Unpacking the Impact of Writing Task Complexity, Use of Digital Tools, and Engagement Strategies on University Students’ Academic Writing Performance
, Bambang Yudi Cahyono, Mala Rovikasari, Dyah Fitri MulatiThis study aims to investigate how writing task complexity, digital tools, and writing engagement strategies impact students’ academic writing performance, identifying their direct and mediated effects.
There is little exploration regarding the interplay of technological, cognitive, and affective-related factors, such as the use of digital tools, the complexity of writing tasks, and engagement strategies contributing to students’ academic writing performance, which has prominently emerged in higher education. Such exploration is essential to provide clear nuance and the importance of technological, cognitive, and affective roles in academic writing practices.
Employing Partial Least Squares-Structural Equation Modeling (PLS-SEM), this quantitative study used a validated 20-item questionnaire adapted from past research. Data were collected from 211 undergraduate students majoring in English Education and English literature from fourteen Indonesian universities.
This research systematically explores how digital tools and engagement strategies mediate the relationship between writing task complexity and academic writing performance. Distinct from previous research on writing task complexity, use of digital tools, and engagement strategies, which were studied in isolation, the findings of this study offer nuanced insights into how the use of digital tools and engagement strategies simultaneously mediate the effect of task complexity into potential interconnected pathways and mechanisms influencing academic writing outcomes. These insights highlight that students do not merely respond to complex tasks cognitively but also use digital support systems and perform engagement strategies to optimize their writing. These findings inform the design of integrated writing instruction that aligns task difficulty with digital scaffolding and engagement strategies, particularly in EFL higher education settings.
The results revealed significant positive effects of writing task complexity directly affecting academic writing performance (β=0.363), writing engagement strategies (β=0.560), and use of digital tools (β=0.694). Furthermore, use of digital tools significantly influenced writing performance (β=0.222) and engagement strategies (β=0.278), while engagement strategies notably affected academic writing performance (β=0.294). These findings underscore that appropriately structured complex tasks, enhanced by digital support and active engagement strategies, significantly bolster students’ academic writing capabilities.
Educational practitioners can leverage these insights to design complex yet manageable writing tasks and integrate digital tools effectively, thereby fostering improved student engagement and enhanced academic writing outcomes.
Indonesian ELT scholars may conduct future research to investigate additional elements such as individual differences in digital literacy, motivation, and language competency to better understand the complex relationships between writing task complexity, use of digital tools, engagement strategies, and academic writing performance. Researchers may also investigate the efficacy of specific digital tools or technologies in various academic and cultural contexts to determine optimal integration strategies.
This study clarifies the roles of task complexity, technology, and engagement in providing educators with evidence-based approaches to improve academic writing instruction, thereby preparing students more effectively for academic and professional contexts.
This paper has figured out the collective impact of writing task complexity, use of digital tools, and writing engagement strategies on academic writing performance using the PLS-SEM approach as a framework for analysis. Future studies are called to examine different approaches regarding these research findings, such as experimental design, which can manipulate task complexity to provide deeper insights into optimal task design.
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