The Interaction of Task Motivation and Task Complexity in Pragmatic Performance

November 2025 – Volume 29, Number 3

https://doi.org/10.55593/ej.29115a6

Daniel Márquez
Charles University
<daniel.marquezff.cuni.cz>

Abstract

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.

Keywords: task motivation, task complexity, second language pragmatics, suggestion speech acts

Motivation is a fundamental driver in second language (L2) acquisition, fueling learners’ persistence and effort and thereby influencing success (Dörnyei, 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 (Dörnyei, 2005). These tasks help manage the complexities of language acquisition, which highlights the need for workplans that sustain motivation (Ellis et al., 2019).

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 (Márquez & Barón, 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.

Examining task motivation alongside task complexity is essential for understanding L2 pragmatic development. As a multifaceted, dynamic construct (Dörnyei, 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–a directive act in which the speaker proposes an action without exerting strong pressure on the listener (Searle, 1969).

Literature Review

Task Motivation

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 (Dörnyei, 2005). These discrete observations support a broader understanding of L2 motivation. As Dörnyei (2002, p. 80) notes, “language learning tasks can be seen as the culmination of the situated approach in L2 motivation research,” 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–often due to differences in motivation. Research shows that task motivation arises from a complex interaction between task-related factors and motivational variables (Poupore, 2013).

Two major approaches to task motivation stem from Jülkunen (1989) and Dörnyei (2005). Jülkunen distinguishes between trait motives–stable, long-term intentions to learn–and state motives–which are temporary and shaped by task topics or outcomes. He contends that “different tasks affect motivation and learning in different ways” (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.

Dörnyei (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 (Dörnyei, 2020). Emerging from the cognitive-situated phase of L2 motivation theory (Dörnyei & Ryan, 2015), this approach highlights the dynamic interplay between situational task features and stable dispositions. These “actional contexts” activate motivational mindsets that evolve during performance, creating a complex, interactive process.

The motivational mindsets generate a dynamic system consisting of task execution, appraisal, and action control (Dörnyei, 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.

Despite extensive research, task motivation remains hard to delineate. Dörnyei (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’ 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).

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 Csíkszentmihályi (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).

Self-efficacy and expectancy-value theory also underpin task motivation (Kormos & Wilby, 2019). Self-efficacy refers to beliefs about one’s 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).

Empirical Studies on Task Motivation

Research on task motivation highlights its multidimensional, context-sensitive nature. Dörnyei 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 Dörnyei and Kormos.

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’s conclusion that excessive demands deplete motivational resources.

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–such as peer recognition–in sustaining engagement.

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.

Task Complexity and Task Motivation

Two prominent theoretical models have influenced research on task complexity–the inherent processing demands of tasks (Robinson, 2007): Skehan’s Limited Attentional Capacity Model (1998, 2014) and Robinson’s Cognition Hypothesis (2001). Skehan posits that increased cognitive demands compel learners to prioritize aspects of performance–complexity, accuracy, fluency–due 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).

Robinson’s (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–informed by motivational dynamics (Dörnyei, 2020)–influence task engagement and performance. Therefore, investigating motivation alongside complexity is central for understanding learners’ responses to demanding tasks.

Task-based research has largely focused on task conditions and complexity, often overlooking learners’ attitudes and motivation (Lambert et al., 2023). Few studies have examined the motivation-complexity interplay. Kormos and Dörnyei (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.

Kormos and Préfontaine (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–typically more vigilant–exhibited 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.

In L2 pragmatics, studies on task complexity are even more limited. Recent research reinforces motivation’s 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.

Research Questions

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:

RQ1. How does motivation to perform pragmatic tasks change over time among L2 learners?

RQ2. What is the relationship between task complexity and motivation in pragmatic task performance over time?

Method

Participants

This study involved 37 secondary school students aged 16–18 (M = 17.2, SD = 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).

In line with ethical standards, participants provided written informed consent in their native language. The form outlined the study’s 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.

Tasks

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’s 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.

Tasks followed Ellis’s (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). 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 = 3.09, p < .001, r = .631; fundraising task: z = 3.52, p < .001, r = .719; party task: z = 3.28, p < .001, r = .670).

Task Motivation Questionnaire

Given task motivation’s multifaceted nature (Dörnyei, 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 (Révész et al., 2016). The items employed a semantic differential scale, presenting bipolar anchors and asking respondents to position themselves between these extremes (Dörnyei & Csizér, 2012).

The questionnaire addressed six clusters of task motivation (see Appendix B). Four followed Kormos et al. (2020): task appraisal (perceived value), emotional states (feelings during the task), result assessment (performance expectations), and reported effort. Two additional clusters were included: goal-orientedness, which captured future task engagement and collaboration attitudes (Kormos & Préfontaine, 2017; Kormos & Wilby, 2019), and reported willingness to communicate (WTC), which reflected participants’ readiness to speak and share ideas (Dörnyei & Kormos, 2000; Kormos & Dörnyei, 2004). An independent item on task difficulty was added to assess task complexity (Révész et al., 2016), based on the assumption that higher complexity correlates with greater perceived difficulty (Robinson, 2007).

Follow-up Interviews

Half of the students (n = 18) were randomly selected for semi-structured interviews after task performance to elaborate on their questionnaire responses. Interviews were conducted one-on-one in a quiet classroom, lasted approximately five minutes, and focused on task appraisal (e.g., “Do you think the tasks helped improve your English skills? Why?”), emotional states (e.g., “Did you enjoy the tasks? Why?”), result assessment (e.g., “Did you feel capable of communicating everything you intended? What led you to this conclusion?”), and reported effort (e.g., “Were there moments when you were more eager to speak? Why?”). The interviews also explored participants’ perceptions of the relationship between task difficulty and complexity (see Márquez & Barón, 2021), as they compared tasks and explained their reasoning. This qualitative approach offered insights beyond questionnaire findings. As recommended by Dörnyei and Csizér (2012), interviews were conducted in the learners’ first language to enhance data quality.

Data Collection Procedure

This study, part of a broader investigation into task complexity and pragmatic learning, collected data at three time points: pretest, posttest (three weeks later), and delayed posttest (seven weeks after the pretest). Each phase was completed within a single day and involved participants performing both simple and complex versions of a different decision-making task. Task order was counterbalanced across participants with respect to both complexity (simple vs. complex) and topic (trip, fundraiser, or party) to minimize carryover and practice effects. Pairings were kept consistent whenever possible, with changes made only when unavoidable due to logistical constraints. Because the number of participants was uneven, one student volunteered to perform both tasks twice; however, her data were excluded from the analysis. At the delayed posttest, only 34 students participated due to scheduling limitations. The task motivation questionnaire was administered after each task, and individual interviews followed the second task (simple or complex) in each phase. The three testing phases thus involved both performance and associated data collection (see Figure 1).

Figure 1. Data Collection Design

At the posttest and delayed posttest, participants answered the same interview questions as in the pretest, with additional prompts exploring changes in their motivation over time. Task performances and interviews were audio-recorded, transcribed, and coded for subsequent analysis.

Between the pretest and posttest, participants received explicit instruction on suggestion-making to strengthen their understanding of pragmatic forms and encourage reflection on speech acts. Instruction focused on politeness strategies and the regulation of directness, given the face-threatening nature of suggestions (Brown & Levinson, 1987). Pedagogical activities included individual and interactive listening, reading, and speaking tasks, as well as metapragmatic discussions, during which learners received both instructor and peer feedback.

Assessment of Pragmatic Outcomes

To examine the impact of task motivation on pragmatic outcomes, both the quantity and quality of speech acts were evaluated. Quantity was assessed as the number of suggestions per turn, following Gilabert and Barón (2013). Quality was analyzed through complexity and accuracy (Márquez & Michel, 2025). Pragmatic complexity refers to the breadth and depth of L2 realizations, while pragmatic accuracy captures adherence to pragmalinguistic norms and sociopragmatic expectations.

For a holistic evaluation, two five-point rating scales were used: one for complexity and one for accuracy. Complexity descriptors targeted speech act variety, politeness, directness, and sentence structure (Brown & Levinson, 1987; Bulté & Housen, 2012), and accuracy descriptors focused on content delivery, pragmalinguistics, sociopragmatics, interactional engagement, and turn organization (Youn, 2015). Additionally, analytical measures included the number of types per suggestion (for complexity) and the number of pragmatic inaccuracies per suggestion (for accuracy).

Four applied linguists with expertise in pragmatic assessment served as raters. They initially rated a random sample of 104 tasks drawn from all three phases (pretest, posttest, delayed posttest). Each week, approximately 20 tasks were rated independently, followed by online meetings to provide feedback, clarify interpretations, and review training materials. Disagreements were resolved through collaborative transcript review and consultation of the coding scheme. Final scores were determined by consensus. Once acceptable inter-rater reliability was achieved (Krippendorff’s α = .837), the remaining 112 tasks were evenly distributed for independent scoring.

Additional Measure for Task Motivation

In addition to self-reported task motivation, an analytical measure was used to complement the assessment. Specifically, the ratio of turns to total task time was calculated as a behavioral indicator of situated WTC (see Dörnyei, 2002; Dörnyei & Kormos, 2000; Kormos & Dörnyei, 2004). Although this measure does not encompass non-verbal engagement, it offers a context-sensitive proxy for verbal task engagement and communicative orientation. In oral interaction, task motivation is assumed to align with situated WTC due to the interplay of task content, partner dynamics, and opportunities for self-expression (Pawlak & Mystkowska-Wiertelak, 2015).

Data Analysis

All statistical analyses employed two-tailed significance tests with an alpha level of .05. Given that some variables were measured on ordinal Likert scales and others did not meet normality assumptions based on Shapiro-Wilk tests, non-parametric methods were applied where appropriate.

Wilcoxon signed-rank tests compared participants’ perceptions of task difficulty between simple and complex tasks at the pretest and posttest phases, with effect sizes reported as r following Fritz et al. (2012). A prior power analysis confirmed that the sample size was sufficient to detect effects with adequate power. Asymptotic p-values were reported except at the delayed posttest, where exact p-values were used due to slight participant attrition.

Changes in self-reported task motivation over time were analyzed through a generalized linear mixed-effects model, with fixed effects for time and task type, and random intercepts for participants. A separate linear mixed-effects model analyzed situated WTC across time points and task types.

Internal consistency of the six task motivation subscales was assessed via Cronbach’s alpha coefficients, which ranged from .883 to .908 across phases and task types. This indicated high reliability and supported the homogeneity and coherence of the scale items (Dörnyei & Dewaele, 2023).

Spearman rank-order correlation coefficients were calculated separately for each task type and time point to examine relationships between task motivation and pragmatic performance. These correlations included situated WTC and the six motivation questionnaire variables, correlated with outcome measures of pragmatic performance in terms of quantity and quality (both holistic and analytic). Interpretations of correlation benchmarks and effect sizes followed Plonsky and Oswald (2014).

Qualitative data from retrospective interviews were analyzed using an adapted version of Dörnyei’s (2014) retrodictive qualitative modeling. This involved identifying recurring motivational tendencies, mapping responses to specific variables, and tracing shifts in learner trajectories. Sample responses were translated and transcribed to illustrate salient themes.

Results

Assessment of Task Complexity

As shown in Table 1, Wilcoxon signed-rank tests indicated that participants rated the complex task (CT) as significantly more difficult than the simple task (ST) across all three phases: pretest (PE), posttest (PO), and delayed posttest (DP).

Table 1. Statistical Results of Perceived Task Difficulty

Research phase	Task complexity	Min/Max	Mean/Median	SD	Wilcoxon signed-rank test (ST vs. CT)
PE	Simple	4/6	4.95/5	0.84	z = 3.57, p < .001, r = .587
	Complex	3/5	4.35/5	0.85
PO	Simple	4/6	4.95/5	0.74	z = 3.21, p < .001, r = .528
	Complex	2/5	4.43/5	0.83
DP	Simple	4/6	5.06/5	0.73	z = 4.48, p < .001, r = .751
	Complex	2/5	4.32/4	0.72

Task Motivation over Time

Participants demonstrated greater disposition to perform the ST than the CT throughout the study, although motivation declined for both tasks over time (see Table 2). A generalized linear mixed-effects model confirmed a significant motivation decrease at DP compared to PE (β = -0.29, p = .039), with no significant difference between tasks (β = 0.03, p = .845) or interaction effect, indicating similar declines for both the ST and the CT.

Table 2. Statistical Results of Overall Task Motivation

Research phase	Task complexity	Mean	95% confidence interval of mean		SD
			Lower bound	Upper bound
PE	Simple	4.68	4.44	4.93	0.73
	Complex	4.66	4.41	4.91	0.75
PO	Simple	4.57	4.30	4.84	0.81
	Complex	4.44	4.16	4.72	0.84
DP	Simple	4.52	4.22	4.82	0.85
	Complex	4.36	4.07	4.65	0.82

While the model captured overall motivation trends, item-level analyses identified specific motivational dimensions underlying these variations. At PE, only two of the six motivation variables were significantly higher: task appraisal (ST) and reported effort (CT). At PO and DP, scores favored the ST across variables, except for goal-orientedness, which was equal for both tasks at PO. As summarized in Table 3, task appraisal reached significance at all phases, whereas result assessment was significant only at DP.

Table 3. Statistical Results of Task Motivation Factors

Task motivation factor	Research phase	Task complexity	Mean	95% confidence interval of mean		SD	Wilcoxon signed-rank test (ST vs. CT)
				Lower bound	Upper bound
Task appraisal	PE	Simple	5.06	4.86	5.27	.623	z = 2.88, p = .004, r = .473
		Complex	4.81	4.60	5.01	.605
	PO	Simple	4.92	4.72	5.12	.600	z = 2.78, p = .005, r = .457
		Complex	4.70	4.47	4.93	.688
	DP	Simple	4.87	4.65	5.10	.651	z = 2.77, p = .005, r = .475
		Complex	4.66	4.42	4.89	.669
Reported effort	PE	Simple	4.41	4.00	4.80	1.19	z = 2.24, p = .025, r = .368
		Complex	4.68	4.30	5.05	1.13
	PO	Simple	4.51	4.11	4.92	1.22	z = 0.30, p = .764, r = .049
		Complex	4.41	3.92	4.89	1.44
	DP	Simple	4.53	4.11	4.95	1.21	z = 0.88, p = .447, r = .152
		Complex	4.35	3.92	4.79	1.25
Emotional state	PE	Simple	4.34	4.00	4.68	1.01	z = 0.98, p = .324, r = .162
		Complex	4.48	4.15	4.81	.982
	PO	Simple	4.20	3.81	4.59	1.17	z = 0.41, p = .679, r = .068
		Complex	4.10	3.70	4.50	1.20
	DP	Simple	4.24	3.82	4.65	1.19	z = 0.95, p = .353, r = .163
		Complex	4.14	3.75	4.52	1.11
Result assessment	PE	Simple	4.50	4.13	4.87	1.09	z = 1.27, p = .205, r = .209
		Complex	4.39	4.00	4.78	1.16
	PO	Simple	4.59	4.27	4.92	.963	z = 1.50, p = .134, r = .247
		Complex	4.39	3.99	4.80	1.22
	DP	Simple	4.66	4.26	5.06	1.15	z = 2.50, p = .012, r = .429
		Complex	4.35	3.98	4.72	1.05
Goal orientedness	PE	Simple	4.99	4.74	5.23	.731	z = 0.39, p = .693, r = .065
		Complex	4.95	4.70	5.19	.743
	PO	Simple	4.77	4.49	5.05	.830	z = 0.09, p = .926, r = .015
		Complex	4.77	4.51	5.03	.778
	DP	Simple	4.62	4.25	4.98	1.05	z = 0.44, p = .712, r = .077
		Complex	4.54	4.14	4.95	1.15
Self-reported WTC	PE	Simple	4.65	4.31	4.99	1.02	z = 0.14, p = .885, r = .024
		Complex	4.66	4.31	5.02	1.07
	PO	Simple	4.39	4.01	4.77	1.14	z = 0.79, p = .427, r = .131
		Complex	4.27	3.87	4.67	1.19
	DP	Simple	4.16	3.71	4.61	1.30	z = 0.44, p = .663, r = .077
		Complex	4.06	3.58	4.53	1.36

Table 4 shows that most participants were either motivated (PE: 41%; PO: 38%; DP: 35%) or somewhat motivated (consistently 38%) to complete the ST. A similar situation emerged for the CT, with the proportion of motivated learners increasing from PE (43%) to PO (49%) before dropping at DP (38%), while somewhat motivated learners steadily decreased (consistently 32%). Few students reported being very motivated for the ST (PE: 16%; PO: 14%; DP: 15%), with similar rates at PE for the CT (16%) but lower percentages at PO (5%) and DP (9%). A minority were barely motivated, and the number of somewhat unmotivated participants rose over time for both tasks: ST (PE: 5%; PO: 8%; DP: 12%) and CT (PE: 8%; PO: 14%; DP: 21%). Only one learner (3%) was unmotivated (ST at PO).

Table 4. Number of Task-Motivated Participants

Research phase	Task complexity	Very unmotivated	Unmotivated	Somewhat unmotivated	Somewhat motivated	Motivated	Very motivated
PE	ST	0	0	2	14	15	6
	CT	0	0	3	12	16	6
PO	ST	0	1	3	14	14	5
	CT	0	0	5	12	18	2
DP	ST	0	0	4	13	12	5
	CT	0	0	7	11	13	3

Regardless of motivation, interviewees acknowledged the tasks’ relevance (see Excerpts 1–4). At PE, they highlighted opportunities to develop oral (1) and critical thinking skills (2), suggesting frequent use and curriculum integration for real-world application (3) and academic development (4). This positive view persisted at PO and DP, emphasizing the value of interactive tasks for improving L2 communication.

Excerpts 1–4

(1) This type of task is quite good for developing speaking abilities. If I did this often, like every day, I would certainly improve. (Student 24)
(2) Here you have to justify your arguments and do it your own way, which offers you many possibilities to use your own brain and your own constructions. (Student 28)
(3) This could happen in real life, so they [the tasks] help me prepare for real situations. (Student 29)
(4) These tasks help us a lot for the end-of-school exams. (Student 23)

As shown in Excerpts 5–9, participants enjoyed task performance at PE due to the absence of pressure (5), task novelty (6), and perceived challenge (7). Mood influenced enjoyment, with better mood associated with greater enjoyment. Declines in motivation were attributed to repetition and monotony (8), which increased from PE to PO and DP. The least motivated students cited speaking anxiety and low self-confidence (9), although confidence improved over time as task understanding and mood increased.

Excerpts 5–9

(5) It was great not having the pressure to perform well so I just chatted with my friend. (Student 18)
(6) It was different from what we usually see in an English textbook. In class, we don’t speak much. We just have to use a model sentence and repeat it all the time. (Student 9)
(7) I enjoy finding other ways to express my thoughts and it feels good when I manage. (Student 1)
(8) I know that it is important to speak, but I didn’t like that it was always kind of the same. (Student 34)
(9) For me, speaking in English is traumatic. My English level is not good enough. (Student 38)

Not all students demonstrated high self-confidence at PE (see Excerpts 10–15), citing vocabulary gaps (10) and unfamiliarity with task structure (11). Even motivated learners believed that they could have performed better. The least motivated linked low confidence and poor outcomes to limited L2 proficiency. At PO, most participants reported stable confidence; changes were attributed to clearer expectations (12) and better preparation (13). At DP, two students described shifts: one gained confidence from a better grasp of suggestions (14), while the other expressed frustration over failing to incorporate new pragmatic features (15).

Excerpts 10–15

(10) My grammar was all right, but I sometimes couldn’t recall vocabulary, so I had to use other words that didn’t mean exactly what I wanted to express. (Student 41)
(11) I wasn’t prepared for this type of task, so I didn’t do well. It was hard to put my ideas together and say what I wanted. (Student 12)
(12) It was better than last time [at pretest] because I knew how the tasks worked, so I planned my ideas and could express everything I wanted. (Student 9)
(13) I was able to communicate but it wasn’t perfect. The good thing is that I didn’t repeat the same suggestions all the time. I’m happy that I learned. (Student 28)
(14) After these weeks, I realized that I’ve become better at some of these aspects. In the beginning, it all sounded the same. Now it’s more varied, more colorful. (Student 36)
(15) I just wasn’t able to say complex sentences the way I wanted. I couldn’t include what we learned in class, so I sounded like a primitive. (Student 5)

Nearly all participants reported willingness to communicate at PE, appreciating speaking opportunities despite initial nervousness. Those initially unwilling noted gradual increases as conversations progressed. While self-reported WTC remained constant from PE to PO and DP, perceptions of performance varied. Excerpts 16–19 illustrate that, after instruction, participants attributed differences in performance quality to tiredness (16), boredom (17), anxiety (18), and task topic changes (19).

Excerpts 16–19

(16) I felt less productive because I was tired. I didn’t sleep well. (Student 5)
(17) I didn’t want to do anything today. (Student 12)
(18) I already knew what to expect, and I was also calmer than I was before. I enjoyed it rather than overthinking it. (Student 23)
(19) This topic was better for me. I was trying harder. (Student 18)

Task Motivation and Pragmatic Performance

Mean turns-per-time scores indicated an increase in situated WTC over time for both tasks (see Table 5), an improvement confirmed by a linear mixed-effects model (β = 0.62, p = .008). No overall difference emerged between the ST and the CT (β = -0.01, p = .96), though a trend toward higher situated WTC in the ST was observed at DP (interaction β = 0.60, p = .065).

Table 5. Statistical Results of Situated WTC

Research phase	Task complexity	Mean	95% confidence interval of mean		SD
			Lower bound	Upper bound
PE	Simple	3.38	2.94	3.82	1.26
	Complex	3.42	2.97	3.89	1.31
PO	Simple	3.82	3.34	4.29	1.35
	Complex	3.64	3.25	4.03	1.11
DP	Simple	4.67	4.08	5.25	1.68
	Complex	4.08	3.62	4.54	1.32

Spearman rank-order correlations between situated WTC and pragmatic outcomes (see Table 6) revealed three key findings. First, situated WTC significantly correlated with the quantity of pragmatic moves in both tasks, showing medium-to-large negative coefficients at PE and PO, and smaller ones at DP. The implications of these negative values are discussed in the next section. Second, a medium-sized significant correlation between situated WTC and pragmatic complexity was found only in the CT at PE, based on analytical ratings. Third, no significant correlations were observed between situated WTC and pragmatic accuracy.

Table 6. Correlation Matrix for Situated WTC and Task Performance

Research phase	Task complexity	Number of suggestions	Holistic complexity	Holistic accuracy	Analytical complexity	Analytical accuracy
PE	Simple	-.509**	-.223	.112	.076	-.182
	Complex	-.693**	-.144	-.153	-.491**	-.049
PO	Simple	-.569**	-.271	-.239	-.218	-.167
	Complex	-.506**	-.083	.054	.249	.118
DP	Simple	-.349*	-.298	-.268	-.087	-.071
	Complex	-.382*	-.313	-.220	.083	-.227

Note. * = p < .05; ** = p < .01

Task motivation variables followed a different trajectory. Unlike situated WTC, no strong correlations were identified with the quantity of suggestions. However, pragmatic complexity and accuracy showed weak associations with overall task motivation at PE. Specifically, motivation correlated with holistic complexity in the CT (r = .368, p = .032), primarily through goal orientedness and self-reported WTC. Accuracy correlated with motivation in both the ST (r = -.353, p = .032) and the CT (r = .337, p = .041). In the ST, this correlation was mainly influenced by emotional state and goal orientedness (with negative values reflecting inaccuracy-based scoring), while in the CT, it was driven by self-reported WTC and goal orientedness (see Appendix C).

Discussion

Fluctuation of Task Motivation

The study revealed a nuanced interaction between task complexity and motivation, with fluctuations over time. As Dörnyei and Tseng (2009) suggest, motivation can vary within and across tasks depending on perceived success and engagement. Retrospective self-reports over eight weeks showed a gradual motivational decline for both task types, with no significant difference between them. This partly aligns with prior findings and underscores the challenge of sustaining motivation during prolonged engagement with cognitively demanding L2 tasks. It supports Mozgalina’s (2015) claim that complex tasks without adequate guidance can deplete motivation, and echoes Poupore’s (2013) view that motivation fluctuates with task demands and emotional states–a factor that is relevant for L2 pragmatic tasks, which require mastery of intricate social and linguistic norms (Taguchi & Kim, 2018).

Simple tasks received more favorable ratings, probably due to lower cognitive demands. This may have contributed to slightly more sustained motivation, despite signs of fatigue from repetition. One participant remarked that the tasks became “always kind of the same,” reflecting a loss of novelty. Combined with high processing demands, this may have further accelerated the decline in motivation. Still, learners valued the tasks for their relevance to real-world communication, suggesting that perceived usefulness mitigated demotivation. The direct link to pragmatic competence may have reinforced this appraisal.

Robinson’s (2001) Cognition Hypothesis, which posits that greater task complexity elicits deeper cognitive processing, contextualizes these findings. While complex tasks may initially attract interest, motivation may wane without sufficient support–particularly with speech acts like suggestions, which demand both cognitive and pragmatic resources (House & Kádár, 2021). Of the six motivation variables, only appraisal and result assessment shifted significantly over time, with the latter emerging at the delayed posttest. This suggests that learners continued to value the tasks but adjusted their performance expectations as task complexity and contextual factors (e.g., fatigue, topic familiarity) became more salient. Motivation for complex tasks was also enhanced by peer interaction–rare in many L2 classrooms–supporting Dörnyei’s (2005) emphasis on external stimuli, such as social engagement, in sustaining motivation.

Differences in motivation may also reflect how well tasks aligned with learners’ cognitive resources and expectations (Masrom et al., 2015). Higher appraisal ratings for simple tasks suggest a better match with current proficiency, while complex tasks may have induced overload, especially when required to process nuanced pragmatic and sociocultural elements–an effect possibly compounded by the lack of performance feedback. This misalignment appears to have contributed to the observed motivational decline.

Emotional state, a key component of task motivation, also played a significant role. Li (2024) highlights emotional engagement as critical for sustaining motivation, while this study suggests that excessive cognitive demands may undermine it. Balancing task complexity with learners’ emotional and cognitive capacities is therefore essential to prevent motivational burnout.

Interview data further support this interpretation. Participants reported that repetitive task formats led to monotony, with complex tasks mentioned more often. This is consistent with Roothooft et al. (2022), who found that unvaried repetition reduces engagement. Although learners valued the tasks’ pragmatic relevance, the absence of novelty and limited scaffolding–such as feedback–may have hindered long-term motivation.

Interaction between Task Motivation and Task Performance

The study also explored the relationship between motivation and pragmatic performance, focusing on two perspectives: overall correlations between motivation and performance, and the roles of self-reported motivation and situated WTC as observable indicators of engagement over time.

Self-reported motivation positively correlated with speech act quality–measured by pragmatic complexity and accuracy–but not with quantity. This suggests that motivated learners prioritized precision and depth over frequency in their L2 pragmatic output (see Kormos & Préfontaine, 2017; Mozgalina, 2015). The pattern was more evident in complex tasks, where cognitive demands intensified motivation’s effect. Robinson (2007) noted that such tasks, in particular those involving decision-making and justification, enhance language complexity and accuracy when learners are motivated. In complex tasks, however, goal-orientedness and self-reported WTC were key predictors (see Dörnyei & Kormos, 2000; Kormos & Dörnyei, 2004; Kormos & Préfontaine, 2017; Kormos & Wilby, 2019).

These variables likely had the strongest impact because they are closely related to task-specific engagement (see Dörnyei & Ushioda, 2021). Learners who perceived tasks as goal-relevant or experienced a strong willingness to engage approached them with performance-driven intent, focusing on pragmatic demands. In contrast, other motivational aspects, such as reported effort or emotional state, were weaker predictors, possibly because they are less stable and less cognitively focused (see Robinson, 2007, for learner factors affecting task complexity). Over time, correlations between motivation and performance declined, which may be attributed to increasing familiarity and reduced novelty. Cognitive habituation from repetition (see Sample & Michel, 2014) may have allowed learners to rely more on procedural knowledge than on motivation.

Situated WTC–measured through turn-taking–increased over time, with a trend toward higher levels in the simpler task at the delayed posttest. This suggests continued communicative engagement despite repeated exposure. Although task type did not significantly affect situated WTC overall, this finding challenges the assumption that WTC and motivation tend to align (Elahi Shirvan et al., 2019). While participants reported strong motivation, the behavioral indicator of WTC did not consistently correlate with performance.

This discrepancy reflects the distinction between holistic and analytical motivation measures. Self-reports captured general retrospective perceptions of interest and engagement, while situated WTC captured real-time interaction. Learners may have viewed themselves as motivated even when their actual behavior suggested otherwise. Pinner (2016) argues that self-reports are shaped by confidence and attitudes, whereas analytical measures capture immediate cognitive and social demands. Moreover, learners may have underreported WTC or overlooked moments of engagement.

The negative correlation between situated WTC and speech act quantity reinforces that high participation does not necessarily translate into more pragmatic contributions. Learners may have been behaviorally engaged and eager to speak but lacked the cognitive resources for complex or accurate pragmatic moves, with situated WTC reflecting floor-taking behavior more than interactional quality. This aligns with the idea that motivation often influences the extent of behavioral engagement rather than its qualitative aspects (Dörnyei & Kormos, 2000; Kormos & Dörnyei, 2004). Despite active participation, learners may have struggled to integrate newly acquired pragmatic knowledge.

Motivation appeared to shift over time in response to changing task demands and instructional support. The strong correlation between situated WTC and pragmatic complexity at the pretest, predominantly in the complex task, may reflect early curiosity and initial engagement. As cognitive demands became clearer, motivation declined, and pragmatic complexity dropped. An ecological view (Consoli, 2021) explains these fluctuations by treating learners as dynamic individuals shaped by emotional, cognitive, and contextual factors. This supports Takahashi’s (2023) argument that motivation in L2 pragmatics is highly context-sensitive.

Instruction on suggestion-making between the pretest and posttests significantly improved performance, with marked gains in pragmatic accuracy. Explicit teaching of pragmalinguistic and sociopragmatic features lowered cognitive load and supported focus on accuracy. While the main goal was performance, instruction likely boosted motivation indirectly by increasing task manageability and relevance. Defined aims, structured task-based work, and constructive feedback further enhanced engagement and confidence.

Task repetition had a dual effect: it consolidated learning and refined output but also led to motivational fatigue–most pronounced in complex tasks. Learners reported boredom and disengagement as task novelty faded. Robinson (2001) points out that complex tasks require sustained effort, and without variation or scaffolding, both performance and motivation may decline–a view echoed in learner interviews.

Implications

The findings provide valuable insights for improving pedagogy in task-based approaches to L2 pragmatics. Educators should adopt flexible, responsive strategies that account for the complex nature of motivation, adjusting their practice when initial methods prove ineffective. Tasks should reflect learners’ experiences and incorporate real-world relevance to sustain interest and prevent motivational decline (Guo et al., 2020). A well-sequenced progression of tasks that vary in format and complexity can balance cognitive demands, gradually introduce pragmatic challenges, and support both motivation and performance (Robinson, 2001).

From an ecological perspective, responding to learner-specific contexts and emotional states is essential (see Consoli, 2021; Pinner, 2016). Effective task design should include clear objectives, timely feedback–most crucial for more demanding tasks–and consistent opportunities for meaningful communication. Scaffolding benefits all learners, but it holds greater importance for those who experience anxiety or low self-confidence, as it promotes engagement and strengthens learning outcomes while maintaining focus on L2 pragmatic development.

Limitations and Suggestions for Further Research

Caution is warranted in generalizing the findings due to the small sample size, reliance on self-reported data, and differences in learners’ sociocultural settings and levels of engagement. Future research could adopt individual-centered approaches to more fully capture the dynamic, context-sensitive nature of motivation. The behavioral measures of output quantity and situated WTC also present limitations: the former may underestimate efficient pairs who reached agreement quickly (reflecting a ceiling effect), while the latter does not account for silent or non-verbal expressions of communicative intent. These constraints could be addressed through pragmatic benchmarking (e.g., obligatory occasion analysis) and real-time tracking tools to detect subtler forms of participation. Encouraging purposeful task engagement may further enhance data validity and pedagogical value, even if total adherence remains difficult to ensure.

Conclusion

This study examined how learners’ motivation for L2 pragmatic tasks changes over time and interacts with task complexity. Motivation declined as tasks progressed, with a sharper drop in complex tasks. However, learners maintained positive views of the tasks, emphasizing their authenticity and contribution to pragmatic skill development. Task motivation was associated with pragmatic complexity and accuracy, most notably under higher cognitive demands. These findings highlight the importance of thoughtful task design to sustain motivation, accounting for repetition, cognitive load, and interaction opportunities. The study underscores the dynamic nature of motivation and its impact on L2 pragmatic performance.

About the Author

Daniel Márquez is assistant professor of Spanish and English linguistics at Charles University in Prague, Czechia. His research focuses on second language pragmatics, task-based pedagogy, and affective factors–particularly learner motivation–in language learning environments. ORCID ID: 0000-0001-7245-1538

To Cite this Article

Márquez, D. (2025). The interaction of task motivation and task complexity in pragmatic performance. Teaching English as a Second Language Electronic Journal (TESL-EJ), 29(3). https://doi.org/10.55593/ej.29115a6

References

Agawa, T. (2020). A qualitative examination of relatedness needs in Japanese EFL classrooms and task motivation. Journal for the Psychology of Language Learning, 2(1), 6–25.

Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning, and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545–561. https://doi.org/10.1037/0022-0663.94.3.545

Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261–271. https://doi.org/10.1037/0022-0663.84.3.261

Anderman, E. M., Austin, C. C., & Johnson, D. M. (2002). The development of goal orientation. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation (pp. 197–220). Academic Press. https://doi.org/10.1016/B978-012750053-9/50010-3

Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (pp. 71–81). Academic Press.

Bandura, A. (2010). Self-efficacy. In I. B. Weiner & W. E. Craighead (Eds.), The Corsini encyclopedia of psychology (1–3). https://doi.org/10.1002/9780470479216.corpsy0836

Brown, P., & Levinson, S. (1987). Politeness: Some universals in language usage. Cambridge University Press.

Bulté, B., & Housen, A. (2012). Defining and operationalising L2 complexity. In A. Housen, F. Kuiken, & I. Vedder (Eds.), Dimensions of L2 performance and proficiency: Complexity, accuracy and fluency in SLA (pp. 21–46). John Benjamins. http://doi.org/10.1075/lllt.32.02bul

Consoli, S. (2021). Understanding motivation through ecological research: The case of exploratory practice. In R. Sampson & R. Pinner (Ed.), Complexity perspectives on researching language learner and teacher psychology (pp. 120–135). Multilingual Matters & Channel View Publications. https://doi.org/10.2307/jj.22730471.11

Council of Europe (2020). Common European framework of reference for languages: Learning, teaching, assessment – Companion volume. Council of Europe Publishing.

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.

Dörnyei, Z. (2002). The motivational basis of language learning tasks. In P. Robinson (Ed.), Individual differences in second language acquisition (pp. 137–158). John Benjamins. https://doi.org/10.1075/lllt.2.10dor

Dörnyei, Z. (2005). The psychology of the language learner: Individual differences in second language acquisition. Erlbaum.

Dörnyei, Z. (2014). Researching complex dynamic systems: ‘Retrodictive qualitative modelling’ in the language classroom. Language Teaching, 47(1), 80–91. https://doi.org/10.1017/S0261444811000516

Dörnyei, Z. (2019a). From integrative motivation to directed motivational currents: The evolution of the understanding of L2 motivation over three decades. In M. Lamb, K. Csizér, A. Henry, & S. Ryan (Eds.), The Palgrave handbook of motivation for language learning (pp. 39–70). Palgrave Macmillan.

Dörnyei, Z. (2019b). Task motivation: What makes an L2 task engaging? In Z. Wen & M. J. Ahmadian (Eds.), Researching L2 performance and pedagogy (pp. 53–66). John Benjamins. https://doi.org/10.1075/tblt.13.04dor

Dörnyei, Z. (2020) Innovations and challenges in language learning motivation. Routledge. https://doi.org/10.4324/9780429485893

Dörnyei, Z., & Csizér, K. (2012). How to design and analyze surveys in second language acquisition research. In A. Mackey & S. Gass (Eds.), Research methods in second language acquisition: A practical guide (pp. 74–94). Wiley-Blackwell. https://doi.org/10.1002/9781444347340.ch5

Dörnyei, Z., & Dewaele, J. (2023). Questionnaires in second language research: Construction, administration, and processing. Routledge.

Dörnyei, Z., & Kormos, J. (2000). The role of individual and social variables in oral task performance. Language Teaching Research, 4(3), 275–300. https://doi.org/10.1177/136216880000400305

Dörnyei, Z., & Ryan, S. (2015). The psychology of the language learner revisited. Routledge.

Dörnyei, Z., & Tseng, W.-T. (2009). Motivational processing in interactional tasks. In A. Mackey & C. Polio (Eds.), Multiple perspectives on interaction: Second language research in honor of Susan M. Gass (pp. 117–134). Lawrence Erlbaum.

Dörnyei, Z., & Ushioda, E. (2021). Teaching and researching motivation. Routledge. https://doi.org/10.4324/9781351006743

Eccles, J. S. (2005). Subjective task value and the Eccles et al. model of achievement-related choices. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). Guilford Press.

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153

Elahi Shirvan, M., Khajavy, G. H., MacIntyre, P. D., & Taherian, T. (2019). A meta-analysis of L2 willingness to communicate and its three high-evidence correlates. Journal of Psycholinguist Research, 48, 1241–1267. https://doi.org/10.1007/s10936-019-09656-9

Ellis, R. (2017). Task-based language teaching. In S. Loewen & M. Sato (Eds.), The Routledge handbook of instructed second language acquisition (pp. 108–125). Routledge. https://doi.org/10.4324/9781315676968

Ellis, R., Skehan, P., Li, S., Shintani, N., & Lambert, C. (2019). Task-based language teaching: Theory and practice. Cambridge University Press.

Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18. https://doi.org/10.1037/a0026092

Gilabert, R., & Barón, J. (2013). The impact of increasing task complexity on L2 pragmatic moves. In K. McDonough & A. Mackey (Eds.), Second language interaction in diverse contexts (pp. 45–70). John Benjamins. https://doi.org/10.1075/lllt.34.06ch3

Guo, Y., Xu, J., & Xu, X. (2020). An investigation into EFL learners’ motivational dynamics during a group communicative task: A classroom-based case study. System, 89, 102214. https://doi.org/10.1016/j.system.2020.102214

Han, Y., & McDonough, K. (2019). Motivation as individual differences and task conditions from a regulatory focus perspective: Their effects on L2 Korean speech performance. Innovation in Language Learning and Teaching, 15(1), 1–12. https://doi.org/10.1080/17501229.2019.1652614

Hidi, S. (2006). Interest: A unique motivational variable. Educational Research Review, 1(2), 69–82. https://doi.org/10.1016/j.edurev.2006.09.001

House, J., & Kádár, D. Z. (2021). Cross-cultural pragmatics. Cambridge University Press.

Jackson, D. O., & Suethanapornkul, S. (2013). The cognition hypothesis: A synthesis and meta-analysis of research on second language task complexity. Language Learning, 63(2), 330–367. https://doi.org/10.1111/lang.12008

Johnson, M. D. (2017). Cognitive task complexity and L2 written syntactic complexity, accuracy, lexical complexity, and fluency: A research synthesis and meta-analysis. Journal of Second Language Writing, 37, 13–38. https://doi.org/10.1016/j.jslw.2017.06.001

Jülkunen, K. (1989). Situation- and task-specific motivation in foreign-language learning and teaching. University of Joensuu.

Jülkunen, K. (2001). Situation- and task-specific motivation in foreign language learning. In Z. Dörnyei & R. Schmidt (Eds.), Motivation and second language acquisition (pp. 29–42). University of Hawaiʻi Press.

Kormos, J., & Dörnyei, Z. (2004). The interaction of linguistic and motivational variables in second language task performance. Zeitschrift für Interkulturellen Fremdsprachenunterricht, 9(2), 1–17.

Kormos, J., & Préfontaine, Y. (2017). Affective factors influencing fluent performance: French learners’ appraisals of second language speech tasks. Language Teaching Research, 21, 699–716. https://doi.org/10.1177/1362168816683562

Kormos, J., & Wilby, J. (2019). Task motivation. In M. Lamb, K. Csizér, A. Henry, & S. Ryan (Eds.), The Palgrave handbook of motivation for language learning (pp. 267–286). Palgrave Macmillan

Kormos, J., Brunfaut, T., & Michel, M. (2020). Motivational factors in computer-administered integrated skills tasks: A study of young learners. Language Assessment Quarterly, 17(1), 43–59. https://doi.org/10.1080/15434303.2019.1664551

Lambert, C., Aubrey, S., & Bui, G. (Eds.). (2023). The role of the learner in task-based language teaching: Theory and research methods. Routledge. https://doi.org/10.4324/9781003227267

Li, C. (2024). Task-specific emotions in L2 writing: A control-value theory approach from a positive psychology perspective. Studies in Second Language Learning and Teaching, 15(1), 73–103. https://doi.org/10.14746/ssllt.43371

Ma, J. H. (2009). Autonomy, competence, and relatedness in L2 learners’ task motivation: A self-determination theory perspective [Unpublished doctoral dissertation]. Graduate Division, University of Hawaiʻi at Mānoa.

Márquez, D., & Barón, J. (2021). Effects of task complexity on L2 suggestions: An exploratory study on trade-offs between accuracy and complexity. Journal on Task-Based Language Teaching and Learning, 1(2), 277–265. https://doi.org/10.1075/task.20007.mar

Márquez, D., & Michel, M. (2025). Assessment of task-based pragmatic performance: Measuring pragmatic complexity and accuracy. Manuscript submitted for publication.

Masrom, U. K., Nik Mohd Alwi, N. A., & Mat Daud, N. S. (2015). The role of task complexity and task motivation in language production. GEMA Online® Journal of Language Studies, 15(2), 33–49.

Michel, M. (2017). Complexity, accuracy and fluency in L2 production. In S. Loewen & M. Sato (Eds.), Routledge handbook of instructed second language acquisition (pp. 50–68). Routledge.

Mozgalina, A. (2015). More or less choice? The influence of choice on task motivation and task engagement. System, 49, 120–132. https://doi.org/10.1016/j.system.2015.01.004

Nakamura, J., & Csíkszentmihályi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). Oxford University Press.

Nakamura, J., & Csíkszentmihályi, M. (2014). The concept of flow. In M. Csíkszentmihályi (Ed.), Flow and the foundations of positive psychology (pp. 239–263). Springer. https://doi.org/10.1007/978-94-017-9088-8_16

Pawlak, M., & Mystkowska-Wiertelak, A. (2015). Investigating the dynamic nature of L2 willingness to communicate. System, 50, 1–9. https://doi.org/10.1016/j.system.2015.02.001

Pinner, R. (2016). Using self-assessment to maintain motivation in a dynamic classroom environment: An exploratory practice inquiry of one Japanese university speaking course. The Asian Journal of Applied Linguistics, 3(1), 27–40.

Plonsky, L., & Oswald, F. L. (2014). How big is “big”? Interpreting effect sizes in L2 research. Language Learning, 64(4), 878–912. https://doi.org/10.1111/lang.12079

Poupore, G. (2013). Task motivation in process: A complex systems perspective. The Canadian Modern Language Review, 69(1), 91–116. https://doi.org/10.3138/cmlr.1139

Révész, A., Michel, M., & Gilabert, R. (2016). Measuring cognitive task demands using dual-task methodology, subjective self-ratings, and expert judgments: A validation study. Studies in Second Language Acquisition, 38(4), 703–737. https://doi.org/10.1017/S0272263115000339

Robinson, P. (2001). Task complexity, cognitive resources and syllabus design: A triadic framework for examining task influences on SLA. In P. Robinson (Ed.), Cognition and second language instruction (pp. 287–318). Cambridge University Press. https://doi.org/10.1017/CBO9781139524780.012

Robinson, P. (2007). Criteria for classifying and sequencing pedagogic tasks. In M. P. García Mayo (Ed.), Investigating tasks in formal language learning (pp. 7–27). Multilingual Matters. https://doi.org/10.21832/9781853599286-004

Roothooft, H., Lázaro-Ibarrola, A., & Bulté, B. (2022). Task repetition and corrective feedback via models and direct corrections among young EFL writers: Draft quality and task motivation. Language Teaching Research, 0(0). https://doi.org/10.1177/13621688221082041

Safdari, S. (2021). Task motivation and transfer of learning across tasks: The case of learning the English definite article. Issues in Language Teaching, 10(2), 203–232. https://doi.org/10.22054/ilt.2022.63219.625

Sample, E., & Michel, M. (2014). An exploratory study into trade-off effects of complexity, accuracy, and fluency on young learners’ oral task repetition. TESL Canada Journal, 31(8), 23–46. https://doi.org/10.18806/tesl.v31i0.1185

Searle, J. (1969). Speech acts: An essay in the philosophy of language. Cambridge University. https://doi.org/10.1017/CBO9781139173438

Skehan, P. (1998). A cognitive approach to language learning. Oxford University Press.

Skehan, P. (2014). Limited attentional capacity, second language performance, and task-based pedagogy. In P. Skehan (Ed.), Processing perspectives on task performance (pp. 211–260). John Benjamins. https://doi.org/10.1075/tblt.5.08ske

Taguchi, N., & Kim, Y. (2018). Task-based approaches to teaching and assessing pragmatics: An overview. In N. Taguchi & Y. Kim (Eds.), Task-based approaches to teaching and assessing pragmatics (pp. 2–24). John Benjamins. https://doi.org/10.1075/tblt.10.01tag

Takahashi, S. (2023). Task motivation effects on L2 pragmatics. In A. Martínez-Flor, A. Sánchez-Hernández, & J. Barón (Eds.), L2 pragmatics in action: Teachers, learners and the teaching-learning interaction process (pp. 191–213). John Benjamins. https://doi.org/10.1075/lllt.58.08tak

Toprak, T. E. (2020). A study on the use of suggestion strategies among Turkish EFL learners. Turkish Online Journal of Qualitative Inquiry, 11, 36–55. https://doi.org/10.17569/tojqi.505686

Youn, S. J. (2015). Validity argument for assessing L2 pragmatics in interaction using mixed methods. Language Testing, 32(2), 199–225. https://doi.org/10.1177/0265532214557113

Zhang, L., & Aubrey, S. (2024). The role of individual differences in second language pragmatics: A systematic review. International Journal of Applied Linguistics, 1–19. https://doi.org/10.1111/ijal.12573

Appendix A. Operationalization of Task Complexity – Sample Research Task

Item	ST		CT
	Student 1	Student 2	Student 1	Student 2
Attendees:	all school teachers + one family member of each last-year student	one friend of each last-year student + all school teachers	a few teachers (for supervision) + two friends (only from school) who are in other years	a few parents (for supervision) + two friends (not from school) of each last-year student
Date:	Friday evening before state exams	Saturday afternoon before state exams	weekend before Christmas break	weekend before summer break
Music:	remixes made by the students	Spotify list set up by the students	ask our parents to pay for a DJ or to pay for a Czech pop band	collect funds to hire a rock and roll band or to hire the school band
Food and drinks:	tofu snacks (€150) + homemade lemonade (€100)	water (€50) + pizza (€150)	have an outdoor barbecue (€145) + one beer per person (€75) or one cocktail per person (€115)	have an indoor buffet (€195) + one glass of wine per person (€105) or one cider per person (€85)
Theme:	Mexican Day of the Dead	horror stories	superheroes	the eighties

[back]

Appendix B. Task Motivation Questions

This task was easy/difficult.
I did/didn’t do well on this task.
I wanted/didn’t want to speak in this conversation.
I was/wasn’t in the mood to do this task.
I liked/didn’t like working with a partner in this task.
I wanted/didn’t want to express my ideas during this task.
My English skills are/aren’t good for this type of task.
I felt/didn’t feel relaxed and comfortable doing this task.
I want/don’t want to do more tasks like this.
This task was/wasn’t interesting.
I did/didn’t do my best to complete this task.
I enjoyed/didn’t enjoy this task.
This task is/isn’t useful for learning English.

[back]

Appendix C. Correlation Matrix for Task Motivation Factors and Task Performance

	Research phase	Task complexity	Number of suggestions	Holistic complexity	Holistic accuracy	Analytical complexity	Analytical accuracy
Overall motivation	PE	Simple	.008	.008	.185	-.133	-.353*
		Complex	-.180	.368*	.337*	.222	-.097
	PO	Simple	.104	.088	.006	-.074	-.063
		Complex	.061	.128	.219	-.026	.041
	DP	Simple	.193	.158	.181	-.107	-.296
		Complex	-.237	.199	.136	.159	-.328
Task appraisal	PE	Simple	-.180	-.071	.157	-.078	-.287
		Complex	-.263	.191	.311	.230	-.081
	PO	Simple	.074	-.144	-.004	.065	-.144
		Complex	.036	-.037	.240	-.116	-.102
	DP	Simple	.121	.030	-.014	-.188	-.265
		Complex	-.291	.115	.088	.058	-.329
Reported effort	PE	Simple	-.127	.004	-.027	-.196	-.160
		Complex	-.109	.181	.227	.211	-.104
	PO	Simple	.033	.122	.068	.142	.127
		Complex	.074	.181	.030	-.060	.124
	DP	Simple	.049	.050	-.029	.086	-.225
		Complex	-.052	.092	.025	-.135	-.168
Emotional state	PE	Simple	.038	.009	.137	-.182	-.332*
		Complex	-.194	.191	.243	.173	-.102
	PO	Simple	.066	.128	-.051	-.136	-.046
		Complex	.057	.124	.233	-.020	.020
	DP	Simple	.234	.081	.155	-.215	-.236
		Complex	-.239	.169	.067	.115	-.310
Result assessment	PE	Simple	.200	-.103	.144	-.033	-.193
		Complex	-.080	.249	.228	.200	-.120
	PO	Simple	.227	.184	.229	-.193	.045
		Complex	.170	.289	.284	.036	.149
	DP	Simple	.054	.126	.124	.135	-.114
		Complex	.210	.043	.165	.090	-.309
Goal orientedness	PE	Simple	-.079	.059	.200	-.112	-.344*
		Complex	-.207	.335*	.357*	.259	.117
	PO	Simple	.134	.105	-.051	-.180	-.068
		Complex	-.008	.095	.230	.099	.053
	DP	Simple	.149	.164	.236	-.084	-.269
		Complex	.304	.225	.070	.105	-.218
Reported WTC	PE	Simple	.065	.125	.184	-.102	-.309
		Complex	-.054	.373*	.398*	.181	-.093
	PO	Simple	.055	.254	.060	-.087	-.057
		Complex	.063	.127	.056	.011	.177
	DP	Simple	.182	.180	.251	-.048	-.298
		Complex	-.278	.153	.135	.252	-.202

Note. * = p < .05

[back]

Copyright of articles rests with the authors. Please cite TESL-EJ appropriately. Editor’s Note: The HTML version contains no page numbers. Please use the PDF version of this article for citations.