Daniel M\u00e1rquez<\/strong> This study investigates how task complexity shapes motivation and performance in second language (L2) pragmatics, a domain that remains underexplored within task-based learning research. To address this gap, 37 secondary school learners of English completed both simple and complex decision-making tasks at three intervals over an eight-week period. Task complexity was manipulated through adjustments to intrinsic task elements and reasoning demands. Motivation was assessed via Likert-scale questionnaires, semi-structured interviews, and an analytical turn-taking metric, while performance was measured through pragmatic complexity and accuracy in suggestion speech acts. The findings revealed that higher self-reported motivation corresponded with increased pragmatic complexity and accuracy, particularly during more cognitively demanding tasks. Additionally, motivation was found to fluctuate according to cognitive load, underscoring the significance of task relevance and contextual factors in sustaining learner engagement over time. These insights contribute to a deeper understanding of the dynamic interplay between motivation and cognitive complexity in L2 pragmatics and offer valuable implications for pedagogical practice and future research.<\/p>\n Keywords<\/em><\/strong>: task motivation, task complexity, second language pragmatics, suggestion speech acts<\/p>\n Motivation is a fundamental driver in second language (L2) acquisition, fueling learners\u2019 persistence and effort and thereby influencing success (D\u00f6rnyei, 2020). In the L2 classroom, it strongly shapes task performance. While learners pursue long-term goals like fluency, progress often depends on completing smaller, incremental tasks (D\u00f6rnyei, 2005). These tasks help manage the complexities of language acquisition, which highlights the need for workplans that sustain motivation (Ellis et al., 2019).<\/p>\n In task-based language teaching (TBLT), task complexity refers to the cognitive and attentional demands required for effective task execution (Robinson, 2007). Although its impact on L2 output has been widely studied (see Jackson & Suethanapornkul, 2013; Johnson, 2017, for a meta-analysis) and linked to aspects of speech act production (M\u00e1rquez & Bar\u00f3n, 2021), its broader interplay with L2 pragmatic competence has not been thoroughly scrutinized (Taguchi & Kim, 2018). Since task complexity is theorized to enhance motivation by offering challenge (Robinson, 2001), and recent work calls for the integration of motivational dimensions in task-based research (Lambert et al., 2023), this study applies task complexity principles to academic task design, pragmatic assessment, and the analysis of how motivation interacts with oral performance.<\/p>\n Examining task motivation alongside task complexity is essential for understanding L2 pragmatic development. As a multifaceted, dynamic construct (D\u00f6rnyei, 2019a), motivation often escapes full capture in traditional quantitative, cross-sectional studies, which may overlook its temporal fluctuations (Zhang & Aubrey, 2024). To address this, a longitudinal mixed-methods design was employed to track motivation over time and its relationship with task complexity in L2 pragmatics. While prior research has focused on speech acts like requests, apologies, and refusals (Toprak, 2020), this study centers on suggestions\u2013a directive act in which the speaker proposes an action without exerting strong pressure on the listener (Searle, 1969).<\/p>\n Future images of the ideal L2 user represent long-term goals, yet proficiency is achieved through many small steps. Academic tasks help teachers and researchers segment the complex process of language learning into manageable units (D\u00f6rnyei, 2005). These discrete observations support a broader understanding of L2 motivation. As D\u00f6rnyei (2002, p. 80) notes, \u201clanguage learning tasks can be seen as the culmination of the situated approach in L2 motivation research,\u201d since they offer a uniquely contextualized lens. Although tasks support L2 knowledge acquisition and consolidation, they do not always meet their purpose. Some students engage fully, while others participate minimally, missing key learning opportunities\u2013often due to differences in motivation. Research shows that task motivation arises from a complex interaction between task-related factors and motivational variables (Poupore, 2013).<\/p>\n Two major approaches to task motivation stem from J\u00fclkunen (1989) and D\u00f6rnyei (2005). J\u00fclkunen distinguishes between trait motives\u2013stable, long-term intentions to learn\u2013and state motives\u2013which are temporary and shaped by task topics or outcomes. He contends that \u201cdifferent tasks affect motivation and learning in different ways\u201d (2001, p. 33), largely due to task-specific, state-like appraisals. These are conditioned by task value, perceived control, competence, satisfaction, and task presentation, all of which influence engagement. Learner characteristics also interact with these factors.<\/p>\n D\u00f6rnyei (2005) takes a more nuanced view, rejecting the simple trait-state dichotomy. He defines motivation as a complex concept that governs the direction, persistence, and effort behind human behavior, including task engagement (D\u00f6rnyei, 2020). Emerging from the cognitive-situated phase of L2 motivation theory (D\u00f6rnyei & Ryan, 2015), this approach highlights the dynamic interplay between situational task features and stable dispositions. These \u201cactional contexts\u201d activate motivational mindsets that evolve during performance, creating a complex, interactive process.<\/p>\n The motivational mindsets generate a dynamic system consisting of task execution, appraisal, and action control (D\u00f6rnyei, 2005). Task execution refers to engagement in task-supportive behaviors, following instructions or personal goals. Appraisal entails continuous evaluation of progress and feedback, comparing actual with expected outcomes. Action control encompasses self-regulatory strategies to maintain or adjust effort during tasks. Together, these mechanisms enable learners to monitor and optimize task-based learning.<\/p>\n Despite extensive research, task motivation remains hard to delineate. D\u00f6rnyei (2019b) attributes this to the multiple factors involved, including learner-specific, situational, and task-related variables. Recent studies link task motivation to several core motivational concepts (Kormos & Wilby, 2019), one of which is goal orientation: learners\u2019 reasons for engaging in academic tasks (Anderman et al., 2002). These cover mastery orientations, focused on skill development, and performance orientations, aimed at demonstrating L2 competence (Ames, 1992).<\/p>\n Other integral components include intrinsic motivation, flow, and interest. Intrinsic motivation arises from the inherent satisfaction of the task itself (Deci & Ryan, 1985). Nakamura and Cs\u00edkszentmih\u00e1lyi (2002, 2014) describe flow as a state of deep immersion in activities where challenge and skill are balanced, boosting engagement and performance. Interest, defined by Ainley et al. (2002) as a psychological state of focused attention and persistence, positively affects learning outcomes (Hidi, 2006).<\/p>\n Self-efficacy and expectancy-value theory also underpin task motivation (Kormos & Wilby, 2019). Self-efficacy refers to beliefs about one\u2019s ability to succeed in a task (Bandura, 2010), with higher efficacy leading to greater effort and persistence (Bandura, 1994). Expectancy-value theory (Eccles & Wigfield, 2002) posits that task engagement is driven by beliefs about success likelihood and task value. These perceptions influence motivation and performance (Eccles, 2005).<\/p>\n Research on task motivation highlights its multidimensional, context-sensitive nature. D\u00f6rnyei and Kormos (2000) shifted focus from traditional performance metrics to observable behaviors such as turn-taking and word production, emphasizing the influence of attitudes and self-confidence. Positive attitudes promote sustained engagement, while negative ones lead to unpredictable outcomes. Building on this, Ma (2009) applied Self-Determination Theory, showing that task motivation is closely connected to the satisfaction of autonomy, competence, and relatedness needs. Her findings reinforce the view that motivation is shaped by how well tasks meet these psychological needs, expanding on the contextual focus of D\u00f6rnyei and Kormos.<\/p>\n Adding further depth, Poupore (2013) introduced a Complex Dynamic Systems approach, challenging linear models of motivation. His research showed that motivation fluctuates during task execution, influenced by difficulty and emotional response. Mozgalina (2015) supported this by explaining that excessive autonomy, when paired with high cognitive complexity, can overwhelm learners and reduce motivation. Her findings point to the importance of balancing autonomy with structured support, echoing Poupore\u2019s conclusion that excessive demands deplete motivational resources.<\/p>\n Safdari (2021) extended the discussion by linking task motivation to long-term outcomes, showing that high motivation enhances not only immediate performance but also knowledge transfer, retention, and deep learning. Guo et al. (2020) and Agawa (2020) further underscored that motivation fluctuates based on cognitive load, group dynamics, and social factors. Guo et al. documented minute-by-minute motivational shifts, while Agawa highlighted the role of social relatedness\u2013such as peer recognition\u2013in sustaining engagement.<\/p>\n Collectively, these studies confirm that task motivation results from complex interactions among attitudes, cognitive demands, autonomy, and social dynamics. Effective task design must navigate this complexity, moving beyond one-size-fits-all models to foster personalized, engaging learning experiences.<\/p>\n Two prominent theoretical models have influenced research on task complexity\u2013the inherent processing demands of tasks (Robinson, 2007): Skehan\u2019s Limited Attentional Capacity Model (1998, 2014) and Robinson\u2019s Cognition Hypothesis (2001). Skehan posits that increased cognitive demands compel learners to prioritize aspects of performance\u2013complexity, accuracy, fluency\u2013due to limited attention. Conversely, Robinson argues that attention can be drawn from multiple resource pools, making such competition avoidable. While both frameworks offer valuable insights into cognitive processing in task-based learning (Michel, 2017), their application to L2 pragmatics remains limited (Taguchi & Kim, 2018).<\/p>\n Robinson\u2019s (2007) Triadic Componential Model suggests that task complexity affects attentional allocation, either dispersing focus or concentrating it on specific linguistic forms. Increased complexity may draw attention toward relevant L2 features, enhancing accuracy. Robinson also notes that task complexity interacts with task conditions and perceived difficulty. Perceptions of task difficulty\u2013informed by motivational dynamics (D\u00f6rnyei, 2020)\u2013influence task engagement and performance. Therefore, investigating motivation alongside complexity is central for understanding learners\u2019 responses to demanding tasks.<\/p>\n Task-based research has largely focused on task conditions and complexity, often overlooking learners\u2019 attitudes and motivation (Lambert et al., 2023). Few studies have examined the motivation-complexity interplay. Kormos and D\u00f6rnyei (2004) showed that motivation significantly affects output quantity, though its influence on quality is more nuanced. High task attitudes yielded more complex outputs and richer argumentation, whereas learners with low task attitudes but high general motivation improved accuracy and engagement. This suggests that motivation drives output quantity, but its effect on sophistication is mediated by task-specific attitudes and context.<\/p>\n Kormos and Pr\u00e9fontaine (2017) extended this by indicating that greater task complexity can heighten anxiety, which undermines fluency despite motivational gains. Their findings underscore a non-linear relationship between complexity and motivation, with emotional factors like anxiety moderating outcomes. Similarly, Han and McDonough (2019) examined motivational orientations. Contrary to expectations, prevention-focused learners\u2013typically more vigilant\u2013exhibited lower accuracy, possibly due to anxiety. In contrast, promotion-focused learners demonstrated higher lexical complexity and greater linguistic risk-taking. These results suggest that motivational orientation influences how learners approach complex tasks, and that anxiety associated with prevention may hinder performance.<\/p>\n In L2 pragmatics, studies on task complexity are even more limited. Recent research reinforces motivation\u2019s role in engaging with nuanced pragmatic features, which often demand sensitivity to cultural and contextual cues (Zhang & Aubrey, 2024). Takahashi (2023) found that mastery-oriented learners with sustained motivation achieved deeper understanding through metapragmatic analysis. Conversely, those who lacked focus on target forms processed them superficially, despite high motivation. Learners with performance goals who remained interested in target forms also performed well, suggesting that interest can compensate for orientation type. Additionally, factors like familiarity, listening skills, and cognitive abilities interacted with external conditions, influencing motivation and learning outcomes.<\/p>\n As outlined above, prior research shows that task complexity and motivation significantly influence task performance, and that cognitively demanding tasks may enhance learner motivation. However, how motivation evolves in pragmatic tasks and how complexity shapes L2 pragmatic performance over time remain unclear. To address these gaps, the following research questions were formulated:<\/p>\n RQ1. How does motivation to perform pragmatic tasks change over time among L2 learners?<\/p>\n RQ2. What is the relationship between task complexity and motivation in pragmatic task performance over time?<\/p>\n This study involved 37 secondary school students aged 16\u201318 (M<\/em> = 17.2, SD<\/em> = 0.66), all native Czech speakers, including seven early bilinguals. They had studied English since primary school, and received four hours of instruction weekly. None had lived or studied in English-speaking countries, though 23 had supplementary lessons via language schools or private tutors. Based on self-reports, teacher evaluations, and LexTALE scores, participants were classified as upper-intermediate to advanced L2 users (see Council of Europe, 2020).<\/p>\n In line with ethical standards, participants provided written informed consent in their native language. The form outlined the study\u2019s aims, procedures, and duration. It also clarified that participation was voluntary at all stages, grades would not be affected, and anonymity would be preserved. All participants consented to the use and sharing of their data for research purposes.<\/p>\n Participants completed three decision-making tasks centered around familiar contexts (e.g., work, school, leisure): planning an exchange trip, organizing a fundraising event, and arranging a graduation party. Each task followed a uniform scenario, where learners imagined belonging to one of two school groups that had previously discussed event ideas. One student per group represented their peers in a meeting, during which pairs negotiated suggestions and attempted to persuade one another to adopt their group\u2019s preferences. They concluded by agreeing on a final proposal and presenting it as if to a school council. This setup was designed to elicit suggestion speech acts through authentic negotiation and persuasion; pilot testing confirmed its effectiveness.<\/p>\n Tasks followed Ellis\u2019s (2017) guidelines, emphasizing pragmatic language use and meaning-focused communication. Each was developed in simple and complex versions. Complex tasks added elements requiring greater working memory and linguistic control (Robinson, 2007), increasing cognitive load through interconnected components and enhanced reasoning demands. While simple tasks involved basic information exchange, complex tasks required evaluating and justifying decisions across more options (see Appendix A<\/a>). Task complexity was validated by 24 pilot students using the same difficulty scale as in the main study. Complex tasks were rated significantly more challenging, with large effect sizes (trip task: z<\/em> = 3.09, p<\/em> < .001, r<\/em> = .631; fundraising task: z<\/em> = 3.52, p<\/em> < .001, r<\/em> = .719; party task: z<\/em> = 3.28, p<\/em> < .001, r<\/em> = .670).<\/p>\n Given task motivation\u2019s multifaceted nature (D\u00f6rnyei, 2019b), thirteen items from an existing instrument (Kormos et al., 2020) were adapted to examine clusters of related factors. Participants provided self-ratings, based on the assumption that individuals can gauge the mental effort involved in cognitive activities (R\u00e9v\u00e9sz et al., 2016). The items employed a semantic differential scale, presenting bipolar anchors and asking respondents to position themselves between these extremes (D\u00f6rnyei & Csiz\u00e9r, 2012).<\/p>\n
\nCharles University
\n<daniel.marquez![\"atmark\"](\"http:\/\/www.tesl-ej.org\/atmark.png\")
ff.cuni.cz><\/p>\nAbstract<\/h3>\n
Literature Review<\/h3>\n
Task Motivation<\/h4>\n
Empirical Studies on Task Motivation<\/h4>\n
Task Complexity and Task Motivation<\/h4>\n
Research Questions<\/h4>\n
Method<\/h3>\n
Participants<\/h4>\n
Tasks<\/h4>\n
Task Motivation Questionnaire<\/h4>\n
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