t rush! [haha] Carl: Let's do the whole thing but a bit under tempo.
Performing	Play	Group members focusing on playing/practicing.	N/A

The plan refers to the initial segment of rehearsals where ensembles set goals and outline strategies. Script ensembles engaged in structured planning, guided by the macroscript prompts, dedicating between 11–13% of their total rehearsal time to this phase. Non-script ensembles typically omitted formal planning or relied on implicit, informal routines. The spot-check phase involved mid-rehearsal evaluations aimed to monitor progress. While such reflections could occur spontaneously, they appeared exclusively as structured activities prompted by the macroscript in script ensembles, accounting for 6–8% of rehearsal time. No equivalent structured evaluations were observed in non-script ensembles.

The reflection phase, occurring at the conclusion of rehearsals, provided an opportunity for ensembles to assess their performance, evaluate goal attainment, and discuss collaborative dynamics. This phase was explicitly facilitated by the macroscript, with script ensembles dedicating between 4–11% of their rehearsal time to reflective activities. These structured reflection activities did not spontaneously occur in non-script ensembles, further highlighting the macroscript’s specific impact.

Beyond the macroscript-driven phases, all ensembles exhibited regulatory responses to rehearsal challenges, categorized into three types. The most frequent, challenge with musical problem solving, involved discussions addressing technical, expressive, or coordination difficulties, occupying 20–56% of total rehearsal time. These interactions were predominantly task-focused, with minimal emotional expression. In contrast, the challenge with emotional monitoring category captured instances where performance difficulties elicited emotional reactions, such as frustration, accounting for approximately 4–14% of rehearsal time.

The solution/adaptation category involved moments when ensembles expressed satisfaction with performance outcomes, often deciding to move to the next section of the rehearsal. This adaptive response occurred consistently across ensembles, representing 1–6% of rehearsal time and marked a transition from problem solving back to engaging with the musical material. Finally, the play category included continuous performance segments, making up a significant portion of rehearsal time, typically between 32–44% across different ensembles.

While each ensemble exhibited a unique interaction profile, notable differences emerged between script and non-script conditions. As illustrated in Figures 2 and 3, these conditions differed significantly in the time allocated to regulated learning phases—planning, monitoring, and reflection. Ensembles in non-script conditions dedicated less than 10% of their rehearsal time to these phases, whereas script ensembles allocated nearly 40% to these strategic activities. Despite these differences, similarities were observed across both conditions, particularly regarding the proportion of time spent on play.

Figure 2.

Visualization of interaction profiles in script conditions.

Figure 3.

Visualization of interaction profiles in baseline conditions.

The plan phase of the macroscript notably increased the time that ensembles in script conditions spent on proactively structuring their work. In contrast, ensembles in non-script conditions adopted a more spontaneous “play-as-you-go” approach, with minimal pre-planning. These ensembles dedicated an average of 2 min (3%) of their total rehearsal time (∼60 min) to pre-planning. In comparison, ensembles in script conditions allocated significantly more time to initial planning. One group spent 8 min (∼10%), representing the least active engagement, while the other two groups dedicated approximately 14 min (∼13%) before enacting the repertoire.

While the macroscript enhanced engagement in strategic learning phases, it did not substantially alter the relative time spent actively performing. Play time averaged 20 min (34%) in non-script conditions, closely matching script ensembles, which dedicated approximately 27–38 min (32–38%) of total rehearsal time to repertoire enactment. Case 1 spent slightly more time on performance (∼40%, 53 min), although this difference was modest.

Importantly, all ensembles dedicated substantial portions of rehearsal time to discussions and reflective exchanges, emphasizing that ensemble rehearsals involve significant amounts of conversation, negotiation, and collective meaning-making beyond active performance (see Figure 4).

Figure 4.

Distribution of interaction types in script versus baseline rehearsals.

In response to RQ2 (How did the macroscript influence group members’ collaborative interactions related to aspects of socially shared regulation of learning?), the analysis revealed that the macroscript also influenced the content and quality of interactions. Subsequently, these qualitative differences were examined category by category. The two examples shown in Table 5 illustrate these differences. The first presents an episode of active, engaged planning in a script condition, while the second demonstrates the beginning of a rehearsal in a non-script condition.

Table 5.

Rehearsal openings—Case 1 and Case 5.

In the first example, the ensemble's engagement with the orientation phase of the script demonstrates a strong instance of shared regulation within their collaborative decision-making process. The students engage in transactive actions as they actively solicit each other's input, discuss their preferences, and collectively draft their rehearsal plan. The orientation phase of the macroscript facilitates the ensemble's ability to move from individual ideas to co-constructed plans. It demonstrates how scripting the planning encourages individual contributions and sharing of goals, increases socio-emotional awareness, preparing the group to face anticipated challenges in advance, such as the repertoire’s vulnerabilities. In contrast, the non-script condition (Case 5) demonstrated a more spontaneous and informal approach to planning. The initial conversation lasted about two minutes, with limited focus on goal setting or strategic preparation. The dialogue shifted quickly from casual remarks to brief decisions about which section of the repertoire to rehearse. The differences highlight how the macroscript influenced the organization and focus of collaborative interactions. Additionally, the focus group discussions provided additional insights into the perceived utility of the macroscript.

The planning phase was consistently identified as beneficial across ensembles. Participants emphasized that this phase contributed to a more organized and less chaotic rehearsal environment, creating space for all members to voice their opinions. Several students noted that planning facilitated a clearer structure and helped align the ensemble's goals. As shown in Table 6, participants highlighted how the planning phase influenced factors such as group coordination, the articulation of personal and collective goals, and the development of shared understanding within the ensemble.

Table 6.

Students’ accounts of plan phase in the script.

Examples from focus group data	Elements in the response influencing collaboration, emotion regulation or socio-emotional awareness
I think planning was nice because then you get a sense of what everyone wants to do, and once it is down on the paper, it is hard to take it away. (Noam, Case 1).	Awareness of others’ preferences and goals.
We really communicated. Communication—we found that this is important. What is the reason? Why are we practicing today? I think that was very helpful. If we didn’t do that, the rehearsal might have been more chaotic. I think it structured it, made the rehearsal better. I mean, you have to think individually to get them on the big paper. I think the most helpful thing was actually having the big paper. (Amy, Case 2).	Awareness of the goals, interaction, planning.
I like that idea. I was thinking we should do this for us at every rehearsal—just to really say out loud what we want to do. It structures the rehearsal. Also, we can have an overview of the time, which can help with time management. (Tara, Case 3).	Awareness of rehearsal timeline and structure.

In contrast, the spot-check phase was generally perceived as disruptive. This perception was consistent across all participating ensembles. The disruption was particularly noticeable during rehearsals that were already progressing smoothly, as participants felt that structured interruptions broke their momentum and focus. Many described the process of disengaging from active rehearsal to record observations on iPads as jarring. Some expressed a preference for this type of monitoring to occur more organically rather than through structured pauses. However, participants acknowledged that spot-checks could be valuable in less cohesive rehearsals. Some students reflected on past experiences, noting that such pauses might have helped overcome initial rehearsal difficulties or facilitated a return to shared understanding during challenging moments. Overall, while the spot-check phase was seen as intrusive during productive rehearsals, its potential value in situations requiring recalibration was recognized. These responses are detailed in Table 7.

Table 7.

Students’ accounts of spot-check phase in the script.

Examples from focus group data	Elements in the response influencing collaboration, emotion regulation or socio-emotional awareness
Yeah, we had moments in other rehearsals where the first half was really rough. There have been times when I thought “hey, can we take this break now and just regroup?” But I think for that particular rehearsal, we were pretty much on the same page. (Ella, Case 1).	Awareness of both positive and negative group affect
We planned a break anyway, more or less at the same time. But mentally, instead of just stopping properly, we had to write things down. That was a little bit disturbing. (Max, Case 1)
Yeah, I think if I was really bad, it would be really good to just stop and focus a bit on myself. Getting really used to, ‘Oh, I’m feeling bad. Why I’m feeling bad?’ Yeah, okay, I can do something about that to get my mood better. (Amy, Case 2).
We felt pretty good. But if there is more, I don’t know if you have a bad feeling or more like if it is not going great, maybe then it’s more helpful to have this kind of short stop and think about what is actually going on. (Zoe, Case 2).	Awareness of both positive and negative group affect
I think spot-check can be nice. But when we are really working on something and I know that, okay, we have to continue like this. I really don’t want to be disturbed by anything. (Tara, Case 3).	Awareness of the situational appropriateness of when to spot-check.

The reflection phase of the macroscript involved individual documentation on iPads, with minimal group discussion. This structure appeared to be influenced by practical constraints, such as the need to conclude rehearsals and pack away instruments after 1.5-h sessions. Despite the limited collaborative reflection, focus group discussions revealed that participants generally valued the reflective process. Perspectives on its timing and effectiveness varied. Some students found it challenging to articulate meaningful reflections immediately after rehearsal, expressing a need for more time to process their experiences. Others appreciated the opportunity to capture emotions in real-time, although they described the process as challenging and occasionally unstructured. As shown in Table 8, students’ reflections highlighted both the benefits and difficulties associated with immediate post-rehearsal reflection. While individual reflection was seen as meaningful, the absence of group dialogue raised questions about the depth and impact of this phase. Some participants suggested that incorporating optional group discussions or adjusting the timing of reflections might enhance their effectiveness.

Table 8.

Students’ accounts of reflection phase in the script.

Examples from focus group data	Elements in the response influencing collaboration, emotion regulation or socio-emotional awareness
For me, it is difficult to reflect immediately after. I need time. I need time to process it, to think, to wind back (Sara, Case 1).	Self-awareness of her own reflective process, temporal distance.
You have just one general feeling, positive for example, but you don’t know exactly what to mention and what’s important for you. I also understand why it’s very important to know emotions in the moment, but it’s complicated (Mia, Case 2).	Awareness of complexity of expressing emotions promptly
So, it’ is… If you’re pushed to just put those words out, then… I mean, it’s probably the truest, I don’t know, question mark. But of course, it’s, as I said, unfiltered (Tom, Case 3).	Awareness of the emotional expressions in unfiltered

Overall, the planning phase emerged as the most valuable component of the macroscript, contributing to structured, goal-oriented rehearsals. The spot-check phase was perceived as disruptive in smoothly functioning rehearsals but was recognized as potentially beneficial for facilitating real-time adjustments when needed. The reflection phase posed challenges related to timing, with students expressing mixed feelings about the immediacy of post-rehearsal reflections.

Discussion

This study employed DBR principles to design, implement, and evaluate a macroscripts intervention aimed at enhancing regulatory processes and socio-emotional awareness in small ensemble rehearsals. It adopted a dual approach by examining the impact from two different perspectives: the actualized interactions during the rehearsals and subjective interpretations on the perceived usefulness of the macroscript. The study comprised five ensembles (n = 3 script and n = 2 nonscript-conditions), enabling detailed analyses and deepening our understanding of the macroscript's potential impact on collaborative dynamics.

Preliminary analysis identified seven qualitatively distinct interaction types. Three of these, plan, spot-check, and reflection, were directly linked to the macroscripts. Three others, emotional monitoring, musical problem solving, and solution/adaptation, represented regulatory actions in response to rehearsal challenges and the active performance phases were categorized as play. A key finding was that ensembles spent less than half of their rehearsal time on actual play, emphasizing the need to further explore the role of verbal interaction in rehearsal settings. While the non-verbal communication, such as gestures, has been extensively studied as a primary channel for conveying tempo, expressivity, and coordination (e.g., Bishop et al., 2023; King & Ginsborg, 2016; McCaleb, 2017; Pennill & Timmers, 2022; Schiavio & Høffding, 2015; Schiavio et al., 2021; Timmers et al., 2021), this study underscores the significance of verbal communication in fostering consensus building, shaping the socio-emotional climate and supporting group-level regulation. Moreover, the self-managed, student-led structure of small ensemble rehearsals demands a higher degree of consensus building (Gilboa & Tal-Shmotkin, 2012), where students must balance individual autonomy with collective goals. The macroscript effectively facilitated these processes by providing structured prompts that encouraged open dialogue and collaborative decision-making, enabling ensembles to negotiate both musical and interpersonal dynamics with greater clarity (Miller & Hadwin, 2024).

Similar to other learning contexts and domains, the role of verbal communication cannot be underestimated in the context of learning and performing music. The significance of examining group members’ verbal communication has been extensively studied in the field of collaborative learning (Järvelä et al., 2015), particularly concerning its role in regulation processes (e.g., Bakhtiar & Hadwin, 2020; Järvenoja et al., 2018). Although not all regulation is verbal, much of the metacognitive activity and shifts in thinking during collaboration become visible through spoken dialogue (Bakhtiar & Hadwin, 2020).

In this study, verbal communication served as a key mechanism for externalizing regulatory processes, enabling ensemble members to articulate thoughts, refine strategies, and collaboratively negotiate decisions. Macroscript prompts encouraged transactive dialogue, fostering shared decision-making around rehearsal plans and musical interpretations. This aligns well with the concept of socially shared regulation emphasizing that shared regulation is a dynamic, negotiated process reliant on continuous interaction (Hadwin et al., 2018; Järvenoja et al., 2015; Miller & Hadwin, 2024).

While this study advances our understanding of how different interaction modes support learning in ensembles, further research is needed to explore the connections between interaction quality, learning, and performance outcomes. Shared regulation, as a process-oriented construct from rehearsal to performance, warrants further empirical attention.

The macroscript did not significantly affect the time ensembles spent in active performance. On average, ensembles allocated 34% of their rehearsal time to play, with no notable differences between conditions. However, the distribution of the remaining rehearsal time varied considerably depending on the presence of the script. Ensembles using the script dedicated more time to structured learning phases, such as spot-check and reflection, whereas non-script ensembles adopted a more spontaneous approach, with minimal pre-planning and increased time spent addressing challenges as they arose. For example, the time ensembles spent on musical problem solving differed notably. Those using the macroscript appeared better equipped to anticipate and prevent challenges through proactive planning and monitoring. In contrast, non-script ensembles spent over half of their rehearsal time engaged in problem solving. This pattern aligns with self-regulation of learning research, which suggests that effective instrumental practitioners engage in strategic activities as part of their initial task analysis (Miksza & Brenner, 2023; Nielsen, 2004). Our findings suggest that the macroscripts similarly facilitated rehearsal orientation through explicit goal setting, proactive problem anticipation, and collaborative focus, all well-established factors successful regulation of learning in collaborative contexts (Järvenoja et al., 2024; Linnenbrink-Garcia et al., 2016; Mänty et al., 2023; Näykki et al., 2021; Törmänen et al., 2023). While the structured rehearsal interactions introduced by the macroscript theoretically imply enhanced rehearsal processes, this study did not empirically assess whether such improvements directly translate into enhanced performance outcomes in final performances. Future research should directly investigate how macroscript-supported rehearsal interactions impact measurable performance quality, and concert preparedness.

Finally, the macroscript aimed to increase socio-emotional awareness, facilitating moments of reflection and turn-taking to support regulation of emotions. The analysis of both the video data and the group discussion data supported the conclusion that this goal was achieved. Students stated that the script promoted equal participation by enabling all partners to share their opinions, not only on aspects of the repertoire like interpretation, but also on individual preferences within the group work. This is promising, as prior studies have shown socio-emotional challenges often arise from unequal leadership roles, overruling ensemble partners, or different artistic choices (Ginsborg, 2017; Murnighan & Conlon, 1991; Sætre & Zhukov, 2022; Tahirbegi, 2023a).

Furthermore, while the reflection phase was the least utilized component of the macroscript during rehearsals, focus group discussions revealed that students engaged in reflective thinking beyond the immediate rehearsal context. This suggests that the macroscript may have a sustained impact, acting as a trigger for ongoing socio-emotional reflection. In addition, the focus group discussions themselves provided valuable opportunities for students to explore these issues in depth, highlighting the importance of creating dedicated spaces for dialogue outside of rehearsals. Facilitators might consider incorporating regular opportunities for ensemble members to reflect on both cognitive and socio-emotional aspects of their collaboration, which could further support group cohesion and learning.

Prompting ensembles’ awareness of such processes through scripts seems to be a functional way to reduce challenges and ensure a socio-emotionally pleasant atmosphere for goal-oriented learning and rehearsing. In this study, this was evident in the way the script ensembles proactively addressed their challenges through well-organized rehearsals that established mutual understanding of the repertoire in advance. The orientation phase facilitated higher-level cognitive processes associated with learning and performance within an enhanced socio-emotional context (Näykki et al., 2017; Rojas et al., 2022). Additionally, previous studies have also emphasized the significance of maintaining positive socio-emotional interactions early in the collaborative process (Linnenbrink-Garcia et al., 2011). The script as a whole offered students opportunities to reflect on their emotional experiences at different rehearsal stages, prompting them to assess and adjust as needed (Miller & Hadwin, 2015).

Limitations and Future Directions

The dynamic nature of ensemble rehearsals presents inherent challenges when analyzing interaction patterns. The ‘amount, type and purpose’ of verbal interaction in small ensembles was found to be ‘idiosyncratic and subject to change over time within and across rehearsals’ (Pennill & Timmers, 2022, p. 2). This suggests that relying solely on a single session macroscript intervention may present a limitation in this study, as the interaction profiles presented are likely to evolve over time. Moreover, this study was qualitative and included only five ensembles, which should be considered especially when comparing script versus nonscript-conditions. Part of the differences between the conditions are most probably due to natural situational variation in the interaction. Future research should include longitudinal interventions with larger samples and mixed-methods approaches to deepen understanding of the macroscript’s effects over time.

Despite these limitations, this study bridges theory and practice by applying regulated learning frameworks in the music domain. Given that many rehearsals occur without a teacher, such a framework could serve as a roadmap for nurturing regulated learning in ensemble collaborations.

Furthermore, the DBR approach proved effective in integrating theory with practice, enhancing ecological validity while adapting to evolving rehearsal dynamics (Easterday et al., 2018). Future studies should explore how to balance structure and spontaneity in ensemble rehearsals as overly rigid script designs may constrain creative collaboration (Wang et al., 2017).

Footnotes

Action Editor

Elaine King, University of Hull, School of Arts.

Peer Review

J Murphy McCaleb, York St John University, School of the Arts.

One anonymous reviewer.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

This study protocol underwent review and approval by the Sikt—Norwegian Agency for Shared Services in Education and Research (formerly NSD). Informed consent was obtained, and students were provided with a modest gift card as a token of appreciation for their involvement.

ORCID iDs

Damla Tahirbegi

Hanna Järvenoja

Data Availability Statement

The focus group data supporting the findings of this study are available from the corresponding author upon reasonable request.

Appendix I: Macroscript in Three Parts

Appendix II

Appendix III: Sample Video Data Analysis

References

Bakhtiar

Hadwin

A. F.

(2020). Dynamic interplay between modes of regulation during motivationally challenging episodes in collaboration. Frontline Learning Research, 8(2), 1–34. https://doi.org/10.14786/flr.v8i2.561

Bakhtiar

Hadwin

A. F.

(2022). Self-, co-, and socially shared regulation dynamics. Routledge. https://doi.org/10.4324/9781138609877-REE123-1

Bakhtiar

Webster

E. A.

Hadwin

A. F.

(2018). Regulation and socio-emotional interactions in a positive and a negative group climate. Metacognition and Learning, 13(1), 57–90. https://doi.org/10.1007/s11409-017-9178-x

Biasutti

Concina

Wasley

Williamon

(2016). Music regulators in two string quartets: A comparison of communicative behaviors between low-and high-stress performance conditions. Frontiers in Psychology, 7, 205771. https://doi.org/10.3389/fpsyg.2016.01229

Bishop

Høffding

Lartillot

Laeng

(2023). Mental effort and expressive interaction in expert and student string quartet performance. Music & Science, 6, 20592043231208000. https://doi.org/10.1177/20592043231208000

Brandler

B. J.

Peynircioglu

Z. F.

(2015). A comparison of the efficacy of individual and collaborative music learning in ensemble rehearsals. Journal of Research in Music Education, 63(3), 281–297. https://doi.org/10.1177/0022429415597885

Cleary

T. J.

Callan

G. L.

(2018). Assessing self-regulated learning using microanalytic methods. In Handbook of self-regulation of learning and performance (2nd ed., pp. 338–351). Routledge. https://doi.org/10.4324/9781315697048-22

Cobb

Confrey

DiSessa

Lehrer

Schauble

(2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102/0013189X032001009

Collective

D.-B. R.

(2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8. https://doi.org/10.3102/0013189X032001005

10.

Creech

Hallam

(2017). Facilitating learning in small groups: Interpersonal dynamics and task dimensions. In Musicians in the making: Pathways to creative performance (Vol. 1, pp. 57–75). Oxford University Press.

11.

Davidson

King

E. C.

(2004). Strategies for ensemble practice. In A. Williamon (Ed.), Musical Excellence (pp. 105–122). Oxford: Oxford University Press.

12.

Easterday

M. W.

Rees Lewis

D. G.

Gerber

E. M.

(2018). The logic of design research. Learning: Research and Practice, 4(2), 131–160. https://doi.org/10.1080/23735082.2017.1286367

13.

Erickson

(2006). Definition and analysis of data from videotape: Some research procedures and their rationales. Handbook of Complementary Methods in Education Research, 3, 177–192. https://doi.org/10.4324/9780203874769

14.

Gaunt

Treacy

(2020). Ensemble practices in the arts: A reflective matrix to enhance team work and collaborative learning in higher arts education. Arts and Humanities in Higher Education, 19, 419–444. https://doi.org/10.1177/1474022219885791

15.

Gilboa

Tal-Shmotkin

(2012). String quartets as self-managed teams: An interdisciplinary perspective. Psychology of Music, 40(1), 19–41. https://doi.org/10.1177/0305735610377593

16.

Ginsborg

(2017). Small ensembles in rehearsal. In Musicians in the making: Pathways to creative performance (pp. 164–184). Oxford University Press.

17.

Hadwin

Järvelä

Miller

(2018). Self-regulation, co-regulation, and socially shared regulation in collaborative learning environments (2nd ed., pp. 83–106). Routledge. https://doi.org/10.4324/9781315697048-6

18.

Hakkarainen

(2016). Mapping the research ground: Expertise, collective creativity and shared knowledge practices. In Collaborative learning in higher music education (pp. 13–26). Routledge.

19.

Hargreaves

D. J.

MacDonald

R. A. R.

Miell

(2018). Musical identities mediate musical development. In G. F. McPherson & G. F. Welch (Eds.), Music and music education in people’s lives (Vol. 1, pp. 124–142). Oxford University Press.

20.

Hill

Walsh

(2018). Conflict in collaborative musical composition: A case study. Psychology of Music, 46(2), 192–207. https://doi.org/10.1177/0305735617704712

21.

Hsieh

H.-F.

Shannon

S. E.

(2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687

22.

Järvelä

Kirschner

P. A.

Panadero

Malmberg

Phielix

Jaspers

Koivuniemi

Järvenoja

(2015). Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools. Educational Technology Research and Development, 63, 125–142. https://doi.org/10.1007/s11423-014-9358-1

23.

Järvenoja

Järvelä

Malmberg

(2015). Understanding regulated learning in situative and contextual frameworks. Educational Psychologist, 50(3), 204–219. https://doi.org/10.1080/00461520.2015.1075400

24.

Järvenoja

Järvelä

Törmänen

Näykki

Malmberg

Kurki

Mykkänen

Isohätälä

(2018). Capturing motivation and emotion regulation during a learning process. Frontline Learning Research, 6(3), 85–104. https://doi.org/10.14786/flr.v6i3.369

25.

Järvenoja

Malmberg

Törmänen

Mänty

Haataja

Ahola

Järvelä

(2020). A collaborative learning design for promoting and analyzing adaptive motivation and emotion regulation in the science classroom. Frontiers in education (Lausanne), 5, 1–11. https://doi.org/10.3389/feduc.2020.00111

26.

Järvenoja

Näykki

Törmänen

(2019). Emotional regulation in collaborative learning: When do higher education students activate group level regulation in the face of challenges? Studies in Higher Education, 44(10), 1747–1757. https://doi.org/10.1080/03075079.2019.1665318

27.

Järvenoja

Törmänen

Järvelä

Shubina

(2024). Motivation and emotion regulation in collaborative learning contexts. In Motivation and emotion in learning and teaching across educational contexts (pp. 100–113). Routledge.

28.

King

(2021). Working with and in small groups. In Routledge international handbook of music psychology in education and the community (pp. 320–334). Routledge.

29.

King

Ginsborg

(2016). Gestures and glances: Interactions in ensemble rehearsal. In New perspectives on music and gesture (pp. 203–228). Routledge.

30.

King

E. C.

Gritten

(2017). Dialogue and beyond. In Musicians in the making: Pathways to creative performance (Vol. 1, pp. 306–321). Oxford University Press.

31.

Kobbe

Weinberger

Dillenbourg

Harrer

Hämäläinen

Häkkinen

Fischer

(2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2, 211–224. https://doi.org/10.1007/s11412-007-9014-4

32.

Koivuniemi

Järvenoja

Järvelä

(2018). Teacher education students’ strategic activities in challenging collaborative learning situations. Learning, Culture and Social Interaction, 19, 109–123. https://doi.org/10.1016/j.lcsi.2018.05.002

33.

Kokotsaki

(2007). Understanding the ensemble pianist: A theoretical framework. Psychology of Music, 35(4), 641–668. https://doi.org/10.1177/0305735607077835

34.

Kvale

Brinkmann

(2015). Interviews: Learning the craft of qualitative research interviewing (3rd ed.). Sage.

35.

Linnenbrink-Garcia

Patall

E. A.

Pekrun

(2016). Adaptive motivation and emotion in education: Research and principles for instructional design. Policy Insights from the Behavioral and Brain Sciences, 3(2), 228–236. https://doi.org/10.1177/2372732216644450

36.

Linnenbrink-Garcia

Rogat

T. K.

Koskey

K. L.

(2011). Affect and engagement during small group instruction. Contemporary Educational Psychology, 36(1), 13–24. https://doi.org/10.1016/j.cedpsych.2010.09.001

37.

Mänty

Järvenoja

Törmänen

(2020). Socio-emotional interaction in collaborative learning: Combining individual emotional experiences and group-level emotion regulation. International Journal of Educational Research, 102, 101589. https://doi.org/10.1016/j.ijer.2020.101589

38.

Mänty

Järvenoja

Törmänen

(2023). The sequential composition of collaborative groups’ emotion regulation in negative socio-emotional interactions. European Journal of Psychology of Education, 38(1), 203–224. https://doi.org/10.1007/s10212-021-00589-3

39.

McCaleb

J. M.

(2017). Embodied knowledge in ensemble performance. Routledge.

40.

McPherson

G. E.

Osborne

M. S.

Evans

Miksza

(2019). Applying self-regulated learning microanalysis to study musicians’ practice. Psychology of Music, 47(1), 18–32. https://doi.org/10.1177/0305735617731614

41.

Miksza

Blackwell

Roseth

N. E.

(2018). Self-regulated music practice: Microanalysis as a data collection technique and inspiration for pedagogical intervention. Journal of Research in Music Education, 66(3), 295–319. https://doi.org/10.1177/0022429418788557

42.

Miksza

Brenner

(2023). A descriptive study of intra-individual change in advanced violinists’ music practice. Applications of Research in Music Education, 41(3), 22–36. https://doi.org/10.1177/87551233221090232

43.

Miller

Hadwin

(2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 52, 573–588. https://doi.org/10.1016/j.chb.2015.01.050

44.

Miller

Hadwin

A. F.

(2024). Comparing the effectiveness of CSCL scripts for shared task perceptions in socially shared regulation of collaborative learning. International Journal of Computer-Supported Collaborative Learning, 19, 455–478. https://doi.org/10.1007/s11412-024-09434-3

45.

Murnighan

J. K.

Conlon

D. E.

(1991). The dynamics of intense work groups: A study of British string quartets. Administrative Science Quarterly, 36, 165–186. https://doi.org/10.2307/2393352 .

46.

Näykki

Isohätälä

Järvelä

(2021). You really brought all your feelings out”–scaffolding students to identify the socio-emotional and socio-cognitive challenges in collaborative learning. Learning, Culture and Social Interaction, 30, 100536. https://doi.org/10.1016/j.lcsi.2021.100536

47.

Näykki

Isohätälä

Järvelä

Pöysä-Tarhonen

Häkkinen

(2017). Facilitating socio-cognitive and socio-emotional monitoring in collaborative learning with a regulation macro script–an exploratory study. International Journal of Computer-Supported Collaborative Learning, 12, 251–279. https://doi.org/10.1007/s11412-017-9259-5

48.

Näykki

Järvelä

Kirschner

P. A.

Järvenoja

(2014). Socio-emotional conflict in collaborative learning—A process-oriented case study in a higher education context. International Journal of Educational Research, 68, 1–14. https://doi.org/10.1016/j.ijer.2014.07.001

49.

Nielsen

S. G.

(2004). Strategies and self-efficacy beliefs in instrumental and vocal individual practice: A study of students in higher music education. Psychology of Music, 32(4), 418–431.

50.

Nielsen

S. G.

Johansen

G. G.

(2021). The role of peers in supporting learning in music. In Routledge international handbook of music psychology in education and the community (pp. 418–432). Routledge.

51.

Osborne

M. S.

McPherson

G. E.

Miksza

Evans

(2021). Using a microanalysis intervention to examine shifts in musicians’ self-regulated learning. Psychology of Music, 49(4), 972–988. https://doi.org/10.1177/0305735620915265

52.

Pennill

Breslin

(2021). Music ensembles as self-organized groups. In R. Timmers, F. Bailes, & H. Daffern (Eds.), Together in music: Coordination, expression, participation (pp. 3–12). Oxford University Press.

53.

Pennill

Timmers

(2022). Patterns of verbal interaction in newly formed music ensembles. Frontiers in Psychology, 13, 987775. https://doi.org/10.3389/fpsyg.2022.987775

54.

Rogat

T. K.

Adams-Wiggins

K. R.

(2015). Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation. Computers in Human Behavior, 52, 589–600. https://doi.org/10.1016/j.chb.2015.01.026

55.

Rojas

Nussbaum

Guerrero

Chiuminatto

Greiff

Del Rio

Alvares

(2022). Integrating a collaboration script and group awareness to support group regulation and emotions towards collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 17(1), 135–168. https://doi.org/10.1007/s11412-022-09362-0

56.

Rumiantsev

van der Rijst

Admiraal

(2023). A systematic literature review of collaborative learning in conservatoire education. Social Sciences & Humanities Open, 8(1), 100683. https://doi.org/10.1016/j.ssaho.2023.100683

57.

Sandoval

W. A.

Bell

(2004). Design-based research methods for studying learning in context: Introduction. Educational Psychologist, 39(4), 199–201. https://doi.org/10.1207/s15326985ep3904_1

58.

Scheuer

McLaren

B. M.

Weinberger

Niebuhr

(2014). Promoting critical, elaborative discussions through a collaboration script and argument diagrams. Instructional Science, 42, 127–157. https://doi.org/10.1007/s11251-013-9274-5

59.

Schiavio

Høffding

(2015). Playing together without communicating? A pre-reflective and enactive account of joint musical performance. Musicae Scientiae, 19(4), 366–388. https://doi.org/10.1177/1029864915593333

60.

Schiavio

Maes

P.-J.

van der Schyff

(2021). The dynamics of musical participation. Musicae Scientiae, 1029864920988319, 604–626. https://doi.org/10.1177/1029864920988319

61.

Schiavio

Meissner

Timmers

(2024). Influences of physical and imagined others in music students’ experiences of practice and performance. In Psychological perspectives on musical experiences and skills (pp. 165–187). https://doi.org/10.11647/OBP.0389

62.

Slette

A. L.

(2019). Negotiating musical problem-solving in ensemble rehearsals. British Journal of Music Education, 36(1), 33–47. https://doi.org/10.1017/S0265051718000141

63.

Sobocinski

Järvelä

Malmberg

Dindar

Isosalo

Noponen

(2020). How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Metacognition and Learning, 15, 99–127. https://doi.org/10.1007/s11409-020-09224-w

64.

Stake

R. E.

(2013). Multiple case study analysis. Guilford press.

65.

Sætre

J. H.

Zhukov

(2022). “Play with me”: Student perspectives on collaborative chamber music instruction. Research Studies in Music Education, 44(1), 205–218. https://doi.org/10.1177/1321103X20974804

66.

Tahirbegi

(2023a). Exploring emotion regulation in small ensemble contexts; three cases from higher music education. Learning, Culture and Social Interaction, 42, 100741. https://doi.org/10.1016/j.lcsi.2023.100741

67.

Tahirbegi

(2023b). If we don’t have a good relationship, we won’t deliver anything good”: Emotion regulation in small music ensembles, insights from higher music education. Music & Science, 6, 20592043231202009. https://doi.org/10.1177/20592043231202009

68.

Timmers

Bailes

Daffern

(2021). Introduction to “Together in music: Coordination, expression, participation”. In Together in music: Coordination, expression, participation (pp. 277–282).

69.

Törmänen

Järvenoja

Saqr

Malmberg

Järvelä

(2023). Affective states and regulation of learning during socio-emotional interactions in secondary school collaborative groups. British Journal of Educational Psychology, 93, 48–70. https://doi.org/10.1111/bjep.12525

70.

Vaismoradi

Jones

Turunen

Snelgrove

(2016). Theme development in qualitative content analysis and thematic analysis. Journal of Nursing Education and Practice 6(5), 100–110. https://doi.org/10.5430/jnep.v6n5p100

71.

Van Kleef

G. A.

Fischer

A. H.

(2016). Emotional collectives: How groups shape emotions and emotions shape groups. Cognition and Emotion, 30(1), 3–19. https://doi.org/10.1080/02699931.2015.1081349

72.

Volpe

D'ausilio

Badino

Camurri

Fadiga

(2016). Measuring social interaction in music ensembles. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1693), 20150377.

73.

Wang

Kollar

Stegmann

(2017). Adaptable scripting to foster regulation processes and skills in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 12, 153–172. https://doi.org/10.1007/s11412-017-9254-x

74.

Weinberger

Ertl

Fischer

Mandl

(2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science, 33, 1–30. https://doi.org/10.1007/s11251-004-2322-4

75.

Zimmerman

B. J.

Campillo

(2003). Motivating self-regulated problem solvers. In The psychology of problem solving (pp. 233–262). Routledge.

Collaboration Scripts as Scaffolds: Enhancing Regulatory Processes and Socio-emotional Dynamics in Ensemble Rehearsals

Abstract

Keywords

Introduction

Small Group Dynamics: The Intersection of Regulation and Emotions

Modes of Regulation

Supporting Collaboration with Scripts

Methodology

A Note on the Pilot Phase

Participants and Context

Macroscript for Regulated Learning in Small Ensembles

Qualitative Content Analysis of Video Data

Analysis of Focus Group Discussions

Findings

Discussion

Limitations and Future Directions

Footnotes

Action Editor

Peer Review

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iDs

Data Availability Statement

Appendix I: Macroscript in Three Parts

Appendix II

Appendix III: Sample Video Data Analysis

References