Deep Learning: a Strategy to Reduce Students' Procrastination in Enhancing Integrated Reading-writing Skills
Year: 2026
Type: article
Abstract: Deep learning (DL), the integrated language skills approach (ILSA), and student procrastination in higher education institutions (HEIs) have become critical topics in academic discussions, explored through conferences, journal articles, and other scholarly publications. This chapter examines the conceptual and practical connections between ILSA, DL, and language teaching methods (LTM), while also analyzing the latest deep-learning models, their benefits, and the challenges they present in enhancing integrated reading-writing (IRW) skills to mitigate student procrastination. A recent case study at a private HEI demonstrated that DL can be an effective strategy for reducing procrastination. The study involved 36 students engaged in an academic IRW experience that integrated DL-based strategies. The results showed that DL significantly decreased class-wide procrastination while improving students’ IRW skills. Notably, 25 out of 36 students exhibited markedly improve their final scores. These findings contribute both theoretically and practically to the field, offering novel insights for future research. Further studies are encouraged to investigate scalable methods for minimizing student procrastination in IRW development. Addressing procrastination is crucial, as it not only affects individual student performance but also impedes the broader academic and economic progress of their respective countries. Keywords: deep learning; integrated language skills approach (ILSA); integrated reading-writing (IRW); language teaching methods (LTM); procrastination
Author: Hermayawati
Institution: Muhammadiyah University of Yogyakarta
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7155685144
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