PREDICTING EFL LEARNERS' WRITING PROFICIENCY THROUGH READING AND WRITING PRACTICES: AN ARTIFICIAL NEURAL NETWORKS APPROACH

Özet

The current study explores the extent to which reading and writing practices predict the writing proficiency of 7th-grade EFL learners through Artificial Neural Networks. This research will fill the literature gap by embedding the Writing Process Theory and Cognitive Load Theory in order to investigate nonlinear interactions among reading comprehension, writing volume, and writing proficiency. The sample in this study consisted of 173 students, and predictive efficacy was assessed using both MLR and ANN models. The results indicate that reading comprehension and writing volume are strong predictors of writing proficiency, and ANN models perform better than traditional regression in capturing complex interdependencies. These findings have important pedagogical implications for EFL curriculum design and recommend systematic reading and writing interventions along with machine learning assessments and personalized instruction.

Keywords: writing proficiency, artificial neural networks, machine learning, EFL learners