Integrating computational thinking into problem-based learning in EAP writing: Effects on motivation and writing performance

Abstract

In English for Academic Purposes (EAP) contexts, learners frequently struggle to sustain motivation and produce well-organized expository texts because of cognitive overload and inadequate structural scaffolding. Although Problem-Based Learning (PBL) fosters autonomy and collaboration, it often fails to provide sufficient cognitive and organizational support for complex academic writing tasks. This study examined whether integrating Computational Thinking (CT) principles within a PBL framework could enhance EAP learners' motivation and expository writing performance. A quasi-experimental design was conducted with 58 third-year university students in Indonesia enrolled in an academic writing course. Participants were assigned to a control group receiving conventional PBL instruction and an experimental group receiving CT-enhanced PBL instruction. Motivation was assessed using a modified version of the Motivated Strategies for Learning Questionnaire (MSLQ) and analyzed through ANCOVA. Writing performance was evaluated across four expository tasks using Jacobs’ (1981) ESL Composition Profile and analyzed through linear mixed-effects modeling. Results indicated that the CT-enhanced PBL group demonstrated significantly higher motivation and superior writing performance, particularly in organization and language use. The findings highlight the pedagogical potential of embedding CT within PBL as a means to support sustained motivation, structured reasoning, and academic literacy in EAP writing instruction.