59 Studies in Second Language Learning and Teaching Department of English Studies, Faculty of Pedagogy and Fine Arts, Adam Mickiewicz University, Kalisz SSLLT 12 (1). 2022. 59-86 http://dx.doi.org/10.14746/ssllt.2022.12.1.4 http://pressto.amu.edu.pl/index.php/ssllt Dynamic engagement in second language computer-mediated collaborative writing tasks: Does communication mode matter? Scott Aubrey The Chinese University of Hong Kong, China https://orcid.org/0000-0003-4365-0516 scaubrey@cuhk.edu.hk Abstract This study takes a dynamic approach to investigating engagement, examining fluctuations in cognitive-affective variables at regular time intervals during online collaborative second language (L2) writing tasks. Using online confer- ence software and online editing software, 16 university students who use English as an L2, completed two collaborative problem-solution L2 writing tasks in two communication modes: video-chat and text-chat. After each task, learners viewed videos of their performances in 12 three-minute segments and were asked to rate their engagement on two scales (interest, focus). They were then interviewed about their attributions for fluctuations in their rat- ings. Group-level analysis revealed that learners experienced significantly higher focus and interest during tasks performed in video-chat mode than text-chat mode. This was contrasted with an analysis from a dynamic perspec- tive, which produced a more nuanced picture of individual engagement tra- jectories during the tasks. Dynamic patterns of engagement fell into either moderately steady, increasing, decreasing, or rollercoaster pattern categories. A content analysis of 32 interviews revealed four factors that accounted for changes in engagement during tasks: task design (e.g., task familiarity), task process (e.g., instances of collaboration), task condition (e.g., communication mode), and learner factors (e.g., perceptions of proficiency). Keywords: dynamic engagement; computer-mediated tasks; collaborative writ- ing; affective engagement; cognitive engagement Scott Aubrey 60 1. Introduction Collaborative writing (CW) tasks engage two or more writers with the common goal of producing a single written text (Storch, 2019). Computer-mediated CW tasks utilize online platforms (e.g., Google Doc, Wikis) that host a range of col- laborative tools, potentially enhancing interaction, composition reflection, and learning in ways that are time/space independent (Li, 2018). In an effort to un- derstand how to optimize learner involvement, researchers have been encour- aged to explore the implementation of more diverse communication modes during computer-mediated CW tasks (Lee, 2010; Yim & Warschauer, 2017). So far, a handful of studies have compared the impact of oral (e.g., audio-chat) and written modes (e.g., text-chat) on the interaction/writing process (Cho, 2017; Kessler et al., 2020; Liao, 2018). However, with mixed research findings, the rel- ative benefit of these modes, in terms of learners’ interaction/observable task behavior, is unclear. An alternative approach to investigating this issue might be to examine learners’ cognitive-affective engagement. Despite a surge in re- search into how task implementation and design can be manipulated to pro- mote greater mental and emotional involvement (e.g., Aubrey, 2017a, 2017b; Aubrey et al., 2020; Lambert et al., 2017; Phung et al., 2020; Qiu & Lo, 2017), little is known about learners’ cognitive-affective responses to CW tasks, let alone how this aspect of engagement evolves over time. To fill this research gap, the present study explored differences in learners’ cognitive-affective engagement at regular intervals during computer-mediated CW tasks performed in video-chat (synchronous oral interaction via a live audio/video feed) and text-chat (synchronous written interaction via a chat function) mode. Group-level analysis compared overall engagement between the conditions, which was then contrasted with an analysis from a dynamic, individual perspective. Finally, the factors that accounted for learners’ engagement dynamics were also investi- gated. This study responds to calls for research to foreground the ways in which engagement is dynamic and emergent at different timescales (Hiver et al., 2021). Furthermore, the application of this approach to computer-mediated CW tasks is both novel and important as it may provide insights into how learners can be sup- ported by technology at different stages during these tasks. 2. Literature review 2.1. Mode of communication in computer-mediated CW tasks Computer-mediated CW studies have predominantly employed online plat- forms in which learners interact with each other to plan within the task, draft, Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 61 and revise the text cooperatively, using both asynchronous (e.g., the “comment” function) and synchronous written communication (e.g., the “chat” function) (for reviews of these studies, see Aubrey & Shintani, 2021; Yim & Warschauer, 2017). The almost exclusive focus on the written mode during these interactions has led to calls for investigations into more diverse channels of collaboration, specifically those involving “verbal interaction” (Yim & Warschauer, 2017, p. 158). Relevant to the current study are two kinds of synchronous computer-me- diated communication modes: text-chat and video-chat. Several insights can first be gleaned from studies that have compared modes of communication during computer-mediated interactive tasks. Early research in this area suggested that text-chat (i.e., synchronous written communication) af- fords several benefits over face-to-face (FTF) communication, which include a less stressful environment (Chun, 1998), reduced anxiety, and greater quality of lan- guage production (Warschauer, 1997). However, recent research has highlighted some of the more limiting aspects of text-chat, indicating that it can be time-con- suming and impersonal (Zeigler, 2016), and can make it difficult for learners to pay attention to language (Loewen & Wolff, 2016) because of the non-contingent na- ture of turn-taking (i.e., occurring in a delayed fashion) (Lai et al., 2008). Some studies have also addressed how text-chat affects the CW process in comparison to FTF and audio communication. Cho (2017) compared interac- tions within a three-learner group while they collaboratively wrote a summary using Google Docs with text-chat followed by a summary using Google Docs with voice-chat (i.e., synchronous audio-only communication). She found that for the upper-intermediate/advanced English learner participants, the voice-chat con- dition facilitated collaboration better than text-chat because of its “interactive and instant nature” (p. 49). However, she also speculated that text-chat might benefit lower proficiency learners because of the slower pace of interaction and the visual aspect of text-chat output. More recently, pre-task planning studies, in which learners collaboratively plan for their compositions, have examined the relative effect of FTF and text-chat mode on learners’ collaborative behavior. Liao (2018) examined the interactions of six university L2 Chinese learners dur- ing pre-writing sessions. She found that learners generated more words, turns, ideas, and lexical discussions in the FTF mode, while text-chat mode resulted in more equal participation. Additionally, because text-chat and composition writ- ing both consist of textual output, learners could more easily retrieve planned ideas and language to use in their compositions. In this way, the visual access in text-chat facilitated information processing and retention, potentially easing at- tentional demands as learners wrote their compositions. In a conceptual repli- cation of Liao’s study, Kessler et al. (2020) explored the collaborative pre-writing discussions of 10 L2 Chinese learners. Similar to Liao, the study involved L2 Chinese Scott Aubrey 62 learners who performed either FTF or text-chat discussions in dyads before com- pleting an individual, timed L2 writing task. The study addressed the methodo- logical flaws in Liao’s study, namely, the lack of clear coding examples and relia- bility measures. Kessler et al. (2020) also found that FTF planning resulted in more language production, while text-chat resulted in more equal interaction despite some learners finding it to be “much slower, awkward, and arduous” (p. 15). In sum, these studies indicate that oral and text-chat modes seem to pre- pare learners for writing in different ways, but such studies have been scarce, and findings have been mixed. Unlike text-chat, video-chat (synchronous audio/video communication) involves actual multimodal communication as it represents both a non-verbal (visual) and verbal (audio) sensory input. This additional channel of communi- cation could be argued to contribute to “social presence,” or a psychological feeling of nearness (Yamada & Akahori, 2009). Social presence has three com- ponents: immediacy (i.e., the feeling of being physically close), intimacy (i.e., the feeling of being understood), and sociability (i.e., the feeling of connection) (Shearer & Park, 2019). These dimensions may be facilitated to different degrees via eye contact, smiling, laughing, or head nodding, which may serve to reduce the psychological distance between interlocutors (Chamberlin Quinlisk, 2008) and establish deeper emotional connections (Develotte et al., 2010). However, according to the cognitive-affective theory of learning with media (Moreno, 2005), multimodal channels, such as video-chat, carry a higher risk of creating excessive extraneous cognitive load for the learner, which can disrupt pro- cessing and induce anxiety. When multiple sources of input are received sepa- rately (i.e., spatially or temporally) in multimodal tasks, learners can waste at- tention integrating information from each stimulus, leading to what is called the split-attention effect (Mayer & Moreno, 1998). In L2 learning, the split-attention effect has been found to occur during audio-visual tasks where audio commen- tary and visual scenes are not directly related (Guichon & McLornan, 2008), and in L2 reading tasks, where connected information is presented in different loca- tions of a text (Al-Shehri & Gitsaki, 2010). Drawing on these ideas, we might expect video-chat and text-chat to strain learners’ attention in different ways. Video-chat requires learners to simultaneously integrate information received from multiple stimuli, which may place an especially high burden on cognitive resources; on the other hand, the split-attention effect may occur during text- chat if written messages overlap or are delayed (i.e., non-contingent turn-tak- ing), which would require learners to expend extra attention to consolidate mes- sages into meaningful discourse. Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 63 2.2. Language learner engagement Rooted in educational psychology, learner engagement refers to heightened at- tention, active participation, and meaningful involvement in a learning task (Mer- cer & Dörnyei, 2020). Furthermore, engagement is seen as a prerequisite for learning and particularly sensitive to classroom interventions (Zhou et al., 2021). Although the exact nature of engagement is debated, there seems to be a con- sensus that it is a multifaceted construct comprising four components: behav- ioral engagement (e.g., time on task), affective engagement (e.g., enjoyment), cognitive engagement (e.g., mental effort) and social engagement (e.g., inter- action) (Philp & Duchesne, 2016). Studies on computer-mediated CW tasks have provided some insight into the observable aspects of behavioral and social engagement. Research has shown that during computer-mediated CW, learners engage in collaborative be- havior (Elola & Oskoz, 2010; Lee, 2010), using their collective knowledge to re- solve language, content, and organizational issues through interaction (Hsu, 2019; Li, 2013). This common line of inquiry reflects Svalberg’s (2009) notion that engaged learners are those “who are actively constructing their knowledge not only by mental processes but also equally by being socially active and taking initiative” (p. 246). However, learners’ mental effort and emotional responses to writing tasks (i.e., the cognitive-affective dimension) are important invisible engagement factors (Hiver et al., 2021) that are often overlooked (Mystkowska- Wiertelak, 2020), particularly in the context of learners performing CW tasks. There is no consensus on what constitutes reliable indicators of affective engagement, though research has commonly used enthusiasm, interest, and en- joyment as positive markers (e.g., Phung et al., 2020; Skinner et al., 2009). Of these, interest has been referred to as an outcome of engagement characterized by positive emotions related to an activity (Fredricks et al., 2004; Krapp, 2003). Chen’s (2001) theory of interestingness suggests that momentary feelings of in- terest in a task derive from novelty, challenge, attention demand, exploration intention, and instant enjoyment. Cognitive engagement, on the other hand, is associated with sustained attention and mental effort (Fredricks et al., 2004), and is thus strongly associated with the notion of focus. Having intense focus or concentration during an activity is a characteristic of flow (Csikszentmihalyi, 1990), a state that has been described as the “ultimate in engagement” (Philp & Duchesne, 2016). Though focus can relate to consciously attending to – or noticing – important linguistic features or patterns (Schmidt, 1990), focus, as an indi- cator of task engagement, relates more to fluency and automaticity (Egbert, 2003). Interest and focus also have a close relationship. Hidi (1990) argues that interest elicits spontaneous, automatic allocation of attention. Similarly, Csikszentmihalyi et Scott Aubrey 64 al. (2005) describes interest as being a by-product of focused involvement in an activity. According to Aubrey’s (2017b) model of flow in the task-based language classroom, key characteristics of heightened engagement in a task are interest and focus, which are mediated by certain pre-conditions, such as an appropriate balance of proficiency and task difficulty level, personal relevance of the task topic, and an environment with few distractions. In the present research, inter- est and focus are measured as indicators of cognitive-affective engagement. An important characteristic of engagement is that it is dynamic (Reschly & Christenson, 2012). Hiver et. al. (2021) argue that in future studies on engage- ment, “there will be clear value in . . . measures that allow the dynamics of en- gagement (e.g., how it is sustained and how it deteriorates) to be investigated” (p. 21). Engagement dynamics can be investigated at larger timescales (e.g., be- tween tasks) or smaller timescales (e.g., during tasks). Aubrey et al.’s (2020) classroom-based study provides a rare example of the former. They required 37 Japanese EFL learners to provide engagement ratings (focus, desire to speak, anxiety, confidence), as well as written descriptions to account for their ratings, after their participation in weekly oral tasks over a 10-week period. Findings re- vealed that engagement was highly variable throughout the period, with changes shaped by learner-level factors (e.g., cognitive/physical state), lesson- level factors (e.g., understanding of the lesson before the task), task-level factors (e.g., task design), and post-task-level factors (e.g., satisfaction of performance). Research on the dynamics of engagement at the within-task level has adopted a dynamic systems perspective in which data are collected at regular intervals over time and analyses are carried out on individual learners (Larsen- Freeman & Cameron, 2008). These studies have primarily focused on the affec- tive dimension of engagement. For example, using the idiodynamic method (MacIntyre, 2012) to measure moment-by-moment fluctuations in emotions, Boudreau et al. (2018) had learners perform a speaking task before asking them to view their performances and rate their enjoyment and anxiety (i.e., markers of affective engagement) on a per-second timescale. They found that enjoyment and anxiety were highly dynamic, with patterns of correlation ranging from neg- ative to positive, suggesting that elevated enjoyment occurred during varying levels of anxiety. In a recent classroom-based study, Dao and Sato (2021) meas- ured dynamic affective engagement, operationalized as enjoyment and interest, via a questionnaire at three five-minute intervals during a 15-minute speaking task. They found these measures to increase significantly from the first to second interval, suggesting that, despite using less nuanced measurement methods, af- fective engagement is still susceptible to considerable change within a task. Em- ploying similar dynamic approaches, other studies have measured learner emo- tions during speaking tasks together with constructs such as task motivation and Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 65 willingness to communicate (e.g., Guo et al., 2020; MacIntyre & Gregersen, 2021; MacIntyre & Serroul, 2015; Pawlak et al., 2016), each providing support for the dynamism of engagement during short speaking tasks. However, this approach has not yet been applied to CW tasks. The current study thus builds on previous research to examine the trajectories of learners’ engagement (operationalized as focus and interest) during computer-mediated paired CW tasks in different modes. Specifically, the following research questions will be addressed: 1. What are the differences in engagement during computer-mediated CW tasks when learners communicate synchronously in text-chat and video- chat mode? 2. What factors do learners perceive to influence engagement during com- puter-mediated CW tasks when learners communicate synchronously in text-chat and video-chat mode? 3. What are common patterns of fluctuation in engagement during com- puter-mediated CW tasks when learners communicate synchronously in text-chat and video-chat mode? 3. Method 3.1. Participants Participants included 16 learners of English (12 female, 4 male) who were at- tending a university in Hong Kong. Due to the COVID-19 pandemic, the univer- sity had conducted all courses online in the previous semester. Thus, all partici- pants had some recent experiences using online synchronous communication tools. Based on information from a background questionnaire, all participants spoke Cantonese as their first language (L1) and had scored “4” on the English language subject level of their Hong Kong Diploma of Secondary School Exam (HKDSE), which is benchmarked to the IELTS score range of 6.31-6.51 (Hong Kong examinations and assessment authority [HKEAA], 2015) and equivalent to the Common European Framework of Reference (CEFR) B2/C1 level. Participants were born and raised in Hong Kong and reported having no experience living in an overseas English-speaking country. Although there are opportunities to use English on campus, all participants used Cantonese almost exclusively on a daily basis and English sometimes in the classroom. Eight participants were initially recruited by the researcher. To ensure a high degree of familiarity between in- terlocutors, the recruited participants were asked to find a task partner who they knew personally and who met the HKDSE “4” requirement. Informed con- sent was obtained from all participants before data collection began. Participants Scott Aubrey 66 were aged between 19 and 22 and had a variety of university majors. A summary of participant information is provided in Table 1. Table 1 Summary of participant information Pair Participant Gender Major Age Pair 1 P1 Male Chinese language 19 P2 Female Chinese language 19 Pair 2 P3 Male Mathematics 20 P4 Female Mathematics 20 Pair 3 P5 Male Philosophy 22 P6 Female Philosophy 22 Pair 4 P7 Male Anthropology 19 P8 Female Anthropology 20 Pair 5 P9 Female Chinese language 19 P10 Female Chinese language 19 Pair 6 P11 Female Mathematics 20 P12 Female Mathematics 19 Pair 7 P13 Female Psychology 21 P14 Female Nutritional sciences 21 Pair 8 P15 Female Nutritional sciences 19 P16 Female Nutritional sciences 19 3.2. Tasks Participants completed two computer-mediated, problem-solution CW tasks in pairs. Each task had a time limit of 36 minutes, which was decided based on pilot- ing the two tasks before the study. Task 1 presented the participants with a prob- lem related to secondary school education (see Appendix A), while Task 2 pre- sented participants with a problem related to university education (see Appendix B). The instructions required learners to read the problem prompt, discuss possi- ble solutions, agree on the most effective solution, and then jointly write a para- graph that summarizes the problem, their solution, and reasons. The problem- solution task was chosen because previous research suggests it is of high concep- tual difficulty, which may compel learners to interact (Németh & Kormos, 2001). 3.3. Procedures Each pair completed Task 1 followed by Task 2 in separate sessions, with a one- week break between sessions. To control for the effect of task topic, communi- cation mode (video-chat, text-chat) was counterbalanced between the tasks by randomly dividing pairs into two groups (see Figure 1). Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 67 n = 8 n = 8 Session 1 Task 1 Task 1 Video-chat mode Text-chat mode ↓ ↓ Session 2 Task 2 Task 2 Text-chat mode Video-chat mode Figure 1 Counterbalancing the tasks The tasks were performed online using Zoom (online video/audio confer- encing software) and Google Docs (online editing software). For each task, the researcher and each participant in the pair logged into the same Zoom meeting and shared the same Google Document from separate locations, which ensured any interaction was done online. Before the first task, the researcher conducted a training activity which involved demonstrating the simultaneous editing fea- tures of Google Docs as well as use of the chat function. The researcher then confirmed that all participants had some previous experience with the online tools. Next, participants wrote a short introduction of themselves to demon- strate their understanding of the software. Immediately prior to each task, the researcher stated the instructions, answered any questions, and explained the notion of CW to encourage interaction throughout the task. In video-chat mode, participants enabled their Zoom video and audio so that they could see and speak to each other while completing their joint composition (see Figure 2). Figure 2 Screenshot of Task 1 writing (video-chat mode) In the text-chat condition, participants disabled their video and audio so they would only interact using the text-chat function in Google Docs (see Figure Scott Aubrey 68 3). After 36 minutes, the researcher signaled the end of the task. All task perfor- mances were audio and video (screen) recorded. Figure 3 Screenshot of Task 2 writing (text-chat mode) Within 24 hours of completing of each task, learners participated in a rat- ing and stimulated-recall interview session. Each rating/interview session was done individually and took approximately 60 minutes. The procedure involved learners viewing a video of their performance in 12 three-minute segments. Af- ter each segment, the video was paused, and learners were asked to rate their focus and interest on a scale from -5 (very low) to +5 (very high). The rating procedure included a brief explanation of each variable, examples, and asking the questions: How focused were you during these three minutes? How inter- ested were you in doing the task during this time? After the rating procedure, a line graph showing changes of each self-rated dimension was created in Mi- crosoft Excel and shown to the participant. The researcher and the participant then looked at the graph together and discussed the trends for each variable across the task period. Examples of questions that were asked by the researcher include: Why did you rate focus/interest low at this stage of the task? Can you explain why your ratings remained stable but then increased over this time in- terval? Why did your ratings for focus/interest in the final minutes of the task suddenly decrease? The rating/interview procedure was an adapted version of the idiodynamic method, which was originally created to understand short-term fluctuations in cognitive-affective responses to oral tasks (MacIntyre, 2012). While previous studies using this method had learners rate each variable on a per-second timescale with the aid of computer software, the current study adopted a three-minute timescale due to the longer duration of writing tasks. 3.4. Data analysis The data for the study consisted of 32 engagement ratings (focus, interest) and 32 transcribed interviews. Rating data were entered into SPSS version 26. Averaged Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 69 ratings for focus and interest across the 12 intervals were calculated and checked for normality. As data were not normally distributed, Wilcoxon signed rank tests were performed on mean focus and interest scores to determine if there were significant differences in engagement between the video-chat and text-chat conditions. Effect sizes (r) for differences in focus and interest were estimated by dividing the z value by the square root of the sample size. A content analysis of interview transcripts was then conducted to determine the reasons for trends in engagement during the tasks. This involved an initial review of the data, cod- ing of data and categorization of codes into themes (Cohen et al., 2007). In total, 357 separate reasons were identified and coded for positive and negative influ- ences on engagement. Due to the intertwined nature of interest and focus (Hidi, 1990), participants were often unable to distinguish between their reasons for the two measures (e.g., P9: “interest and focus are different but kind of the same . . . I can talk about them together”). Thus, comments related to interest and focus were aggregated into one engagement category. The researcher and a research assistant used a coding scheme to independently code 20% of the data and obtained a simple intercoder agreement of 91%. Coding that resulted in disagreement was subject to further discussion until full agreement was reached. As seen in Table 2, the analysis resulted in four categories: learner fac- tors, task design factors, task process factors, and task condition factors. Table 2 Categories of comments on engagement at points of change Categories Examples Learner factors Perceptions of proficiency my English is not very good, so I have some . . . um- something struggle to focus Attitudes towards English I like English, so I started with high interest Cognitive/affective state I- I’ve just wake up (laugh) and a little bit sleepy Task design factors Task familiarity but the- the format is uh- like to the secondary school practice. I mean the exercise this time. Topic interest the topic is about teenagers, which is close to me- the topic, so I’m have a high interest in discussing this topic Topic familiarity because I’m not familiar with the topic Task process factors Task understanding So I’m uh- I’m figuring out what we are doing at this moment Idea conceptualization I’m running out of ideas, so my focus dropped. Collaboration We have some interaction with each other, so it makes me more interested Focus on accuracy I spent a lot of time to think about grammar Scott Aubrey 70 Focus on fluency I started to only write simple words, so my speed is faster Transition between stages I change to start to the proofread end uh- part, so I lose my focus in this part. Little relaxing here Task condition factors Environment and do not have anything to disturb me in the room and I just focus Communication mode [my partner] also typed the same ideas on the chat box at the same time. It means we have mixed up some ideas and then waste time Time constraints I’m in hurry at that time because it’s almost finished Finally, to identify the patterns of fluctuations of focus and interest throughout the tasks, trajectories of each variable were plotted in line graphs showing changes across the 12 three-minute segments of the task. Following MacIntyre and Serroul (2015), “dips” and “spikes” were identified in learners’ ratings for each task performance. A “dip” was defined as a decline of three or more rating points during the task and a “spike” by an increase in three or more points during the task. Participants’ engagement patterns fell into four catego- ries: a moderately steady pattern (no dips or spikes), an increasing pattern (only spikes present), a decreasing pattern (only dips present), or a rollercoaster pat- tern (both dips and spikes present). Though not the focus of this research, it was considered worthwhile to establish the relationship between focus and interest before presenting the re- sults. To do this, Pearson correlations were calculated between interest and fo- cus scores for the 12 three-minute intervals. With the alpha set at .05, results indicated significant correlations for 10 out of 12 intervals in video-chat (.51 < r < .79) and 9 out of 12 intervals in text-chat (.51 < r < .79). Non-significant rela- tionships occurred during the 24-36-minute period for both video-chat (.33 < r < .47) and text-chat (.31 < r < .43), suggesting a close relationship between focus and interest (Csikszentmihalyi et al., 2005; Hidi, 1990), which weakened slightly towards the end of the task period. 4. Results Table 3 provides a summary of the descriptive statistics for focus and interest ratings averaged across the 16 participants. This is represented visually in Figure 4. Wilcoxon signed rank tests revealed that there were significantly higher focus scores reported in the video-chat condition, with a large effect size (Z = 2.87, Nvideo-chat = 16, Ntext-chat = 16, p < .01; r = 0.72), and significantly higher interest scores in the video-chat condition, with a large effect size (Z = 2.30, Nvideo-chat = 16, Ntext-chat = 16, p = .02; r = 0.58). The only time interval both focus and interest were not rated higher for the video-chat condition was for the last three minutes Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 71 of video-chat tasks where learners in the text-chat condition seemed to experi- ence an increase in both measures. Table 3 Descriptive statistics for engagement fluctuations during the 36-minute writing tasks Focus (N = 16) Interest (N = 16) Minute Video-chat M (SD) Text-chat M (SD) Video-chat M (SD) Text -chat M (SD) 0-3 3.19 (1.38) 1.94 (1.98) 2.63 (1.50) 2.00 (1.55) 3-6 3.38 (1.09) 2.19 (1.64) 2.81 (1.33) 1.43 (1.55) 6-9 3.31 (0.87) 2.25 (1.18) 2.69 (1.40) 1.56 (1.41) 9-12 3.31 (0.87) 2.44 (1.32) 2.31 (1.49) 1.68 (1.13) 12-15 3.44 (0.63) 2.44 (1.21) 2.44 (1.32) 1.93 (1.18) 15-18 3.06 (1.00) 2.25 (1.69) 2.19 (1.64) 1.81 (1.11) 18-21 3.13 (1.09) 2.31 (1.85) 2.00 (1.86) 1.75 (1.18) 21-24 3.25 (1.00) 2.44 (1.71) 1.94 (1.48) 1.69 (1.08) 24-27 3.13 (1.09) 2.19 (1.33) 1.75 (1.39) 1.56 (1.21) 27-30 3.25 (0.93) 1.94 (1.44) 1.88 (1.45) 1.44 (1.21) 30-33 2.88 (1.75) 2.38 (1.26) 1.69 (1.35) 1.50 (1.15) 33-36 2.31 (2.15) 2.63 (1.54) 1.63 (1.31) 1.88 (0.96) Total mean 3.14 (1.22) 2.28 (1.53) 2.16 (1.47) 1.69 (1.24) Figure 4 Focus and interest ratings during video-chat and text-chat modes 1 1,5 2 2,5 3 3,5 0-3 3-6 6-9 9-12 12-15 15-18 18-21 21-24 24-27 27-30 30-33 33-36 Ra tin g Time (minutes) Focus (video-chat) Focus (text-chat) Interest (video-chat) Interest (text-chat) Scott Aubrey 72 Table 4 Factors influencing learners’ engagement during video-chat and text- chat tasks Video-chat mode Text-chat mode Positive Negative Positive Negative Learner factors Perceptions of proficiency 0 9 0 3 Attitudes towards English 1 0 0 2 Cognitive/affective state 2 6 3 6 3.0% 3 18.8% 15 3.0% 3 14.1% 11 Task design factors Task familiarity 4 1 2 0 Topic interest 8 5 4 6 Topic familiarity 6 6 9 3 18.2% 18 15.0% 12 15.0% 15 11.5% 9 Task process factors Task understanding 4 3 6 4 Idea conceptualization 12 8 7 3 Collaboration 18 8 8 7 Focus on accuracy 0 7 3 4 Focus on fluency 4 1 3 0 Transition between stages 9 16 12 8 47.5% 47 53.8% 43 39.0% 39 33.3% 26 Task condition factors Environment 2 1 1 2 Communication mode 27 6 30 24 Time constraints 2 5 12 6 31.3% 31 12.5% 10 43.0% 43 41.0% 32 Total 100% 99 100% 80 100% 100 100% 78 Table 4 shows the results of the content analysis on stimulated recall in- terviews, which indicate that engagement was influenced by learner factors, task design factors, task process factors, and task condition factors. For engage- ment during text-chat tasks, task condition factors and task process factors were the largest contributors. Reasons for increasing or decreasing engagement re- lated to communication mode were most frequent, with positive comments re- ferring to a reduction in communication anxiety (e.g., P3: “chatting in the chat box is less stressful”), use of familiar abbreviations to communicate (e.g., P1: “We use texting… always use short versions of English, so it is quite easy for me”), and the permanent record of text-based interaction (e.g., P8: “I lost memory, so I double check by looking back at the chat box”). Overall, however, the text-chat communication mode had a more negative impact on engagement than the video-chat communication mode. Reasons for decreases in engagement Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 73 related to the perceived inefficiency of text-chat interaction (e.g., P10: “Text is okay, but sometimes it- we will mix up with the ideas. Uh- that means waste more time”), with several references to not noticing messages or feeling confu- sion due to overlapping, non-contingent messages (e.g., P6: “I am not finished point one, but he moves to point two. And then we miss two points together”). Similar to text-chat, task condition factors and task process factors were the larg- est contributors to video-chat engagement. However, the video-chat communi- cation mode seemed to facilitate greater collaboration and idea generation than text-chat (e.g., P2: “Why my interest at the beginning is so high because I can discuss with [my partner] a lot so it is fun”), with several positive comments related to the immediacy of communication (e.g., P7: “it’s quicker for us to talk on uh- in front of the camera, like it’s talking real life”) and the intimacy of com- munication due to the presence of visual cues (e.g., P11: “I can guess her emo- tion or her uh- through her face”). A notable factor that suppressed engagement in video-chat was the transition between task stages, which, for some partici- pants, occurred when learners reduced their speaking production to focus on writing (e.g., P13: “Changing to writing now… it is difficult”). Learner factors and task design factors were less influential. A lack of perceived English proficiency (e.g., P15: “It’s difficult because I’m not a good speaker”) accounted for some decline in video-chat engagement, while task interest and familiarity accounted for high initial engagement in the task (e.g., P10: “I start high interest because I know the topic”) for both modes. Examining the dips and spikes in interest and focus throughout the task, individual participants displaying each of the four engagement patterns are shown in Table 5. Figure 5 visually shows examples of each pattern type. Table 5 Distribution of patterns of engagement across participants and commu- nication condition Pattern Number of patterns Participants (focus) Participants (interest) Video-chat Text-chat Video-chat Text-chat Moderately steady 35 (54.7%) P16, P12, P11, P10, P6, P4, P14, P13, P8, P7, P1 P16, P10, P9, P5, P6, P1 P16, P12, P11, P10, P8, P6, P5, P13, P7, P3 P16, P12, P9, P8, P5, P8, P4, P1 Decreasing 15 (23.4%) P15, P9, P3, P2 P15, P14, P12 P15, P14, P9, P4 P15, P14, P11, P10 Increasing 7 (10.9%) P5 P13, P8, P7, P4, P2 P13 Rollercoaster 7 (10.9%) P11, P3 P1, P2 P6, P3, P2 Scott Aubrey 74 Figure 5 Examples of the four patterns of engagement Most engagement trajectories fell into the moderately steady pattern cat- egory. However, this pattern was seen more in video-chat performances (69% of focus patterns; 63% of interest patterns) than in text-chat tasks (38% of focus patterns; 50% of interest patterns). For example, P16 increased her focus slightly during the 0-9-minute stage of the video-chat task from 3 to 4, which she at- tributed to an initial period of understanding the task and generating ideas, then maintained a high focus score of 4 for the rest of task (“once you understand and Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 75 generate ideas, you became more focused and-and concentrated. It lasted me”) (see Figure 5). Within this pattern, high steady engagement was most common, which often increased (or started high) during the initial stages of the tasks. The second most populated category was a decreasing pattern of engage- ment, with approximately equal proportion of this pattern found in video-chat task performances (25% of focus patterns; 25% of interest patterns) and text- chat performances (19% of focus patterns; 25% of interest patterns). Trajecto- ries were either a slow or sudden decline. P15 is an example of the latter (see Figure 5). P15 starts the video-chat task with a focus score of 4, which decreases to 2 at the 30-minute mark, before dropping to -3 in the last 6 minutes of the task. In her case, the initial decline was related to a perceived lack of proficiency (“I can’t speak well . . . it’s always a problem for me”), followed by a steep decline related to finishing the task early (“I think I finished, and I drop uh- lost focus and interest very significantly”). A high initial interest in the task, triggering fo- cused attention, and a lack of perceived proficiency, causing decreases in en- gagement, were common reasons for this pattern category. Thirdly, increasing patterns of engagement were mostly confined to text- chat experiences (31% of focus patterns; 6% of interest patterns), with only one pattern found in video-chat task performances (6% of focus patterns; 0% of in- terest patterns). Most increasing patterns were for focus, such as for P7, who maintained a score of between 1 and -1 during the 0-27-minute portion of the task, but then increased to 3 in the final 9 minutes (see Figure 5). P7 attributed this increase to a lack of initial understanding (“I’m not sure how should I do it”), successful collaboration (“as I discussed with her and I . . . we’re starting to find out a plan”) and a rush to finish due to time constraints (“time is out soon so I concentrate”). Overall, this pattern seemed to be associated with a feeling of urgency to finish, causing focus to increase, and use of language and ideas writ- ten in the text-chat box to assist with composition writing. Finally, the rollercoaster pattern was found in more text-chat trajectories (13% of focus patterns; 17% of interest patterns) than video-chat trajectories (0% of focus patterns; 13% of interest patterns). Most of the participants in this cate- gory experienced a declining pattern but then rapidly increased their engagement at a certain moment in the task. For example, P11 experienced a sudden change in trajectory at 27-30 minutes during the text-chat task (see Figure 5). She at- tributed this dramatic change to discovering a valuable idea that was written by her partner in the text-chat (“Because I suddenly think something I can add in my part, because I saw my . . . I saw that I miss my partner’s message in uh- in the right corner, so I need to finish my new part”) and the transition to editing her partner’s paragraph in the final minutes (“I need to check my partner’s part, so I have uh- maximum focus uh- reversed”). Sudden interruptions in communication Scott Aubrey 76 due to overlapping messaging, or breakthroughs, caused by retrieval of language or ideas from chat records, were characteristics of the rollercoaster pattern. 5. Discussion The first research question asked whether there were group-level differences in learner engagement when computer-mediated CW tasks were performed in text-chat and video-chat conditions. The results revealed that learners’ cogni- tive-affective engagement when aggregated over 12 three-minute segments, was significantly higher in the video-chat mode than the text-chat mode for both focus (p < .01; r = 0.72) and interest (p = .02; r = 0.58). The elevated level of focus for the video-chat task is consistent with research that suggests focused atten- tion is optimized during L2 multimodal tasks when multimodal input is inte- grated (e.g., synchronous audio/video communication) (Al-Shehri & Gitsaki, 2010; Guichon & McLornan, 2008). Furthermore, the combined higher focus/in- terest scores suggest that video-chat tasks may generate interest as a by-prod- uct of heightened focus as learners experience feelings of challenge, automatic- ity, and enjoyment (Chen, 2001; Csikszentmihalyi et al., 2005; Egbert, 2003). Re- garding group-level changes in engagement, although a slight decreasing trend in the video-chat condition could be observed, variation in both task conditions was minimal (less than a 2-point variation on a 10-point scale, see Figure 4). This is in line with Guo et al.’s (2020) finding that learners’ effort and enjoyment fluc- tuate minimally during learning tasks when measurements are averaged across learners. Such little variation may indicate that group-level analyses obscure the more extreme fluctuations at an individual level, which is of interest in complex dynamic systems research (Larsen-Freeman & Cameron, 2008) and discussed in relation to the third research question. The second research question asked what factors influenced changes in engagement in each task condition. Learner factors, task design factors, task process factors, and task condition factors were identified as perceived influ- ences. Task condition factors, such as communication mode, and task process factors, such as collaboration and idea conceptualization, were most prominent in accounting for changes in engagement in both tasks. Consistent with previous research, reasons for reduced engagement in the text-chat communication mode were related to the time-consuming nature of text-chat interaction (Zei- gler, 2016) and non-contingent turn-taking due to overlapping messages (Lai et al., 2008; Loewen & Wolff, 2016). As messages were sometimes reported to be delayed and out of sequence in text-chat mode, there was likely a split-attention effect (Mayer & Moreno, 1998), whereby learners needed to expend additional attention “untangling” overlapping messages, leading to cognitive overload, Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 77 anxiety, and reduced task engagement. However, increases in engagement were frequently attributed to learners’ use of the chat record as an ideational and linguistic resource, which may have facilitated productivity during writing. The straightforward process of transforming chat resources into resources for com- posing has been documented in pre-task planning research (Liao, 2018) and is uniquely beneficial to learners performing computer-mediated CW tasks. Over- all, learners were less divided in their perception of the video-chat communica- tion mode, with results indicating that engagement derived from substantial col- laboration and idea conceptualization. As previous CW studies have indicated enhanced collaboration in FTF mode compared with text-chat mode (Kessler et al., 2020; Liao, 2018), it is possible that video-chat closely approximates FTF in- teraction in terms of the overall collaborative experience. In support of this claim, reports from several learners indicate that video-chat seemed to facilitate “social presence” to a greater degree as linguistic (verbal) and extralinguistic (visual) information were used to establish feelings of immediacy, intimacy, and sociability (Chamberlin Quinlisk, 2008; Yamada & Akahori, 2009). For both modes, task design factors and learner factors seemed to mediate engagement to a moderate extent. Consistent with task-based research (e.g., Aubrey, 2017a, 2017b; Dao & Sato, 2021; Qiu & Lo, 2017), task interest and familiarity played a facilitating role in engagement. The third research question asked whether there were common patterns of fluctuation in engagement during the tasks. Four distinct patterns emerged from the data, with just over half of the participants’ engagement exhibiting lit- tle variation (i.e., moderately steady). The remaining engagement trajectories exhibited considerable variation (i.e., increasing, decreasing and rollercoaster patterns), with some participants reporting extreme changes from very positive to very negative interest and focus (e.g., P11 reported a 6-point increase in a 9- minute period, see Figure 5). This is consistent with previous research that has found affective variables (e.g., anxiety, enjoyment: Boudreau et al., 2018) and conative variables (e.g., willingness to communicate: MacIntrye & Gregersen, 2021; MacIntyre & Serroul, 2015) to be susceptible to considerable change within short speaking tasks. Admittedly, compared to these studies, which pro- vided ratings on a per-second basis, the larger 3-minute rating interval in this study may have masked micro-fluctuations within each segment; however, measurements were sufficiently nuanced to the extent that it was possible to identify patterns that were not seen when engagement was averaged across all learners (see Figure 2). A notable observation was that more video-chat task per- formances exhibited high and moderately stable engagement trajectories, while text-chat performances had more variable and increasing trajectories (see Table 5). This might suggest a trade-off effect in how the two modes impact engagement Scott Aubrey 78 during CW tasks. That is, video-chat mode may afford learners a better environ- ment to collaboratively generate ideas, thereby sustaining focus and interest during planning; on the other hand, the text-chat mode, which requires learners to permanently record written ideas during interaction, can facilitate more fo- cused production during composition writing. At the same time, however, there is evidence to suggest that engagement-inhibiting learner factors (e.g., per- ceived lack of proficiency) can override task condition influences, leading to de- clining engagement regardless of mode for some learners (see P14 and P15, Ta- ble 5). Taken together, these findings highlight the complex relationship be- tween learner internal and learner external factors and their combined influ- ence on engagement during computer-mediated CW tasks. 6. Implications Importantly, this research addresses the pedagogical issue of how teachers should implement computer-mediated CW tasks. Video-chat tasks demand more attention and generate a higher level “social presence,” which may benefit advanced learners who are comfortable with the quicker pace of spoken collab- oration and/or who are familiar with their interlocutor(s) and thus value inti- macy in communication. The video-chat mode may also be suitable for learners who complete tasks that require ample idea conceptualization (e.g., problem- solution tasks). However, the slower pace of text-chat may benefit learners who are less fluent, and the permanent nature of the chat script may provide a scaf- fold through which learners can refer to planned ideas and language as they compose their writing. If teachers decide to combine communication modes during a single task, they might consider doing so in a principled manner and in line with engagement needs of the writing process. For example, teachers might begin with an initial stage using video-chat which focuses on task understanding and idea generation, followed by a text-chat stage in which learners encode their ideas in the written language via interaction before they begin writing their joint composition. Such integration would take advantage of the relative bene- fits of each communication mode while gradually diverting learners’ attention from planning ideas and language to drafting and editing, thus optimizing learn- ers’ cognitive-affective engagement throughout the task. 7. Conclusions This study has compared a group-level analysis and a dynamic-level analysis of individual learners’ engagement when Hong Kong learners of English completed computer-mediated CW tasks in video-chat and text-chat mode. In terms of Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 79 methodology, this research represents the first foray into engagement dynamics during computer-mediated CW tasks, an approach that has been encouraged by scholars (e.g., Hiver et al., 2021). In contrast to previous studies in computer- mediated CW tasks that have looked at observable engagement via peer inter- action (e.g., Hsu, 2019; Li, 2013), this research has probed into the more invisible cognitive-affective factors using self-reported measures of focus and interest. It has revealed that learners were significantly more engaged in video-chat than text-chat tasks overall, which can be primarily attributed to the more immediate way in which learners can process multimodal information during interaction to collaborate and plan ideas for the task, requiring high levels of focused attention and generating elevated levels of interest. Examining individual dynamic pat- terns of engagement, findings suggest that the text-chat mode produces more increasing patterns as learners initially struggle with the less efficient communi- cation mode but then experience more focused writing in the latter stages of the task as they draw on pre-planned ideas in their chat records. In sum, this study contributes to our knowledge of the relative benefits of different com- puter-mediated modes of interaction during CW and provides further evidence that engagement is dynamic in various kinds of L2 tasks. The limitations of this study should be highlighted. First, as this research examined the cognitive-affective aspect of engagement, we did not investigate the behavioral dimension of engagement (e.g., collaborative discourse or writ- ing behavior). Although some studies have looked at CW behavior at different stages of longer multi-week writing projects (e.g., Kessler & Bikowski, 2010), it would be a novel approach to examine changes in observable written and/or interactional behavior on a shorter timescale (e.g., per-minute). Combining this with self-report ratings of engagement would shed light on the relationship be- tween different dimensions of engagement (i.e., behavioral, cognitive, affective, social), which remains underexplored (Philp & Duchesne, 2016). Similarly, writ- ing outcomes (i.e., fluency, accuracy, complexity) were not analyzed. A process- product approach (Long, 2015), in which engagement (i.e., the process) is re- lated to resultant writing quality (i.e., the product), may highlight which patterns of engagement are desirable for facilitating optimal writing outcomes. Finally, regarding engagement ratings, having learners rate their focus and interest at three-minute intervals was deemed suitable considering the longer duration of CW writing tasks (as opposed to shorter speaking tasks). However, future re- search may consider employing ratings at smaller timescales (e.g., per-second) using idiodynamic computer software (e.g., Boudreau et al., 2018; MacIntyre, 2012), which may capture important momentary changes. Scott Aubrey 80 References Al-Shehri, S., & Gitsaki, C. (2010). Online reading: A preliminary study of the impact of integrated and split-attention formats on L2 students’ cognitive load. Re- CALL, 22(3), 356-375. https://doi.org/10.1017/S0958344010000212 Aubrey, S. (2017a). Inter-cultural contact and flow in a task-based Japanese EFL classroom. Language Teaching Research, 21(6), 717-734. https://doi.org/10. 1177/1362168816683563 Aubrey, S. (2017b). Measuring flow in the EFL classroom: Learners’ perceptions of in- ter- and intra-cultural task-based interactions. TESOL Quarterly, 51(3), 661-692. Aubrey, S., King, J., & Almkiled, H. A. A. (2020). Language learner engagement during speaking tasks: A longitudinal study. RELC Journal. https://doi.org/ 10.1177/0033688220945418 Aubrey, S., & Shintani, N. (2021). L2 writing and language learning in electronic environments. In R. M. Manchón & C. Polio (Eds.), Handbook of second language acquisition and writing (pp. 282-296). Routledge. Boudreau, C., MacIntyre, P. D., & Dewaele, J.-M. (2018). Enjoyment and anxiety in second language communication: An idiodynamic approach. Studies in Second Language Learning and Teaching, 8(1), 149-170. https://doi.org/ 10.14746/ssllt.2018.8.1.7 Chamberlin Quinlisk, C. (2008). Nonverbal communication, gesture and second language classrooms: A review. In S. G. McCafferty & G. Stam (Eds.), Ges- ture: SLA and classroom research (pp. 25-44). Routledge. Chen, A. (2001). A theoretical conceptualization for motivation research in phys- ical education: An integrated perspective. Quest, 53, 35-58. https://doi.org /10.1080/00336297.2001.10491729 Cho, H. (2017). Synchronous web-based collaborative writing: Factors mediating interaction among second-language writers. Journal of Second Language Writing, 36(2), 37-51. https://doi.org/10.1016/j.jslw.2017.05.013 Chun, D. M. (1998). Signal analysis software for teaching discourse intonation. Lan- guage Learning & Technology, 2(1), 61-77. https://doi.org/10125/25033 Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education. Routledge. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper Perennial. Csikszentmihalyi, M., Abuhamdeh, S., & Nakamura, J. (2005). Flow. In A. J. Elliot, & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 598- 608). Guilford. Dao, P., & Sato, M. (2021). Exploring fluctuations in the relationship between learners’ positive emotional engagement and their interactional behav- iors. Language Teaching Research. Advance online publication. https://doi. org/10.1177/13621688211044238 Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 81 Develotte, C., Guichon, N., & Vincent, C. (2010). The use of the webcam for teaching a foreign language in a desktop videoconferencing environment. ReCALL, 22(3), 293-312. https://doi.org/10.1017/S0958344010000170 Egbert, J. (2003). A study of flow theory in the foreign language classroom. Modern Language Journal, 87(4), 499-518. https://doi.org/10.1111/1540-4781.00204 Elola, I., & Oskoz, A. (2010). Collaborative writing: Fostering foreign language and writing conventions development. Language Learning & Technology, 14(3), 51-71. Fredricks, J. A., Blumenfeld P. C., & Paris A. H. (2004). School engagement: Po- tential of the concept, state of the evidence. Review of Educational Re- search, 74(1), 59-109. https://doi.org/10.3102/00346543074001059 Guichon, N., & McLornan, S. (2008). The effects of multimodality on L2 learners: Implications for CALL resource design. System, 6(1), 85-93. https://doi. org/10.1016/j.system.2007.11.005 Guo, Y., Xu, J., & Xu, X. (2020). An investigation into EFL learners’ motivational dy- namics during a group communicative task: A classroom-based case study. System, 89(1), 1-15. https://doi.org/10.1016/j.system.2020.102214 Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60, 549-571. https://doi.org/10.3102/ 00346543060004549 Hiver, P., Al-Hoorie, A., Vitta, J., & Wu, J. (2021). Engagement in language learning: A systematic review of 20 years of research methods and definitions. Lan- guage Teaching Research. https://doi.org/10.1177/13621688211001289 Hong Kong examinations and assessment authority. (2015). IELTS. http://www. hkeaa.edu.hk/en/recognition/benchmarking/hkdse/ielts/ Hsu, H. C. (2019). Wiki-mediated collaboration and its association with L2 writ- ing development: An exploratory study. Computer Assisted Language Learning, 32(8), 1-23. https://doi.org/10.1080/09588221.2018.1542407 Kessler, G., & Bikowski, D. (2010). Developing collaborative autonomous lan- guage learning abilities in computer mediated language learning: Atten- tion to meaning among students in wiki space. Computer Assisted Lan- guage Learning, 23(1), 41-58. Kessler, M., Polio, C., Xu, C., & Hao, X. (2020). The effects of oral discussion and text chat on L2 Chinese writing. Foreign Language Annals, 53(4), 666-685. https://doi.org/10.1111/flan.12491 Krapp, A. (2003). Interest and human development: An educational-psychologi- cal perspective. In L. Smith, C. Rogers, & P. Tomlinson (Eds.), Development and motivation: Joint perspectives (pp. 57-84), British Journal of Educa- tional Psychology Monograph Series II(2). Scott Aubrey 82 Lai, C., Fei, R., & Roots, R. (2008). The contingency of recasts and noticing. CAL- ICO Journal, 26(1), 70-90. https://doi.org/10.1558/cj.v26i1.70-90 Lambert, C., Philp, J., & Nakamura, S. (2017). Learner-generated content and engagement in second language task performance. Language Teaching Research, 21(6), 665-680. https://doi.org/10.1177/1362168816683559 Larsen-Freeman, D., & Cameron, L. (2008). Complex systems and applied linguis- tics. Oxford University Press. Lee, L. (2010). Exploring wiki-mediated collaborative writing: A case study in an elementary Spanish course. CALICO Journal, 27(2), 260-276. https://doi. org/10.11139/cj.27.2.260-276 Li, M. (2013). Individual novices and collective experts: Collective scaffolding in wiki-based small group writing. System, 41(3), 752-769. https://doi.org/10. 1016/j.system.2013.07.021 Li, M. (2018). Computer-mediated collaborative writing in L2 contexts: An anal- ysis of empirical research. Computer Assisted Language Learning, 31(8), 882-899. https://doi.org/10.1080/09588221.2018.1465981 Liao, J. (2018). The impact of face-to-face oral discussion and online text chat on L2 Chinese writing. Journal of Second Language Writing, 41, 27-40. https:// doi.org/10.1016/j.jslw.2018.06.005 Loewen, S., & Wolff, D. (2016). Peer interaction in FTF and CMC contexts. In M. Sato & S. Ballinger (Eds.), Peer interaction and second language learning: Pedagogical potential and research agenda (pp. 163-184). John Benjamins. Long, M. H. (2015). Experimental perspectives on classroom interaction. In N. Markee (Ed.), Handbook of classroom discourse and interaction (pp. 60- 73). Wiley- Blackwell. MacIntyre, P. D. (2012). The idiodynamic method: A closer look at the dynamics of communication traits. Communication Research Reports, 29(4), 361- 367. https://doi.org/10.1080/08824096.2012.723274 MacIntrye, P. D., & Gregersen, T. (2021). The idiodynamic method: Willingness to communicate and anxiety processes interacting in real time. International Review of Applied Linguistics. https://doi.org/10.1515/iral-2021-0024. MacIntyre, P. D., & Serroul, A. (2015). Motivation on a per-second timescale: Examining approach-avoidance motivation during L2 task performance. In Z. Dörnyei, P. D. MacIntyre, & A. Henry (Eds.), Motivational dynamics in language learning (pp. 109-138). Multilingual Matters. Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educa- tional Psychology, 90, 312-320. https://doi.org/10.1037/0022-0663.90.2.312 Mercer, S., & Dörnyei, Z. (2020). Engaging language learners in contemporary classrooms. Cambridge University Press. Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 83 Moreno, R. (2005). Instructional technology: Promise and pitfalls. In L. PytlikZillig, M. Bodvarsson, & R. Bruning (Eds.), Technology-based education: Bringing re- searchers and practitioners together (pp. 1-19). Information Age Publishing. Mystkowska-Wiertelak, A. (2020). Teachers’ accounts of learners’ engagement and disaffection in the language classroom. The Language Learning Jour- nal. https://doi.org/10.1080/09571736.2020.1800067 Németh, N., & Kormos, J. (2001). Pragmatic aspects of task-performance: The case of argumentation. Language Teaching Research, 5(3), 213-240. https:// doi.org/10.1177/136216880100500303 Pawlak, M., Mystkowska-Wiertelak, A., & Bielak, J. (2016). Investigating the na- ture of classroom willingness to communicate (WTC): A micro-perspec- tive. Language Teaching Research, 20(5), 654-671. https://doi.org/10.1177/ 1362168815609615 Philp, J., & Duchesne, S. (2016). Exploring engagement in tasks in the language classroom. Annual Review of Applied Linguistics, 36, 50-72. https://doi.org/ 10.1017/S0267190515000094 Phung, L., Nakamura, S., & Reinders, H. (2020). The effect of choice on affective engagement: Implications for task design. In P. Hiver, S. Mercer, & A. Al- Hoorie (Eds.), Student engagement in the language classroom (pp. 163- 181). Multilingual Matters. Qiu, X., & Lo, Y. (2017). Content familiarity, task repetition and Chinese EFL learn- ers’ engagement in second language use. Language Teaching Research, 21(6), 681-698. https://doi.org/10.1177/1362168816684368 Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Chris- tenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 3-19). Springer. Schmidt, R. (1990). The role of consciousness in second language learning. Applied Linguistics, 11(2), 129-158. https://doi.org/10.1093/applin/11.2.129. Shearer, R. L., & Park, E. (2019). Theory to practice in instructional design. In M. G. Moore & W. C. Diehl (Eds.), Handbook of distance education (pp. 260- 280). Routledge. Skinner, E. A., Kindermann, T. A., & Furrer, C. (2009). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69(3), 493- 525. https://doi.org/10.1177/0013164408323233 Storch, N. (2019). Collaborative writing. Language Teaching, 52(1), 40-59. https:// doi.org/10.1017/s026144481800032 Scott Aubrey 84 Svalberg, A. M. L. (2009). Engagement with language: Interrogating a construct. Language Awareness, 18(3-4), 242-258. https://doi.org/10.1080/096584 10903197264 Warschauer, M. (1997). Computer-mediated collaborative learning: Theory and practice. Modern Language Journal, 81(4), 470-481. https://doi.org/10.11 11/j.1540-4781.1997.tb05514.x Yamada, M., & Akahori, K. (2009). Awareness and performance through self- and partner’s image in videoconferencing. CALICO Journal, 27(1), 1-25. https:// doi.org/10.1558/cj.27.1.1-25 Yim, S., & Warschauer, M. (2017). Web-based collaborative writing in L2 con- texts: Methodological insights from text mining. Language Learning & Technology, 21(1), 146-165. https://doi.org/10125/44599 Zeigler, N. (2016). Taking technology to task: Technology-mediated TBLT, perfor- mance, and production. Annual Review of Applied Linguistics, 36, 136- 163. https://doi.org/10.1017/S0267190516000039 Zhou, S., Hiver, P., & Al-Hoorie, A. H. (2021). Measuring L2 engagement: A review of issues and applications. In P. Hiver, A. H. Al-Hoorie, & S. Mercer (Eds.), Student engagement in the language classroom (pp. 75-98). Multilingual Matters. Dynamic engagement in second language computer-mediated collaborative writing tasks: Does . . . 85 APPENDIX A Task prompt 1 Scott Aubrey 86 APPENDIX B Task prompt 2