Yanti Anggraini
Universitas Dharma Indonesia

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Effectiveness of Deep Learning–Based Reading Platforms on EFL Reading Comprehension: A Quasi-Experimental Study Yanti Anggraini
Journal of English Language Teaching, Literatures, Applied Linguistic (JELTLAL) Vol. 3 No. 2 (2025): December
Publisher : Merwinspy Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69820/jeltlal.v3i2.441

Abstract

This study aimed to examine the effectiveness of a deep learning–based reading platform in improving EFL students’ reading comprehension achievement. A quantitative approach was employed using a quasi-experimental design, specifically the non-equivalent control group design. The sample consisted of two classes of university students (N = 60), divided into an experimental group and a control group. The experimental group used a deep learning–based reading platform equipped with adaptive reading features, difficult vocabulary analysis, AI-generated questions, and automated feedback, while the control group received conventional teaching. A 30-item reading comprehension test, validated and tested for reliability, served as the research instrument. Data were analyzed using paired sample t-tests, independent sample t-tests, and effect size calculation. The results revealed a significant improvement in the experimental group’s reading comprehension scores (p < 0.05). Furthermore, a significant difference was found between experimental and control groups in the post-test results, with an effect size of 1.30, indicating a large effect. These findings demonstrated that the deep learning–based reading platform was more effective than traditional instructional methods in enhancing EFL students’ reading comprehension.  
Metacognitive Scaffolding through AI-Assisted Digital Annotation: Developing Argument Evaluation and Inference-Making in EFL Academic Reading Yanti Anggraini
Reforma: Jurnal Pendidikan dan Pembelajaran Vol. 16 No. 1 (2026): Reforma: Jurnal Pendidikan dan Pembelajaran
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/reforma.v16i1.1369

Abstract

The integration of generative AI into academic reading contexts presents both a transformative scaffolding opportunity and a pedagogical risk: without deliberate instructional design, students may default to passive, query-and-paste AI use that reinforces surface-level text processing rather than analytical engagement. This Classroom Action Research (CAR) study investigated the effectiveness of AI-assisted digital annotation as a pedagogical intervention for improving undergraduate EFL students’ critical reading of academic texts. Guided by the Kemmis and McTaggart spiral model, the study was implemented across two iterative cycles with a class of 50 undergraduate students enrolled in Bahasa Inggris course at an Indonesian higher education institution. Data were collected through Critical Reading Performance Tests (pre-test and post-test per cycle) and classroom observation, and were analysed using a mixed-method approach integrating paired-samples t-tests with thematic analysis following Miles, Huberman, and Saldaña’s interactive model. Findings demonstrated statistically significant improvements in critical reading performance across both cycles, with a cumulative mean gain of 28.8 points from pre-test (M = 49.8) to the Cycle II post-test (M = 78.6; p < .001, Cohen’s d = 1.59), and 83.3% of students surpassing the predetermined success threshold. Qualitative findings documented a shift from passive paraphrastic annotation to interrogative critical dialogue with both text and AI tool. The study concluded that AI-assisted digital annotation, when embedded within a structured pedagogical framework and accompanied by explicit instruction in critical AI interrogation, constituted an effective and ecologically valid strategy for developing students’ critical reading competencies in EFL higher education contexts.