Exploring Students’ Verbal Interaction Patterns in Collaborative Learning: The Role of Group Awareness in Improving Task Coordination

Exploring Students’ Verbal Interaction Patterns in Collaborative Learning: The Role of Group Awareness in Improving Task Coordination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Students’ Verbal Interaction Patterns in Collaborative Learning: The Role of Group Awareness in Improving Task Coordination Wenli chen, Lishan Zheng, Xuanyu Chen, Mei Yee Mavis Ho, Hua Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9319024/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Group awareness (GA) plays an important role in collaborative learning by helping learners stay informed about their peers’ actions, progress, and contributions, thereby facilitating coordination during collaborative learning. Effective collaborative learning depends not only on participation, but also on how learners coordinate actions, monitor progress, and build on one another’s ideas over time. However, while prior research has demonstrated that GA support benefits coordination, its influence on the temporal structure of verbal interactions remains underexplored. To address this gap, this study explores the influence of GA support on task coordination in collaborative learning by examining students’ verbal interaction patterns, which provide a valuable process-oriented lens on these coordination dynamics, as it captures how learners coordinate, respond, clarify, and build on one another’s ideas as collaborative learning unfolds. 44 university students worked in dyads across conditions with and without GA support during a design-based collaborative design task. Verbal data were coded and analyzed using lag sequential analysis (LSA) and sequential pattern mining (SPM) to identify significant behavioral transitions and recurring interaction sequences. Results reveal that GA support not only streamlined task coordination by fostering efficient alignment of actions and decisions but also diversified the sequential structure of discourse, promoting richer dialogues towards more effective task coordination during collaborative learning. These findings advance our understanding of how GA support shapes verbal interaction behavior in collaborative learning and provide practical insights for designing Computer-Supported Collaborative Learning (CSCL) environments that balance efficiency with opportunities for critical dialogue and reflection. Group Awareness Verbal Interaction Collaborative Learning Lag Sequential Analysis Sequential Pattern Mining CSCL Figures Figure 1 Figure 2 1. Introduction Collaborative learning is one of the important 21st century competencies. A key strategy of collaborative learning is the effective sharing of knowledge and information (Bodemer & Dehler, 2011 ; Phielix et al., 2011 ; Pifarré et al., 2014 ). Without active knowledge-sharing, the benefits of collaborative learning are significantly diminished (Baker & Reimann, 2024 ; Kwon et al., 2013 ). However, effective knowledge sharing does not necessarily emerge spontaneously in collaborative settings (Kreijns et al., 2003 ; Kreijns et al., 2013 ). For learners to exchange and integrate ideas productively, they must coordinate their collaborative activity by managing participation, aligning contributions, monitoring progress, and negotiating next steps (Dillenbourg, 1999 ; Janssen et al., 2011 ; Roschelle & Teasley, 1995 ).Collaborative learning requires group members to manage diverse tasks while coordinating efforts of multiple individuals, each bringing unique perspectives. This coordination is essential as group members work toward achieving a shared understanding within the context of a joint task (Dillenbourg, 1999 ; Roschelle & Teasley, 1995 ). Leveraging collaborative interaction to improve learning processes and outcomes is a central goal of Computer-Supported Collaborative Learning (CSCL). However, effective interaction depends not only on learners’ willingness to participate, but also on their ability to coordinate their actions, align contributions, and respond to one another appropriately over time (Chen et al., 2024 ; Miller & Hadwin, 2024 ). In collaborative learning, interaction is the medium through which learners exchange ideas, while coordination refers to the organizational processes that help structure these exchanges and align them with shared task goals (Hernández-Sellés et al., 2020 ; Onrubia et al., 2015 ). In CSCL environments, the reduced availability of perceptual and spatial cues may limit learners’ awareness of what their peers are doing and how the joint work is progressing, thereby making it more difficult to align actions, time responses, and coordinate collaborative activity effectively (Bodemer & Dehler, 2011 ; Gutwin & Greenberg, 2002 ; Gutwin et al., 1996 ). Researchers have proposed group awareness (GA) support to address these coordination challenges by providing learners with real-time information about their group members’ actions, progress, and contributions (Jermann & Dillenbourg, 2008 ; Liu et al., 2018 ). By making such information visible, GA support can help learners better coordinate their collaborative activity, for example by aligning actions, monitoring progress, and responding to one another in a timely manner (Benware & Deci, 1984 ; Janssen et al., 2007 , 2011 ). It may also create conditions for more meaningful task-related interaction by making peers’ work and contributions more interpretable during collaborative learning (Buder & Bodemer, 2008 ; Gutwin & Greenberg, 2002 ; Kimmerle & Cress, 2008 ). To facilitate GA in CSCL, Information and Communication Technology (ICT) tools have been developed to provide students with relevant information about their peers. Prior studies have demonstrated that GA contributes to improved learning outcomes by supporting learners in monitoring and regulating group processes (Janssen et al., 2011 ; Li et al., 2021 ). However, the underlying behavioral evidence through which GA impacts collaborative learning remains insufficiently understood, particularly insufficiently understood, there is limited fine-grained evidence on how students’ verbal interaction patterns unfold over time and how these patterns reflect processes of task coordination and regulation during collaborative learning (Kreijns et al., 2003 ; Ma et al., 2023 ; Lyu et al., 2023 ; Zhang, 2024 ). As CSCL environments increasingly integrate GA tools (Bodemer & Dehler, 2011 ; Schnaubert & Bodemer, 2022 ), more research is needed to understand the influence of GA on learners’ collaborative learning behaviors including students’ verbal interactions. This study aims to examine the role of GA on learners’ verbal interaction, including behavioral transition and sequential patterns in collaborative learning. By analyzing differences in verbal interaction patterns across conditions with and without GA support, this study aims to generate fine-grained evidence about the behavioral mechanisms through which GA shapes collaborative learning. Such findings can contribute to a better understanding of how coordination unfolds over time and inform the design of CSCL environments that better support effectively collaborative learning 2. Literature Review 2.1 Group Awareness in CSCL Compared to face-to-face settings, coordination in CSCL is complicated by limited perceptual information, learners cannot readily determine what tasks their partners are working on, what they know, or how they are contributing (Baker & Reimann, 2024 ; Gutwin & Greenberg, 2002 ; Janssen & Bodemer, 2013 ). GA, broadly defined as the state of being informed about relevant aspects of group members or the group as a whole, helps address the coordination issues (Dourish & Bellotti, 1992; Schnaubert & Bodemer, 2019 ). The construct encompasses three types: social, concerning the interpersonal dynamics and participation levels within a group (Bødker & Christiansen, 2006; Zheng et al., 2025 ); cognitive, concerning group members’ knowledge states and supporting content-focused discussion (Ma et al., 2023 ; Schnaubert & Bodemer, 2022 ); and behavioral, concerning observable actions such as contribution frequency and task progress (Janssen et al., 2011 ; Pifarré et al., 2014 ; Zheng et al., 2025 ). GA tools operationalize these types by visualizing individual and group information, making otherwise implicit group processes externally accessible (Chen, Peng, et al., 2025 ; Phielix et al., 2011 ; Schnaubert & Bodemer, 2019 ). Each dimension contributes to collaborative learning through distinct type. Cognitive GA tools direct learners’ attention to unresolved or conflicting content, prompting deeper engagement and socio-cognitive conflict that catalyzes negotiation and mutual understanding (Heimbuch & Bodemer, 2017 ; Ollesch et al., 2021 ). Social awareness tools facilitate interpersonal interaction by keeping learners informed about others’ presence and responsiveness, supporting turn-taking and the alignment of collaborative efforts (Kreijns et al., 2003 ; Phielix et al., 2011 ). Behavioral awareness tools enable group members to assess collective progress and individual contributions, generating external feedback that promotes coordination toward shared goals (Ma et al., 2023 ; Schnaubert & Bodemer, 2022 ). Empirical evidence consistently demonstrates that these tools improve the coordination of collaborative processes, enhance discussion quality, and support more balanced participation and effective task coordination (Janssen & Bodemer, 2013 ; Janssen et al., 2011 ; Sangin et al., 2011 ; Wang et al., 2019 ). However, while the benefits of GA for collaborative processes and outcomes are well established, its influence on the temporal structure of verbal interaction, especially how learners coordinate, respond, clarify, and build on one another’s contributions as collaboration unfolds, remains underexplored, leaving open the question of whether GA reshapes not only what learners discuss but also the sequential patterns through which coordination is achieved. 2.2 The Role of Verbal Interaction in Collaborative Learning Collaborative learning outcomes are not always superior to individual learning outcomes (Barron, 2003 ; Zambrano et al., 2019 ), as effective collaborative learning depends on a social, interdependent process that thrives on seamless communication, shared knowledge, and mutual consensus (Dado & Bodemer, 2017 ). In collaborative learning environments, learners engage in socially shared regulation of cognition, metacognition, affect, and motivation, collectively working toward shared goals while fostering trust, community, and a sense of belonging (Kreijns et al., 2013 ). This reciprocal regulation renders collaborative learning inherently dialogic (Arvaja & Hämäläinen, 2021 ). Verbal interaction is the primary medium through which learners co-regulate contributions, jointly construct knowledge, and negotiate meaning (Chen, Zheng, et al., 2025 ; Stahl, 2015 ; Stahl et al., 2014 ). Research highlights that quality verbal interactions significantly shape collaborative learning effectiveness (Vuopala et al., 2016 ). Rooted in social-constructivist theory, collaborative learning relies on verbal exchanges, such as discussions and argumentation, for dynamic knowledge construction and conceptual development (Sun & Yang, 2015 ; Lee & Smagorinsky, 2000 ). The verbal interaction encompasses not only the exchange of ideas but also the processes of negotiation, elaboration, and knowledge co-construction (Malmberg et al., 2017 ). It shapes the trajectory of learning by enabling groups to refine ideas (Hu & Chen, 2021 ), coordinate tasks (Scardamalia & Bereiter, 2014 ) through social interaction. These verbal exchanges not only reveal learners’ comprehension but also allow the evaluation of both the quantity and quality of learning (Palinscar, 1998). The dialogic nature of verbal interaction ensures that learning is socially mediated, with cognitive development occurring through meaningful exchanges within group contexts (Vygotsky, 1978 ). Consequently, understanding and leveraging the dynamics of verbal interaction is essential for fostering effective collaborative learning and advancing educational practices. 2.3 Relationship of Group Awareness Support and Verbal Interaction Unlike face-to-face contexts, insufficient interaction frequently stems from the limitations of the CSCL environment in fostering social perceptions and connections (Kreijns et al., 2013 ). Therefore, earlier attempts to study the effect of GA support focus on participation in interaction in response to problems such as social loafing and free riding in CSCL contexts (Janssen & Bodemer, 2013 ; Liu et al., 2018 ). Empirical evidence indicates the use of GA tools exhibit positive effects on facilitating group interaction and stimulating group members to participate equally in discussion and conversation (Janssen et al., 2007 , 2011 ; Phielix et al., 2011 ). By creating the opportunity to observe others’ interactions and contributions to the group, GA support can increase students’ participation in discussion (Lin et al., 2021 ). Although guiding interaction processes has been regarded as a key function of GA support, an increase in participation does not inherently guarantee fruitful collaboration (Bodemer & Dehler, 2011 ) as learners tend to engage in non-task-related conversations during CSCL (Abedin et al., 2011 ; Vogler et al., 2019 ). Beyond mere participation in interaction, recent research has shifted attention towards regulatory process of interaction during collaborative learning. Contemporary GA tools aggregate and visualize multi-dimensional information, such as level of knowledge and skill, contribution, interaction frequency, which allows group members to be informed about individual and group status, thus facilitating the process of interaction between group members on the basis of the shared information (Tang et al., 2025 ; Wang et al., 2019 ). GA support has shown to benefit the collaborative process by supporting joint regulation (Ma et al., 2023 ; Schnaubert & Bodemer, 2019 ), which enable learners to perceive stronger social connectedness and assume greater responsibility for the collective task (Yilmaz & Karaoglan Yilmaz, 2020 ). Ultimately, this regulatory support aims to transition groups from simple exchange to deeper knowledge construction (Li et al., 2021 ). Accordingly, the majority of existing studies assess these effects using aggregate measures, largely overlooking the temporal dynamics of collaboration. Understanding GA improves regulation is distinct from understanding how that regulation unfolds over time. 3. Research Questions Although prior research has established the benefits of GA for coordination and communication, yet how GA shapes the unfolding of verbal interaction behaviors over time remains underexplored. Collaborative learning is inherently regulatory and temporal, where the order in which utterances occur not only reveals information exchange but also exposes how groups progressively build shared knowledge (Lajoie et al., 2015 ). Prior studies often quantify interaction frequencies (e.g., counts of questions/agreements), thereby obscuring whether, for instance, a reached consensus sparks further negotiation or immediately shifts the group into task execution (Malmberg et al., 2017 ). To capture both types and the temporal sequences of discourse, researchers must move beyond aggregate frequencies toward process-sensitive metrics (Azevedo, 2014 ; Molenaar & Chiu, 2014 ). To address this concern, we employ two complementary techniques. Lag-Sequential Analysis (LSA) examines immediate behavioral transitions (i.e., whether a given speech act systematically elicits a specific follow-up act), such as whether offering an idea reliably triggers agreement or negotiation. These transitions are informative but inherently limited to adjacent utterances, while collaborative learning often involves extended chains of action in which learners cycle through proposing, questioning, clarifying, and agreeing across multiple turns before arriving at a shared resolution. Therefore, Sequential Pattern Mining (SPM) addresses this limitation by identifying recurring multi-step subsequences that exceed a minimum frequency threshold, capturing the broader collaborative trajectories through which groups progressively build shared understanding (Srikant & Agrawal, 1996 ). Together, they provide a process-oriented account that moves beyond aggregate interaction frequencies toward understanding how the sequential organization of verbal interaction differs across conditions. Specifically, our research questions are as follows: RQ 1 (Immediate Transition). What are the differences in immediate behavioral transitions of verbal interactions between groups with and without GA support in collaborative learning? RQ 2 (Extended Trajectory). What are the differences in extended behavioral trajectories of verbal interactions between groups with and without GA support in collaborative learning? 4. Methods 4.1 Participants The study was conducted in a university laboratory with 44 participants (22 dyads) aged 21 to 40, recruited from diverse academic disciplines, including Engineering, Education, and Mathematics. Institutional Review Board approval was obtained in advance, and all procedures conformed to applicable ethical guidelines. To minimize any confounding effects of prior acquaintance, participants were intentionally paired with partners whom they did not know. The participants sat back-to-back at separate computers with a partition between them and accessed a shared online task interface (Google Slides) designed to facilitate collaborative learning. In both conditions, dyads were presented with a collaborative design task (e.g., to design a pair of multi-functional knee pads for runners, which can help them protect their knees during training and competition), requiring them to discuss with each other via zoom, conceptualize a product together in response to the given scenario and describe it using as many characteristics as possible. 4.2 Research Design To identify the differences in transition and sequential patterns of learners’ verbal interactions during a collaborative learning task, this study adopted a counterbalanced experimental design comparing two conditions: with and without GA support. The two conditions were presented in a randomized order: 1) the control condition, where participants did not receive any GA support during the collaborative activity, and 2) the experimental condition, where participants were provided with GA support. Each pair experienced both control and experimental conditions in a randomized sequence to minimize potential order effects. Accordingly, it aims to provide empirical evidence that inform the influence of GA support on the transition and sequential structures of verbal interactions observed during the collaborative learning task. Group Awareness Design In the context of this study, GA is conceptualized as an awareness of peers’ ongoing ideas and work processes during collaborative learning (Bodemer & Dehler, 2011 ). In the control condition, participants collaborated without GA support and could not access their partner’s ideation workspace; they worked from their own ideas when contributing to the shared collaboration column. In the GA condition, participants received GA support during collaboration by being able to view their partner’s ideation/working column in real time (Fig. 1 ), which made each other’s evolving ideas visible while they jointly constructed the shared response. Participants were allowed to communicate during the collaborative phase. Collaborative Learning Task. Participants sat at separate computers to access an online platform (Google Slides) designed to support real-time collaboration through synchronized updates. Using a crossover design, each dyad completed three comparable product ideation tasks developed by the research team in a single session. Each task lasted 7 minutes and required participants to collaboratively design a common everyday product based on a practical scenario relevant to university students. Examples of the tasks included designing a multi-functional lunch box for NTU students to help reduce food waste and keep food fresh, and designing a multi-functional schoolbag for NTU students to reduce discomfort caused by heavy loads and Singapore’s hot and rainy weather during commuting. The tasks were intentionally designed to require discussion, negotiation, and integration of ideas. Verbal communication was allowed throughout the collaborative phase so that participants could jointly generate, develop, and refine their design solutions. 4.3 Data Collection All dyads engaged in real-time dialogue to conceptualize their collaborative learning task design. Participants were instructed to work together verbally to conceptualize and describe their product. While no minimum frequency of speech was enforced, all dyads engaged in real-time dialogue via Zoom to complete the task. The frequency and amount of verbal interaction naturally varied depending on each pair’s task progress. In the control condition (without GA support), data from 22 pairs were analyzed, resulting in a total of 1,204 utterances, with an average of approximately 55 utterances per pair. In the experimental condition (with GA support), data from 22 pairs were analyzed, comprising a total of 1,094 utterances, with an average of approximately 50 utterances per dyad. The variation in utterance counts reflects natural differences in collaborative styles and task progression across groups. Audio recordings were captured throughout the entire session and were transcribed using Whisper, an automatic speech recognition tool. To ensure transcription accuracy, two researchers independently reviewed and cross-checked transcripts against the original audio, resolving discrepancies through discussion to provide a reliable record of participant interactions for analysis. 4.4 Data Analysis Methods To address the research questions, this study utilized content analysis, lag sequential analysis (LSA; Bakeman & Gottman, 1997 ), and sequential pattern mining (SPM; Srikant & Agrawal, 1996 ). The section below details how each of these analytical methods was applied to answer the research question. 4.4.1 Content Analysis The coding scheme of verbal interaction is informed by three frameworks: the Interaction Analysis Model (IAM) by Gunawardena et al. ( 1997 ), Hou et al.’s (2011) coding framework, and Wang et al.’s ( 2020 ) verb-centric approach. IAM focuses on the social construction of knowledge through stages such as sharing information, identifying cognitive dissonance, and co-constructing and testing new knowledge. Hou et al. (2011) further advance this by developing a coding scheme that emphasizes task coordination in collaborative learning settings, capturing how students organize and manage tasks during group interactions. Wang et al. ( 2020 ) later refined these ideas with a focus on analyzing students’ interactive behaviors in synchronous online collaborative learning, centering on academic relevance, social connectivity, and off-topic behaviors. As Rourke and Anderson ( 2004 ) pointed out, employing a coding scheme grounded in literature reviews or widely adopted by previous researchers can enhance validity. Adapting from these frameworks, the coding scheme of this study places particular emphasis on two dimensions: knowledge construction and task coordination. These dimensions reflect behaviors such as information sharing, meaning negotiation, and task organization, with task coordination being a core focus, in line with Hou et al.’s (2011) work. The unit of analysis is a verbally articulated interaction behavior, represented by a complete sentence or utterance conveying a clear communicative intent. Two coders utilized a coding framework adapted from Wang et al. ( 2020 ) (Table 1 ) to code the verbal content of each group based on their turn-taking sequences to identify interaction behavior patterns.Prior to formal content coding, two coders held calibration sessions in which they (a) reviewed representative transcript excerpts, (b) aligned their interpretations of every symbol and code in Table 1 , and (c) refined decision rules for ambiguous cases. To establish inter-coder reliability, a subset of the data was independently coded by both coders, yielding a Cohen’s kappa of 0.894, which indicates strong agreement. After reliability had been established, the coders discussed and resolved disagreements and then finalized the coding for the full dataset. Table 1 Coding Scheme for Content Analysis Symbol Code Description Example A1 Offer ideas or help Provides ideas without others requesting it a) So, you said university students, right? A2 Ask for ideas or help Raise questions and attempt to get possible solutions and thoughts from other group members a) And so shall we just put in a function over here? b) Something that can be pushed? A3 Respond to information or questions Provide answers upon other group members; requests or show attitude to others’ statements a) Do you wanna write that down too? b) So, what do you suggest? A4 Agree with suggestions or opinions Show consistency of the solutions or instructions provided by other group members a) Okay, I get what you mean. b) Consider I would suggest this is. It’s fine. A5 Negotiate or challenge proposed opinions/actions Negotiate areas of inconsistency or show disagreement of proposed solutions or instructions a) So maybe it comes with the partition. I don’t know. b) No, I think it just means just the surface reduce waste, reduce the food waste. A6 Discover uncertainty or spot unclear contents Present the exact questions or show unclear contents (present/ ask exact questions on the task or show unclear contents to their partner) a) Sorry, what’s wrong with my spelling this right the room respectable. b) Recyclable material. Eco-friendly. A7 Lead task coordination or guide group actions Instructions that guide group members to perform certain behaviors, e.g. assign tasks and discuss strategies a) Sorry. Do you wanna write to that down too? You can do it in carry on. Okay, sorry. Okay, 3 wheels. A8 Check or report the progress of the learning task Check other group members’ working progress of the given task or report his/her own progress to others (check or report the group’s working progress of the given task) a) Okay, so my side, I wrote something about there’s a warm heating and a cooling effect. b) Right. A9 Contents irrelevant to the learning task Content that are irrelevant to the given task a) Yup. Uh. 4.4.2 Lag Sequential Analysis (LSA) To address RQ1, this study adopted Lag Sequential Analysis (LSA) to explore the immediate behavioral transitions of verbal interactions between groups with and without GA support in collaborative learning. A pattern of behavior refers to the sequential relationship between the contents of a coded discussion and can be determined by calculating the statistical significance of a sequence of another behavior that follows a particular behavior (Wang et al., 2020 ). In this study, GSEQ 5.1 was used for LSA to examine the statistical significance of certain behavioral patterns exhibited by dyads under different conditions. Particularly, it was used to calculate the frequency of each behavioral type in succession and the adjusted residual results of transitions for different levels of two corresponding conditions. The adjusted residual results (Z-score) of each behavioral transition determined whether the subsequently discussed behaviors were significant (Z-scores > 1.96, Bakeman & Gottman, 1997 ). We then depicted the behavior transition diagrams for all sequences that reached statistical significance (Fig. 2 ). 4.4.3 Sequential Pattern Mining (SPM) To address RQ2, this study adopted Sequential Pattern Mining (SPM) to explore the extended behavioral trajectories of verbal interactions between groups with and without GA support in collaborative learning. First introduced by Srikant and Agrawal ( 1996 ), SPM aims to uncover frequently occurring subsequences within sequential data, emphasizing both their occurrence frequency and structural length. In this study, we employ SPM using the Tranmine package in R (Zaki, 2001 ). After quantifying temporal dependencies between consecutive behaviors through lag-sequential analysis (LSA), the method identifies representative subsequences that surpass a predefined frequency threshold, referred to as the “support value.” A subsequence qualifies as frequent if its support value meets or exceeds the minimum threshold, commonly referred to as “minimum support” (Zhang & Paquette, 2023 ). To ensure the retention of significant patterns, we restrict our analysis to subsequences with a support value of at least 0.6 (Yang et al., 2022 ). Furthermore, single-event patterns are excluded to preserve the temporal ordering of events, with the subsequence lengths limited to a minimum of two and a maximum corresponding to the shortest sequence in the dataset. 5. Findings & Discussion 5.1 Immediate Behavioral Transitions of Verbal Interactions With/Without Group Awareness Support The sequential analysis of students’ interaction behavior within experimental and control conditions is applied, and the results are presented in Tables A1, A2, A3, and A4 (see Appendix). These tables show the Z-score in which learners’ behavior from one (in each row) to another (in each column). Based on these tables, a behavior transition diagram is drawn for each condition, as shown in Fig. 2 , showing those sequences that reach a significant effect. The significant sequence is the sequence with a Z-score of more than 1.96 (Bakeman & Quera, 1995). Each transition in Fig. 2 has both significance and Z-score represented on each line. The findings reveal how GA support influences collaborative learning behaviors, shedding light on the awareness support that facilitates coordination and interaction in collaborative learning. The analysis confirms that essential collaborative behaviors, such as offering information (A1) and reaching agreement (A4), are observed in both conditions. In the control group, the bidirectional interactions between offering help (A1) and agreeing with ideas (A4) are significant. These exchanges suggest that learners frequently moved from proposing ideas to acknowledging, accepting, and further developing them, indicating a pattern of idea uptake and emerging consensus that may support task coordination (Chen et al., 2009; Bereiter & Scardamalia, 2014). Students contributed their own ideas to address the collaborative task and often received immediate uptake from their partners. For example, in one exchange, a learner proposed a concrete action for combining ideas, and the partner immediately confirmed the proposal. In another exchange, a learner introduced a possible product feature, and the partner responded by accepting and elaborating the idea. Together, these examples illustrate how verbal interaction moved beyond isolated idea sharing toward jointly endorsed and progressively refined solutions. Similarly, in the experimental group, this bidirectional behavioral path occurs more frequently, indicating that GA support enhances the efficient exchange of information by offering a clearer understanding of group progress (Strauß & Rummel, 2020; Wang et al., 2020). Additionally, the behavior path A2→A3 occurs in both conditions indicating a supportive online collaborative learning environment (Wang et al., 2020). When students requested information or assistance, their group members typically provided relevant responses. Furthermore, the transition from A6 to A3 indicates that the group is engaged in active dialogue and mutual support, with members stepping in to address ambiguities and ensure shared understanding. It shows that participants are not only aware of their own uncertainties but also rely on group members to help clarify those issues, leading to enhanced collective problem-solving and learning (Rojas et al., 2022; Schnaubert & Bodemer, 2022). A key pattern in the control group is the transition from agreeing (A4) to leading (A7). This behavior suggests that once agreement was reached, participants often took charge of task coordination, assigning roles, and guiding actions. In contrast, in the experimental group, the transition from agreement (A4) to monitor and report progress (A8) and then to responding to information (A3) was notable. This path reflects the impact of GA support, as it offers real-time information on the group’s activities, leading to more focused interactions. This transition demonstrates GA support can ensure that after agreeing on ideas or task-related information, progress is consistently monitored, and responses are informed by a clear understanding of both the individual and collective contributions. This leads to a smoother, more synchronized collaborative effort. Another different behavioral transition in the experimental group is the path from offering help (A1) to negotiation (A5), with a strong self-loop within negotiation (A5 to A5). This suggests that GA facilitates a more structured approach to problem-solving, where participants offer solutions but also engage in critical evaluation through negotiation (Lin, 2018; Schnaubert & Bodemer, 2019). In contrast, the control group demonstrates more reliance on the transition from agreeing (A4) to leading (A7), reflecting a more fluid dynamic where leadership emerges spontaneously after agreement. The experimental group demonstrates that with the support of GA, instructions related to group coordination and adjustments consistently receive prompt responses from team members (A3). This responsiveness highlights the effectiveness of GA in facilitating efficient communication and task management within the group. In contrast, while the control group exhibit a higher frequency of self-loop behavior (A8 → A8), which reflects ongoing task monitoring, this behavior can become counterproductive if it devolves into a repetitive cycle of progress checking without advancing the task. Although constant task tracking is important in some contexts, excessive reliance on it without taking action or addressing new challenges may hinder the group’s overall efficiency and progress. 5.2 Extended Behavioral Trajectories of Verbal Interactions With/Without Group Awareness Support To further investigate the influence of GA support on the participants’ verbal interactions in collaborative learning, this study examined the frequency of interaction behavior subsequences using a minimum support threshold of 0.6. The analysis identified 21 representative subsequences in the experimental group (with GA support), exceeding the 18 subsequences observed in the control group (without GA support) (see Table 2). This discrepancy underscores the enhanced richness and complexity of interaction patterns fostered by GA (Li et al., 2021). To better understand the structural composition of these subsequences, we group them into core itemsets based on shared behavioral components. For instance, the sequence {reaching agreement (A4) → seeking assistance (A2) → providing ideas or support (A1)} (Panel A. SID 4) encompasses the behavioral trajectories (A4 → A1), (A4 → A2), and (A2 → A1) (Panel A. SID 1-3) as its integral subsequences. The categorization reveals six distinct itemsets in the experimental group, exceeding the four itemsets identified in the control group, thereby offering deeper insights into the structure and organization of collaborative behaviors. Table 2 Subsequences With/Without Group Awareness Support ID Itemset Subsequence Support Value Count Panel A. Experimental Group (With GA) 1 A1, A2, A4 A4 → A1 1.000 22 2 A2 → A1 0.864 19 3 A4 → A2 0.864 19 4 A4 → A2 → A1 0.864 19 5 A1, A4, A7 A7 → A1 0.727 16 6 A4 → A7 0.727 16 7 A4 → A7 → A1 0.727 16 8 A1, A3, A4 A3 → A1 0.727 16 9 A4 → A3 0.727 16 10 A4 → A3 → A1 0.727 16 11 A1, A2, A3, A4 A3 → A2 0.682 15 12 A3 → A2 → A1 0.682 15 13 A4 → A3 → A2 0.682 15 14 A4 → A3 → A2 → A1 0.682 15 15 A1, A4, A6 A6 → A1 0.682 15 16 A4 → A6 0.682 15 17 A4 → A6 → A1 0.682 15 18 A1, A2, A4, A6 A6 → A2 0.636 14 19 A6 → A2 → A1 0.636 14 20 A4 → A6 → A2 0.636 14 21 A4 → A6 → A2 → A1 0.636 14 Panel B. Control Group (Without GA) 1 A1, A4, A5 A4 → A1 1.000 22 2 A5 → A1 0.864 19 3 A5 → A4 0.864 19 4 A5 → A4 → A1 0.864 19 5 A1, A2, A4 A2 → A1 0.864 19 6 A4 → A2 0.864 19 7 A4 → A2 → A1 0.864 19 8 A1, A2, A4, A5 A5 → A2 0.773 17 9 A5 → A2 → A1 0.773 17 10 A5 → A4 → A2 0.773 17 11 A5 → A4 → A2 → A1 0.773 17 12 A1, A2, A3, A4 A3 → A2 0.636 14 13 A3 → A1 0.636 14 14 A4 → A3 0.636 14 15 A3 → A2 → A1 0.636 14 16 A4 → A3 → A2 0.636 14 17 A4 → A3 → A1 0.636 14 18 A4 → A3 → A2 → A1 0.636 14 Note. Minimum support threshold is 0.6. Among the 39 representative subsequences identified, the most frequent pattern is the transition from reaching agreement (A4) to offering ideas or help (A1). This finding underscores the foundational role of consensus building in collaborative learning, while aligning with the behavioral path highlighted through the aforementioned lag sequential analysis. Specifically, the transition (A4→A1) illustrates how establishing consensus facilitates proactive contributions, enabling participants to fully comprehend and build upon each other’s contributions, which is critical for collaborative knowledge construction (Gijlers et al., 2009; Sangin et al., 2011). To provide a broader perspective on the collaborative learning process, this study extends the analysis of transitions into more complex sequences. For example, the sequence {reaching agreement (A4) → help seeking (A2) → offering ideas or help (A1)} (Panel A. SID 1-4; Panel B. SID 5-7) represents a collaborative trajectory where participants not only achieve consensus but also actively address uncertainties or knowledge gaps through help-seeking behaviors before contributing solutions. This sequence highlights the iterative nature of problem-solving in collaborative contexts, which integrates cognitive processes, such as information seeking and synthesis, with social dynamics, including shared understanding and ambiguity resolution (Graesser et al., 2018). Another extended sequence, {reaching agreement (A4) → responding to information (A3) → help seeking (A2) → offering ideas or help (A1)} (Panel A. SID 11-14; Panel B. SID 12-18), exemplifies how consensus building (A4) transitions into active engagement with shared information (A3). This engagement enables team members to clarify ambiguities and refine their understanding through help-seeking (A2) before contributing actionable ideas or solutions (A1), thus underscoring the pivotal role of information sharing and knowledge exchange in fostering effective collaboration. By activating prior knowledge and guiding the flow of information, it enhances the depth and quality of the collaborative learning process (Erkens & Bodemer, 2019). In contrast, patterns observed in the control group, such as A5→A4→A1 (Panel B. SID 1-4) and A5→A4→A2→A1 (Panel B. SID 8-11), reinforce lag sequential findings that highlighted a reliance on negotiation. The sequence reveals a prolonged cycle of negotiation (A5), reaching consensus (A4), followed by slower transitions to actionable behaviors like task execution (A1) or help-seeking (A2). This slower progression underscores how the absence of GA hampers interaction efficiency, limiting processes such as joint negotiation of meaning and co-construction of knowledge (Buder & Bodemer, 2008; Hull & Saxon, 2009). This study underscores the critical role of GA in shaping collaborative learning dynamics and fostering effective verbal interactions. Findings from both analytical approaches reveal that GA support enhances task coordination, critical evaluation, and structured problem-solving. For example, the experimental group demonstrated superior responsiveness and adaptability through sequences like A4→A6→A2→A1 (Panel A. 18-21), showcasing their ability to identify and address uncertainties efficiently. In contrast, the control group relied on less effective patterns, such as A5→A4→A2→A1 (Panel B. SID 8-11), where prolonged negotiation lacked decisive leadership or structured problem-solving. Additionally, GA facilitated continuous progress monitoring and synchronized collaboration in the experimental group, mitigating unproductive cycles like the repetitive self-loop (A8→A8) observed in the control group. By enabling better regulation of behavior, meaningful negotiation, and targeted problem-solving, GA not only streamlines the collaborative process but also provides valuable insights for designing and implementing GA supported learning environments in educational contexts. 6. Limitations This study contributes to the existing literature on collaborative learning and GA support by exploring the role of GA support in influencing learners’ verbal interactions. Theoretically, the findings advance our understanding of how GA support can enhance coordination and foster deeper engagement in collaborative learning environments. By exploring both behavioral transitions and sequential patterns of verbal interactions, this study highlights the dynamic processes through which GA support impacts the development of shared understanding and knowledge co-construction. Practically, the study provides valuable insights for educators and instructional designers on how to implement GA support in collaborative learning tools, with the potential to improve student engagement, foster more balanced participation, and facilitate deeper cognitive processing during collaborative tasks. This study has some limitations. First, the analysis focused exclusively on verbal communication data, which, while insightful, may not fully capture the complexity of collaborative learning processes. Second, the experimental design involved a short time frame for collaborative tasks, which may not reflect the full development of group dynamics that typically unfold over extended periods in real-world settings. This limited duration could constrain the understanding of how collaboration evolves and how GA support impacts longer-term learning and behavioral changes. To address these limitations, future research could adopt a multimodal approach, integrating online data as well as self-reported data such interview and survey to capture more comprehensive picture of the role of GA support during collaborative learning process. These methods could provide richer insights into how GA tools influence both observable behaviors and internal processes like reflection, metacognition, and motivation. Additionally, longitudinal studies exploring collaboration over extended durations could offer more robust findings, shedding light on the sustained effects of GA support on both the social and cognitive dimensions of collaborative learning. Such approaches would contribute to a more comprehensive understanding of how collaborative learning processes is shaped by GA interventions. 7. Conclusion This study explores the role of GA in influencing students’ verbal interaction patterns in collaborative learning environments. Through an experimental design of collaborative learning with and without GA support, we explored how GA influences the transition and sequential patterns of verbal interactions during collaborative learning tasks. The findings reveal two complementary effects: First, GA streamlined task-coordination moves, allowing learners to align actions and decisions more efficiently. Second, it diversified the sequential structure of knowledge-building discourse, leading to more diverse interaction patterns. These results extend earlier evidence that GA support promotes mutual monitoring and shared regulation (Bodemer & Dehler, 2011 ; Janssen & Bodemer, 2013 ), which facilitates more effective decision-making and alignment of collaborative efforts (Järvelä et al., 2016 ). Learners with GA support engaged in more targeted, task-oriented exchanges and produced richer discourse networks, consistent with findings on improved precision in monitoring (Ghadirian et al., 2016 ) and structured interaction (Phielix et al., 2011 ; Schnaubert & Bodemer, 2022 ). This improved monitoring enhances the focus and quality of collaborative exchanges interaction, supporting both functionality and the generation of creative ideas. Despite the benefits of GA support in enhancing coordination, the findings also point to a potential trade-off. While GA can reduce the need for prolonged negotiation, it sometimes reduced opportunities for engagement through critical debate, a socio-cognitive process essential to deep understanding (Gillies, 2019 ). Therefore, GA support should be designed to balance the efficiency with features that preserve for reflection and conflict. Declarations Author Contribution Conceptualization: [Wenli C., Lishan Z., Xuanyu C.]Methodology: [Lishan Z., Xuanyu C.]Investigation: [Wenli C., Lishan Z., Xuanyu C.; Hua H.; M.-Y.M.H.]Data curation: [Wenli C ., Lishan Z., Xuanyu C.; Hua H.; M.-Y.M.H.]Formal analysis: [Lishan Z., Xuanyu C.]Writing – original draft: [Wenli C., Lishan Z., Xuanyu C.]Writing – review & editing: [Wenli C., Lishan Z., Xuanyu C.] Acknowledgement This research was funded by the National Institute of Education Research Support for Senior Academic Administrator (RS-SAA) Grant (Grant number: RS 1/22 CWL) and administered by the National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NIE and NTU. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9319024","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627864271,"identity":"862cfa78-2aea-45d6-969e-061e03bde212","order_by":0,"name":"Wenli chen","email":"","orcid":"","institution":"National Institute of Education, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"chen","suffix":""},{"id":627864272,"identity":"a37c15a5-dc93-48f1-aaad-fb5b4f601010","order_by":1,"name":"Lishan Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABG0lEQVRIie3RsUrEMBjA8S8E0uWTW7+i2FfI0UEOCvVREgSnUwRBbpCjUOgtfYAbFF9B3yAlcNOhr+Dh4KKQURHEeHpObXEUzH8K4fuRpAUIhf5iHNjKAeyIqDTGbXaxn/CU/MwAF7qZb6Z7CYAgP4DxfJxa/A3Zi7ghgilKc6hsNoHjnBS7f64gT/bbyagUSp6A8MQaO17CKZLiw8sK9I1pJ9KiVASIsqmUPfKTNSmxvVWBGhZdZOAMAuGntaP3NYnePMm7CbICQWJcecKKr1O4J+yq4/nSipSTVP4jC9XUC9L1clXGF7ekr7vInX18dZPprkgerHs5z/RsdtC4p7MsTzou9g1/Vv6vAivWC2n6SGv9p4RCodA/6gOH6VOnSKHxhQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Education, Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Lishan","middleName":"","lastName":"Zheng","suffix":""},{"id":627864273,"identity":"8d6f9e2f-ff7d-4cfb-be37-ea43b17989d1","order_by":2,"name":"Xuanyu Chen","email":"","orcid":"","institution":"Institute of Higher Education, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xuanyu","middleName":"","lastName":"Chen","suffix":""},{"id":627864274,"identity":"98a221a0-d731-4790-ba72-5375b38b3442","order_by":3,"name":"Mei Yee Mavis Ho","email":"","orcid":"","institution":"National Institute of Education, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"Yee Mavis","lastName":"Ho","suffix":""},{"id":627864275,"identity":"1d654ea9-5f37-45c8-8127-65bb544c799b","order_by":4,"name":"Hua Hu","email":"","orcid":"","institution":"Institute of Higher Education, Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2026-04-04 08:53:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9319024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9319024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107667580,"identity":"003dfe10-965c-4339-996e-d7febdd0f217","added_by":"auto","created_at":"2026-04-23 19:30:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGroup Awareness Design\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9319024/v1/5e3e9c703eb35e98f32d769c.png"},{"id":107706811,"identity":"2f21aa4e-3030-4538-bf6b-17cbd5c726b0","added_by":"auto","created_at":"2026-04-24 09:18:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBehavioral Transition Diagrams of Different Conditions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote.\u003c/strong\u003e\u003c/em\u003e Each transition has both significance and Z-score represented on each line.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9319024/v1/ff56edd2343fd917061435cd.png"},{"id":107709054,"identity":"ce7191f1-965d-4170-8449-057eadfed673","added_by":"auto","created_at":"2026-04-24 09:34:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":686357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9319024/v1/7e471a88-8342-49be-8ace-4dc6c6e6fd31.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Students’ Verbal Interaction Patterns in Collaborative Learning: The Role of Group Awareness in Improving Task Coordination","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCollaborative learning is one of the important 21st century competencies. A key strategy of collaborative learning is the effective sharing of knowledge and information (Bodemer \u0026amp; Dehler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Phielix et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pifarr\u0026eacute; et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Without active knowledge-sharing, the benefits of collaborative learning are significantly diminished (Baker \u0026amp; Reimann, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kwon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, effective knowledge sharing does not necessarily emerge spontaneously in collaborative settings (Kreijns et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kreijns et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For learners to exchange and integrate ideas productively, they must coordinate their collaborative activity by managing participation, aligning contributions, monitoring progress, and negotiating next steps (Dillenbourg, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Janssen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Roschelle \u0026amp; Teasley, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).Collaborative learning requires group members to manage diverse tasks while coordinating efforts of multiple individuals, each bringing unique perspectives. This coordination is essential as group members work toward achieving a shared understanding within the context of a joint task (Dillenbourg, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Roschelle \u0026amp; Teasley, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeveraging collaborative interaction to improve learning processes and outcomes is a central goal of Computer-Supported Collaborative Learning (CSCL). However, effective interaction depends not only on learners\u0026rsquo; willingness to participate, but also on their ability to coordinate their actions, align contributions, and respond to one another appropriately over time (Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Miller \u0026amp; Hadwin, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In collaborative learning, interaction is the medium through which learners exchange ideas, while coordination refers to the organizational processes that help structure these exchanges and align them with shared task goals (Hern\u0026aacute;ndez-Sell\u0026eacute;s et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Onrubia et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In CSCL environments, the reduced availability of perceptual and spatial cues may limit learners\u0026rsquo; awareness of what their peers are doing and how the joint work is progressing, thereby making it more difficult to align actions, time responses, and coordinate collaborative activity effectively (Bodemer \u0026amp; Dehler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gutwin \u0026amp; Greenberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gutwin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers have proposed group awareness (GA) support to address these coordination challenges by providing learners with real-time information about their group members\u0026rsquo; actions, progress, and contributions (Jermann \u0026amp; Dillenbourg, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By making such information visible, GA support can help learners better coordinate their collaborative activity, for example by aligning actions, monitoring progress, and responding to one another in a timely manner (Benware \u0026amp; Deci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Janssen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It may also create conditions for more meaningful task-related interaction by making peers\u0026rsquo; work and contributions more interpretable during collaborative learning (Buder \u0026amp; Bodemer, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gutwin \u0026amp; Greenberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kimmerle \u0026amp; Cress, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo facilitate GA in CSCL, Information and Communication Technology (ICT) tools have been developed to provide students with relevant information about their peers. Prior studies have demonstrated that GA contributes to improved learning outcomes by supporting learners in monitoring and regulating group processes (Janssen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the underlying behavioral evidence through which GA impacts collaborative learning remains insufficiently understood, particularly insufficiently understood, there is limited fine-grained evidence on how students\u0026rsquo; verbal interaction patterns unfold over time and how these patterns reflect processes of task coordination and regulation during collaborative learning (Kreijns et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lyu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As CSCL environments increasingly integrate GA tools (Bodemer \u0026amp; Dehler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), more research is needed to understand the influence of GA on learners\u0026rsquo; collaborative learning behaviors including students\u0026rsquo; verbal interactions.\u003c/p\u003e \u003cp\u003e This study aims to examine the role of GA on learners\u0026rsquo; verbal interaction, including behavioral transition and sequential patterns in collaborative learning. By analyzing differences in verbal interaction patterns across conditions with and without GA support, this study aims to generate fine-grained evidence about the behavioral mechanisms through which GA shapes collaborative learning. Such findings can contribute to a better understanding of how coordination unfolds over time and inform the design of CSCL environments that better support effectively collaborative learning\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Group Awareness in CSCL\u003c/h2\u003e \u003cp\u003eCompared to face-to-face settings, coordination in CSCL is complicated by limited perceptual information, learners cannot readily determine what tasks their partners are working on, what they know, or how they are contributing (Baker \u0026amp; Reimann, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gutwin \u0026amp; Greenberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Janssen \u0026amp; Bodemer, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). GA, broadly defined as the state of being informed about relevant aspects of group members or the group as a whole, helps address the coordination issues (Dourish \u0026amp; Bellotti, 1992; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The construct encompasses three types: social, concerning the interpersonal dynamics and participation levels within a group (B\u0026oslash;dker \u0026amp; Christiansen, 2006; Zheng et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); cognitive, concerning group members\u0026rsquo; knowledge states and supporting content-focused discussion (Ma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and behavioral, concerning observable actions such as contribution frequency and task progress (Janssen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pifarr\u0026eacute; et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGA tools operationalize these types by visualizing individual and group information, making otherwise implicit group processes externally accessible (Chen, Peng, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Phielix et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Each dimension contributes to collaborative learning through distinct type. Cognitive GA tools direct learners\u0026rsquo; attention to unresolved or conflicting content, prompting deeper engagement and socio-cognitive conflict that catalyzes negotiation and mutual understanding (Heimbuch \u0026amp; Bodemer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ollesch et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Social awareness tools facilitate interpersonal interaction by keeping learners informed about others\u0026rsquo; presence and responsiveness, supporting turn-taking and the alignment of collaborative efforts (Kreijns et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Phielix et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Behavioral awareness tools enable group members to assess collective progress and individual contributions, generating external feedback that promotes coordination toward shared goals (Ma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Empirical evidence consistently demonstrates that these tools improve the coordination of collaborative processes, enhance discussion quality, and support more balanced participation and effective task coordination (Janssen \u0026amp; Bodemer, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Janssen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sangin et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, while the benefits of GA for collaborative processes and outcomes are well established, its influence on the temporal structure of verbal interaction, especially how learners coordinate, respond, clarify, and build on one another\u0026rsquo;s contributions as collaboration unfolds, remains underexplored, leaving open the question of whether GA reshapes not only what learners discuss but also the sequential patterns through which coordination is achieved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Role of Verbal Interaction in Collaborative Learning\u003c/h2\u003e \u003cp\u003eCollaborative learning outcomes are not always superior to individual learning outcomes (Barron, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zambrano et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as effective collaborative learning depends on a social, interdependent process that thrives on seamless communication, shared knowledge, and mutual consensus (Dado \u0026amp; Bodemer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In collaborative learning environments, learners engage in socially shared regulation of cognition, metacognition, affect, and motivation, collectively working toward shared goals while fostering trust, community, and a sense of belonging (Kreijns et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This reciprocal regulation renders collaborative learning inherently dialogic (Arvaja \u0026amp; H\u0026auml;m\u0026auml;l\u0026auml;inen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Verbal interaction is the primary medium through which learners co-regulate contributions, jointly construct knowledge, and negotiate meaning (Chen, Zheng, et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stahl, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stahl et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch highlights that quality verbal interactions significantly shape collaborative learning effectiveness (Vuopala et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Rooted in social-constructivist theory, collaborative learning relies on verbal exchanges, such as discussions and argumentation, for dynamic knowledge construction and conceptual development (Sun \u0026amp; Yang, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lee \u0026amp; Smagorinsky, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The verbal interaction encompasses not only the exchange of ideas but also the processes of negotiation, elaboration, and knowledge co-construction (Malmberg et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It shapes the trajectory of learning by enabling groups to refine ideas (Hu \u0026amp; Chen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), coordinate tasks (Scardamalia \u0026amp; Bereiter, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) through social interaction.\u003c/p\u003e \u003cp\u003e These verbal exchanges not only reveal learners\u0026rsquo; comprehension but also allow the evaluation of both the quantity and quality of learning (Palinscar, 1998). The dialogic nature of verbal interaction ensures that learning is socially mediated, with cognitive development occurring through meaningful exchanges within group contexts (Vygotsky, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Consequently, understanding and leveraging the dynamics of verbal interaction is essential for fostering effective collaborative learning and advancing educational practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Relationship of Group Awareness Support and Verbal Interaction\u003c/h2\u003e \u003cp\u003eUnlike face-to-face contexts, insufficient interaction frequently stems from the limitations of the CSCL environment in fostering social perceptions and connections (Kreijns et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, earlier attempts to study the effect of GA support focus on participation in interaction in response to problems such as social loafing and free riding in CSCL contexts (Janssen \u0026amp; Bodemer, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Empirical evidence indicates the use of GA tools exhibit positive effects on facilitating group interaction and stimulating group members to participate equally in discussion and conversation (Janssen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Phielix et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). By creating the opportunity to observe others\u0026rsquo; interactions and contributions to the group, GA support can increase students\u0026rsquo; participation in discussion (Lin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although guiding interaction processes has been regarded as a key function of GA support, an increase in participation does not inherently guarantee fruitful collaboration (Bodemer \u0026amp; Dehler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) as learners tend to engage in non-task-related conversations during CSCL (Abedin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vogler et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond mere participation in interaction, recent research has shifted attention towards regulatory process of interaction during collaborative learning. Contemporary GA tools aggregate and visualize multi-dimensional information, such as level of knowledge and skill, contribution, interaction frequency, which allows group members to be informed about individual and group status, thus facilitating the process of interaction between group members on the basis of the shared information (Tang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). GA support has shown to benefit the collaborative process by supporting joint regulation (Ma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which enable learners to perceive stronger social connectedness and assume greater responsibility for the collective task (Yilmaz \u0026amp; Karaoglan Yilmaz, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ultimately, this regulatory support aims to transition groups from simple exchange to deeper knowledge construction (Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accordingly, the majority of existing studies assess these effects using aggregate measures, largely overlooking the temporal dynamics of collaboration. Understanding GA improves regulation is distinct from understanding how that regulation unfolds over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Questions","content":"\u003cp\u003e Although prior research has established the benefits of GA for coordination and communication, yet how GA shapes the unfolding of verbal interaction behaviors over time remains underexplored. Collaborative learning is inherently regulatory and temporal, where the order in which utterances occur not only reveals information exchange but also exposes how groups progressively build shared knowledge (Lajoie et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Prior studies often quantify interaction frequencies (e.g., counts of questions/agreements), thereby obscuring whether, for instance, a reached consensus sparks further negotiation or immediately shifts the group into task execution (Malmberg et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To capture both types and the temporal sequences of discourse, researchers must move beyond aggregate frequencies toward process-sensitive metrics (Azevedo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Molenaar \u0026amp; Chiu, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To address this concern, we employ two complementary techniques. Lag-Sequential Analysis (LSA) examines immediate behavioral transitions (i.e., whether a given speech act systematically elicits a specific follow-up act), such as whether offering an idea reliably triggers agreement or negotiation. These transitions are informative but inherently limited to adjacent utterances, while collaborative learning often involves extended chains of action in which learners cycle through proposing, questioning, clarifying, and agreeing across multiple turns before arriving at a shared resolution. Therefore, Sequential Pattern Mining (SPM) addresses this limitation by identifying recurring multi-step subsequences that exceed a minimum frequency threshold, capturing the broader collaborative trajectories through which groups progressively build shared understanding (Srikant \u0026amp; Agrawal, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Together, they provide a process-oriented account that moves beyond aggregate interaction frequencies toward understanding how the sequential organization of verbal interaction differs across conditions. Specifically, our research questions are as follows:\u003c/p\u003e \u003cp\u003eRQ 1 (Immediate Transition). What are the differences in immediate behavioral transitions of verbal interactions between groups with and without GA support in collaborative learning?\u003c/p\u003e \u003cp\u003eRQ 2 (Extended Trajectory). What are the differences in extended behavioral trajectories of verbal interactions between groups with and without GA support in collaborative learning?\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Participants\u003c/h2\u003e \u003cp\u003eThe study was conducted in a university laboratory with 44 participants (22 dyads) aged 21 to 40, recruited from diverse academic disciplines, including Engineering, Education, and Mathematics. Institutional Review Board approval was obtained in advance, and all procedures conformed to applicable ethical guidelines. To minimize any confounding effects of prior acquaintance, participants were intentionally paired with partners whom they did not know.\u003c/p\u003e \u003cp\u003eThe participants sat back-to-back at separate computers with a partition between them and accessed a shared online task interface (Google Slides) designed to facilitate collaborative learning. In both conditions, dyads were presented with a collaborative design task (e.g., to design a pair of multi-functional knee pads for runners, which can help them protect their knees during training and competition), requiring them to discuss with each other via zoom, conceptualize a product together in response to the given scenario and describe it using as many characteristics as possible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Research Design\u003c/h2\u003e \u003cp\u003e To identify the differences in transition and sequential patterns of learners\u0026rsquo; verbal interactions during a collaborative learning task, this study adopted a counterbalanced experimental design comparing two conditions: with and without GA support. The two conditions were presented in a randomized order: 1) the control condition, where participants did not receive any GA support during the collaborative activity, and 2) the experimental condition, where participants were provided with GA support. Each pair experienced both control and experimental conditions in a randomized sequence to minimize potential order effects. Accordingly, it aims to provide empirical evidence that inform the influence of GA support on the transition and sequential structures of verbal interactions observed during the collaborative learning task.\u003c/p\u003e \u003cp\u003e\u003cb\u003eGroup Awareness Design\u003c/b\u003e In the context of this study, GA is conceptualized as an awareness of peers\u0026rsquo; ongoing ideas and work processes during collaborative learning (Bodemer \u0026amp; Dehler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the control condition, participants collaborated without GA support and could not access their partner\u0026rsquo;s ideation workspace; they worked from their own ideas when contributing to the shared collaboration column. In the GA condition, participants received GA support during collaboration by being able to view their partner\u0026rsquo;s ideation/working column in real time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which made each other\u0026rsquo;s evolving ideas visible while they jointly constructed the shared response. Participants were allowed to communicate during the collaborative phase.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eCollaborative Learning Task.\u003c/b\u003e Participants sat at separate computers to access an online platform (Google Slides) designed to support real-time collaboration through synchronized updates. Using a crossover design, each dyad completed three comparable product ideation tasks developed by the research team in a single session. Each task lasted 7 minutes and required participants to collaboratively design a common everyday product based on a practical scenario relevant to university students. Examples of the tasks included designing a multi-functional lunch box for NTU students to help reduce food waste and keep food fresh, and designing a multi-functional schoolbag for NTU students to reduce discomfort caused by heavy loads and Singapore\u0026rsquo;s hot and rainy weather during commuting. The tasks were intentionally designed to require discussion, negotiation, and integration of ideas. Verbal communication was allowed throughout the collaborative phase so that participants could jointly generate, develop, and refine their design solutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Data Collection\u003c/h2\u003e \u003cp\u003eAll dyads engaged in real-time dialogue to conceptualize their collaborative learning task design. Participants were instructed to work together verbally to conceptualize and describe their product. While no minimum frequency of speech was enforced, all dyads engaged in real-time dialogue via Zoom to complete the task. The frequency and amount of verbal interaction naturally varied depending on each pair\u0026rsquo;s task progress. In the control condition (without GA support), data from 22 pairs were analyzed, resulting in a total of 1,204 utterances, with an average of approximately 55 utterances per pair. In the experimental condition (with GA support), data from 22 pairs were analyzed, comprising a total of 1,094 utterances, with an average of approximately 50 utterances per dyad. The variation in utterance counts reflects natural differences in collaborative styles and task progression across groups.\u003c/p\u003e \u003cp\u003eAudio recordings were captured throughout the entire session and were transcribed using Whisper, an automatic speech recognition tool. To ensure transcription accuracy, two researchers independently reviewed and cross-checked transcripts against the original audio, resolving discrepancies through discussion to provide a reliable record of participant interactions for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data Analysis Methods\u003c/h2\u003e \u003cp\u003eTo address the research questions, this study utilized content analysis, lag sequential analysis (LSA; Bakeman \u0026amp; Gottman, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and sequential pattern mining (SPM; Srikant \u0026amp; Agrawal, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The section below details how each of these analytical methods was applied to answer the research question.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Content Analysis\u003c/h2\u003e \u003cp\u003e The coding scheme of verbal interaction is informed by three frameworks: the Interaction Analysis Model (IAM) by Gunawardena et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), Hou et al.\u0026rsquo;s (2011) coding framework, and Wang et al.\u0026rsquo;s (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) verb-centric approach. IAM focuses on the social construction of knowledge through stages such as sharing information, identifying cognitive dissonance, and co-constructing and testing new knowledge. Hou et al. (2011) further advance this by developing a coding scheme that emphasizes task coordination in collaborative learning settings, capturing how students organize and manage tasks during group interactions. Wang et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) later refined these ideas with a focus on analyzing students\u0026rsquo; interactive behaviors in synchronous online collaborative learning, centering on academic relevance, social connectivity, and off-topic behaviors.\u003c/p\u003e \u003cp\u003eAs Rourke and Anderson (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) pointed out, employing a coding scheme grounded in literature reviews or widely adopted by previous researchers can enhance validity. Adapting from these frameworks, the coding scheme of this study places particular emphasis on two dimensions: knowledge construction and task coordination. These dimensions reflect behaviors such as information sharing, meaning negotiation, and task organization, with task coordination being a core focus, in line with Hou et al.\u0026rsquo;s (2011) work.\u003c/p\u003e \u003cp\u003e The unit of analysis is a verbally articulated interaction behavior, represented by a complete sentence or utterance conveying a clear communicative intent. Two coders utilized a coding framework adapted from Wang et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to code the verbal content of each group based on their turn-taking sequences to identify interaction behavior patterns.Prior to formal content coding, two coders held calibration sessions in which they (a) reviewed representative transcript excerpts, (b) aligned their interpretations of every symbol and code in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and (c) refined decision rules for ambiguous cases. To establish inter-coder reliability, a subset of the data was independently coded by both coders, yielding a Cohen\u0026rsquo;s kappa of 0.894, which indicates strong agreement. After reliability had been established, the coders discussed and resolved disagreements and then finalized the coding for the full dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCoding Scheme for Content Analysis\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOffer ideas or help\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvides ideas without others requesting it\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) So, you said university students, right?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsk for ideas or help\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaise questions and attempt to get possible solutions and thoughts from other group members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) And so shall we just put in a function over here?\u003c/p\u003e \u003cp\u003eb) Something that can be pushed?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespond to information or questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvide answers upon other group members; requests or show attitude to others\u0026rsquo; statements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) Do you wanna write that down too?\u003c/p\u003e \u003cp\u003eb) So, what do you suggest?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree with suggestions or opinions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShow consistency of the solutions or instructions provided by other group members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) Okay, I get what you mean.\u003c/p\u003e \u003cp\u003eb) Consider I would suggest this is. It\u0026rsquo;s fine.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegotiate or challenge proposed opinions/actions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegotiate areas of inconsistency or show disagreement of proposed solutions or instructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) So maybe it comes with the partition. I don\u0026rsquo;t know.\u003c/p\u003e \u003cp\u003eb) No, I think it just means just the surface reduce waste, reduce the food waste.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscover uncertainty or spot unclear contents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent the exact questions or show unclear contents (present/ ask exact questions on the task or show unclear contents to their partner)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) Sorry, what\u0026rsquo;s wrong with my spelling this right the room respectable.\u003c/p\u003e \u003cp\u003eb) Recyclable material. Eco-friendly.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead task coordination or guide group actions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstructions that guide group members to perform certain\u003c/p\u003e \u003cp\u003ebehaviors, e.g. assign tasks and discuss strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) Sorry. Do you wanna write to that down too? You can do it in carry on. Okay, sorry. Okay, 3 wheels.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCheck or report the progress of the learning task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck other group members\u0026rsquo; working progress of the given task or report his/her own progress to others (check or report the group\u0026rsquo;s working progress of the given task)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) Okay, so my side, I wrote something about there\u0026rsquo;s a warm heating and a cooling effect.\u0026nbsp; b) Right.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContents irrelevant to the learning task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContent that are irrelevant to the given task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea) Yup. Uh.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Lag Sequential Analysis (LSA)\u003c/h2\u003e \u003cp\u003e To address RQ1, this study adopted Lag Sequential Analysis (LSA) to explore the immediate behavioral transitions of verbal interactions between groups with and without GA support in collaborative learning. A pattern of behavior refers to the sequential relationship between the contents of a coded discussion and can be determined by calculating the statistical significance of a sequence of another behavior that follows a particular behavior (Wang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, GSEQ 5.1 was used for LSA to examine the statistical significance of certain behavioral patterns exhibited by dyads under different conditions. Particularly, it was used to calculate the frequency of each behavioral type in succession and the adjusted residual results of transitions for different levels of two corresponding conditions. The adjusted residual results (Z-score) of each behavioral transition determined whether the subsequently discussed behaviors were significant (Z-scores\u0026thinsp;\u0026gt;\u0026thinsp;1.96, Bakeman \u0026amp; Gottman, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). We then depicted the behavior transition diagrams for all sequences that reached statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Sequential Pattern Mining (SPM)\u003c/h2\u003e \u003cp\u003e To address RQ2, this study adopted Sequential Pattern Mining (SPM) to explore the extended behavioral trajectories of verbal interactions between groups with and without GA support in collaborative learning. First introduced by Srikant and Agrawal (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), SPM aims to uncover frequently occurring subsequences within sequential data, emphasizing both their occurrence frequency and structural length. In this study, we employ SPM using the Tranmine package in R (Zaki, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). After quantifying temporal dependencies between consecutive behaviors through lag-sequential analysis (LSA), the method identifies representative subsequences that surpass a predefined frequency threshold, referred to as the \u0026ldquo;support value.\u0026rdquo; A subsequence qualifies as frequent if its support value meets or exceeds the minimum threshold, commonly referred to as \u0026ldquo;minimum support\u0026rdquo; (Zhang \u0026amp; Paquette, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To ensure the retention of significant patterns, we restrict our analysis to subsequences with a support value of at least 0.6 (Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, single-event patterns are excluded to preserve the temporal ordering of events, with the subsequence lengths limited to a minimum of two and a maximum corresponding to the shortest sequence in the dataset.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Findings \u0026 Discussion","content":"\u003ch2\u003e5.1 Immediate Behavioral Transitions of Verbal Interactions With/Without Group Awareness Support\u003c/h2\u003e\n\u003cp\u003eThe sequential analysis of students\u0026rsquo; interaction behavior within experimental and control conditions is applied, and the results are presented in Tables A1, A2, A3, and A4 (see Appendix). These tables show the Z-score in which learners\u0026rsquo; behavior from one (in each row) to another (in each column). Based on these tables, a behavior transition diagram is drawn for each condition, as shown in \u003cstrong\u003eFig. 2\u003c/strong\u003e, showing those sequences that reach a significant effect. The significant sequence is the sequence with a Z-score of more than 1.96 (Bakeman \u0026amp; Quera, 1995). Each transition in \u003cstrong\u003eFig. 2\u003c/strong\u003e has both significance and Z-score represented on each line. The findings reveal how GA support influences collaborative learning behaviors, shedding light on the awareness support that facilitates coordination and interaction in collaborative learning.\u003c/p\u003e\n\u003cp\u003eThe analysis confirms that essential collaborative behaviors, such as offering information (A1) and reaching agreement (A4), are observed in both conditions. In the control group, the bidirectional interactions between offering help (A1) and agreeing with ideas (A4) are significant. These exchanges suggest that learners frequently moved from proposing ideas to acknowledging, accepting, and further developing them, indicating a pattern of idea uptake and emerging consensus that may support task coordination (Chen et al., 2009; Bereiter \u0026amp; Scardamalia, 2014). Students contributed their own ideas to address the collaborative task and often received immediate uptake from their partners. For example, in one exchange, a learner proposed a concrete action for combining ideas, and the partner immediately confirmed the proposal. In another exchange, a learner introduced a possible product feature, and the partner responded by accepting and elaborating the idea. Together, these examples illustrate how verbal \u0026nbsp;interaction moved beyond isolated idea sharing toward jointly endorsed and progressively refined solutions. Similarly, in the experimental group, this bidirectional behavioral path occurs more frequently, indicating that GA support enhances the efficient exchange of information by offering a clearer understanding of group progress (Strau\u0026szlig; \u0026amp; Rummel, 2020; Wang et al., 2020). Additionally, the behavior path A2\u0026rarr;A3 occurs in both conditions indicating a supportive online collaborative learning environment (Wang et al., 2020). When students requested information or assistance, their group members typically provided relevant responses. Furthermore, the transition from A6 to A3 indicates that the group is engaged in active dialogue and mutual support, with members stepping in to address ambiguities and ensure shared understanding. It shows that participants are not only aware of their own uncertainties but also rely on group members to help clarify those issues, leading to enhanced collective problem-solving and learning (Rojas et al., 2022; Schnaubert \u0026amp; Bodemer, 2022).\u003c/p\u003e\n\u003cp\u003eA key pattern in the control group is the transition from agreeing (A4) to leading (A7). This behavior suggests that once agreement was reached, participants often took charge of task coordination, assigning roles, and guiding actions. In contrast, in the experimental group, the transition from agreement (A4) to monitor and report progress (A8) and then to responding to information (A3) was notable. This path reflects the impact of GA support, as it offers real-time information on the group\u0026rsquo;s activities, leading to more focused interactions. This transition demonstrates GA support can ensure that after agreeing on ideas or task-related information, progress is consistently monitored, and responses are informed by a clear understanding of both the individual and collective contributions. This leads to a smoother, more synchronized collaborative effort.\u003c/p\u003e\n\u003cp\u003eAnother different behavioral transition in the experimental group is the path from offering help (A1) to negotiation (A5), with a strong self-loop within negotiation (A5 to A5). This suggests that GA facilitates a more structured approach to problem-solving, where participants offer solutions but also engage in critical evaluation through negotiation (Lin, 2018; Schnaubert \u0026amp; Bodemer, 2019). In contrast, the control group demonstrates more reliance on the transition from agreeing (A4) to leading (A7), reflecting a more fluid dynamic where leadership emerges spontaneously after agreement.\u003c/p\u003e\n\u003cp\u003eThe experimental group demonstrates that with the support of GA, instructions related to group coordination and adjustments consistently receive prompt responses from team members (A3). This responsiveness highlights the effectiveness of GA in facilitating efficient communication and task management within the group. In contrast, while the control group exhibit a higher frequency of self-loop behavior (A8\u0026nbsp;\u0026rarr;\u0026nbsp;A8), which reflects ongoing task monitoring, this behavior can become counterproductive if it devolves into a repetitive cycle of progress checking without advancing the task. Although constant task tracking is important in some contexts, excessive reliance on it without taking action or addressing new challenges may hinder the group\u0026rsquo;s overall efficiency and progress.\u003c/p\u003e\n\u003ch2\u003e5.2 Extended Behavioral Trajectories of Verbal Interactions With/Without Group Awareness Support\u003c/h2\u003e\n\u003cp\u003eTo further investigate the influence of GA support on the participants\u0026rsquo; verbal interactions in collaborative learning, this study examined the frequency of interaction behavior subsequences using a minimum support threshold of 0.6. The analysis identified 21 representative subsequences in the experimental group (with GA support), exceeding the 18 subsequences observed in the control group (without GA support) (see Table 2). This discrepancy underscores the enhanced richness and complexity of interaction patterns fostered by GA (Li et al., 2021). To better understand the structural composition of these subsequences, we group them into core itemsets based on shared behavioral components. For instance, the sequence {reaching agreement (A4) \u0026rarr; seeking assistance (A2) \u0026rarr; providing ideas or support (A1)} (Panel A. SID 4) encompasses the behavioral trajectories (A4 \u0026rarr; A1), (A4 \u0026rarr; A2), and (A2 \u0026rarr; A1) (Panel A. SID 1-3) as its integral subsequences. The categorization reveals six distinct itemsets in the experimental group, exceeding the four itemsets identified in the control group, thereby offering deeper insights into the structure and organization of collaborative behaviors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eSubsequences With/Without Group Awareness Support\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItemset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubsequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupport Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 578px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePanel A. Experimental Group (With GA)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A2, A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A4, A7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA7\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A7\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A3, A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA3\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A2, A3, A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A4, A6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA6\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A6\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A2, A4, A6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA6\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA6\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A6\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A6\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 578px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePanel B. Control Group (Without GA)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A4, A5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A4\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A2, A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A2, A4, A5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A4\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA5\u0026nbsp;\u0026rarr;\u0026nbsp;A4\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eA1, A2, A3, A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA3 \u0026rarr; A2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA3 \u0026rarr; A1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eA4\u0026nbsp;\u0026rarr;\u0026nbsp;A3\u0026nbsp;\u0026rarr;\u0026nbsp;A2\u0026nbsp;\u0026rarr;\u0026nbsp;A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Minimum support threshold is 0.6.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the 39 representative subsequences identified, the most frequent pattern is the transition from reaching agreement (A4) to offering ideas or help (A1). This finding underscores the foundational role of consensus building in collaborative learning, while aligning with the behavioral path highlighted through the aforementioned lag sequential analysis. Specifically, the transition (A4\u0026rarr;A1) illustrates how establishing consensus facilitates proactive contributions, enabling participants to fully comprehend and build upon each other\u0026rsquo;s contributions, which is critical for collaborative knowledge construction (Gijlers et al., 2009; Sangin et al., 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo provide a broader perspective on the collaborative learning process, this study extends the analysis of transitions into more complex sequences. For example, the sequence {reaching agreement (A4) \u0026rarr; help seeking (A2) \u0026rarr; offering ideas or help (A1)} (Panel A. SID 1-4; Panel B. SID 5-7) represents a collaborative trajectory where participants not only achieve consensus but also actively address uncertainties or knowledge gaps through help-seeking behaviors before contributing solutions. This sequence highlights the iterative nature of problem-solving in collaborative contexts, which integrates cognitive processes, such as information seeking and synthesis, with social dynamics, including shared understanding and ambiguity resolution (Graesser et al., 2018). Another extended sequence, {reaching agreement (A4) \u0026rarr; responding to information (A3) \u0026rarr; help seeking (A2) \u0026rarr; offering ideas or help (A1)} (Panel A. SID 11-14; Panel B. SID 12-18), exemplifies how consensus building (A4) transitions into active engagement with shared information (A3). This engagement enables team members to clarify ambiguities and refine their understanding through help-seeking (A2) before contributing actionable ideas or solutions (A1), thus underscoring the pivotal role of information sharing and knowledge exchange in fostering effective collaboration. By activating prior knowledge and guiding the flow of information, it enhances the depth and quality of the collaborative learning process (Erkens \u0026amp; Bodemer, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, patterns observed in the control group, such as A5\u0026rarr;A4\u0026rarr;A1 (Panel B. SID 1-4) and A5\u0026rarr;A4\u0026rarr;A2\u0026rarr;A1 (Panel B. SID 8-11), reinforce lag sequential findings that highlighted a reliance on negotiation. The sequence reveals a prolonged cycle of negotiation (A5), reaching consensus (A4), followed by slower transitions to actionable behaviors like task execution (A1) or help-seeking (A2). This slower progression underscores how the absence of GA hampers interaction efficiency, limiting processes such as joint negotiation of meaning and co-construction of knowledge (Buder \u0026amp; Bodemer, 2008; Hull \u0026amp; Saxon, 2009).\u003c/p\u003e\n\u003cp\u003eThis study underscores the critical role of GA in shaping collaborative learning dynamics and fostering effective verbal interactions. Findings from both analytical approaches reveal that GA support enhances task coordination, critical evaluation, and structured problem-solving. For example, the experimental group demonstrated superior responsiveness and adaptability through sequences like A4\u0026rarr;A6\u0026rarr;A2\u0026rarr;A1 (Panel A. 18-21), showcasing their ability to identify and address uncertainties efficiently. In contrast, the control group relied on less effective patterns, such as A5\u0026rarr;A4\u0026rarr;A2\u0026rarr;A1 (Panel B. SID 8-11), where prolonged negotiation lacked decisive leadership or structured problem-solving. Additionally, GA facilitated continuous progress monitoring and synchronized collaboration in the experimental group, mitigating unproductive cycles like the repetitive self-loop (A8\u0026rarr;A8) observed in the control group. By enabling better regulation of behavior, meaningful negotiation, and targeted problem-solving, GA not only streamlines the collaborative process but also provides valuable insights for designing and implementing GA supported learning environments in educational contexts.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003e This study contributes to the existing literature on collaborative learning and GA support by exploring the role of GA support in influencing learners\u0026rsquo; verbal interactions. Theoretically, the findings advance our understanding of how GA support can enhance coordination and foster deeper engagement in collaborative learning environments. By exploring both behavioral transitions and sequential patterns of verbal interactions, this study highlights the dynamic processes through which GA support impacts the development of shared understanding and knowledge co-construction. Practically, the study provides valuable insights for educators and instructional designers on how to implement GA support in collaborative learning tools, with the potential to improve student engagement, foster more balanced participation, and facilitate deeper cognitive processing during collaborative tasks.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the analysis focused exclusively on verbal communication data, which, while insightful, may not fully capture the complexity of collaborative learning processes. Second, the experimental design involved a short time frame for collaborative tasks, which may not reflect the full development of group dynamics that typically unfold over extended periods in real-world settings. This limited duration could constrain the understanding of how collaboration evolves and how GA support impacts longer-term learning and behavioral changes.\u003c/p\u003e \u003cp\u003eTo address these limitations, future research could adopt a multimodal approach, integrating online data as well as self-reported data such interview and survey to capture more comprehensive picture of the role of GA support during collaborative learning process. These methods could provide richer insights into how GA tools influence both observable behaviors and internal processes like reflection, metacognition, and motivation. Additionally, longitudinal studies exploring collaboration over extended durations could offer more robust findings, shedding light on the sustained effects of GA support on both the social and cognitive dimensions of collaborative learning. Such approaches would contribute to a more comprehensive understanding of how collaborative learning processes is shaped by GA interventions.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003e This study explores the role of GA in influencing students\u0026rsquo; verbal interaction patterns in collaborative learning environments. Through an experimental design of collaborative learning with and without GA support, we explored how GA influences the transition and sequential patterns of verbal interactions during collaborative learning tasks. The findings reveal two complementary effects: First, GA streamlined task-coordination moves, allowing learners to align actions and decisions more efficiently. Second, it diversified the sequential structure of knowledge-building discourse, leading to more diverse interaction patterns.\u003c/p\u003e \u003cp\u003eThese results extend earlier evidence that GA support promotes mutual monitoring and shared regulation (Bodemer \u0026amp; Dehler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Janssen \u0026amp; Bodemer, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which facilitates more effective decision-making and alignment of collaborative efforts (J\u0026auml;rvel\u0026auml; et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Learners with GA support engaged in more targeted, task-oriented exchanges and produced richer discourse networks, consistent with findings on improved precision in monitoring (Ghadirian et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and structured interaction (Phielix et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schnaubert \u0026amp; Bodemer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This improved monitoring enhances the focus and quality of collaborative exchanges interaction, supporting both functionality and the generation of creative ideas.\u003c/p\u003e \u003cp\u003eDespite the benefits of GA support in enhancing coordination, the findings also point to a potential trade-off. While GA can reduce the need for prolonged negotiation, it sometimes reduced opportunities for engagement through critical debate, a socio-cognitive process essential to deep understanding (Gillies, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, GA support should be designed to balance the efficiency with features that preserve for reflection and conflict.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: [Wenli C., Lishan Z., Xuanyu C.]Methodology: [Lishan Z., Xuanyu C.]Investigation: [Wenli C., Lishan Z., Xuanyu C.; Hua H.; M.-Y.M.H.]Data curation: [Wenli C ., Lishan Z., Xuanyu C.; Hua H.; M.-Y.M.H.]Formal analysis: [Lishan Z., Xuanyu C.]Writing \u0026ndash; original draft: [Wenli C., Lishan Z., Xuanyu C.]Writing \u0026ndash; review \u0026amp; editing: [Wenli C., Lishan Z., Xuanyu C.]\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was funded by the National Institute of Education Research Support for Senior Academic Administrator (RS-SAA) Grant (Grant number: RS 1/22 CWL) and administered by the National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NIE and NTU.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability: The data that support the findings of this study are not publicly available due to participant privacy but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbedin, B., Daneshgar, F., \u0026amp; D\u0026rsquo;Ambra, J. (2011). Enhancing non-task sociability of asynchronous CSCL environments. \u003cem\u003eComputers \u0026amp; Education\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(4), 2535\u0026ndash;2547.\u003c/li\u003e\n\u003cli\u003eArvaja, M., \u0026amp; H\u0026auml;m\u0026auml;l\u0026auml;inen, R. (2021). 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A systematic meta-analysis of the impacts of group awareness tools on learning achievements, learning behaviors, and learning perceptions from 2010\u0026ndash;2023. \u003cem\u003eInteractive Learning Environments\u003c/em\u003e, 1-18.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Group Awareness, Verbal Interaction, Collaborative Learning, Lag Sequential Analysis, Sequential Pattern Mining, CSCL","lastPublishedDoi":"10.21203/rs.3.rs-9319024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9319024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroup awareness (GA) plays an important role in collaborative learning by helping learners stay informed about their peers\u0026rsquo; actions, progress, and contributions, thereby facilitating coordination during collaborative learning. Effective collaborative learning depends not only on participation, but also on how learners coordinate actions, monitor progress, and build on one another\u0026rsquo;s ideas over time. However, while prior research has demonstrated that GA support benefits coordination, its influence on the temporal structure of verbal interactions remains underexplored. To address this gap, this study explores the influence of GA support on task coordination in collaborative learning by examining students\u0026rsquo; verbal interaction patterns, which provide a valuable process-oriented lens on these coordination dynamics, as it captures how learners coordinate, respond, clarify, and build on one another\u0026rsquo;s ideas as collaborative learning unfolds. 44 university students worked in dyads across conditions with and without GA support during a design-based collaborative design task. Verbal data were coded and analyzed using lag sequential analysis (LSA) and sequential pattern mining (SPM) to identify significant behavioral transitions and recurring interaction sequences. Results reveal that GA support not only streamlined task coordination by fostering efficient alignment of actions and decisions but also diversified the sequential structure of discourse, promoting richer dialogues towards more effective task coordination during collaborative learning. These findings advance our understanding of how GA support shapes verbal interaction behavior in collaborative learning and provide practical insights for designing Computer-Supported Collaborative Learning (CSCL) environments that balance efficiency with opportunities for critical dialogue and reflection.\u003c/p\u003e","manuscriptTitle":"Exploring Students’ Verbal Interaction Patterns in Collaborative Learning: The Role of Group Awareness in Improving Task Coordination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 19:30:09","doi":"10.21203/rs.3.rs-9319024/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f2d0a54-f9c8-4983-829d-38dc9fe510b6","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T19:30:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 19:30:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9319024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9319024","identity":"rs-9319024","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}