english-language-learning
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- PaperDOAJ — Language assessment1 Jun 2026
Robust English Knowledge Tracing via Profile-Driven Forgetting and Masked Consistency
Xibo Chen, Ziqi Zhang, Haize Hu, Jie Jin et al.
A new framework for knowledge tracing in English learning uses a learning-profile-driven adaptive forgetting mechanism to model individual memory retention patterns and a masked consistency regularization method to reduce noise overfitting. Experiments show it outperforms state-of-the-art models in accuracy and robustness, supporting personalized language tutoring.
Original abstract
Knowledge Tracing (KT) plays a pivotal role in Intelligent Tutoring Systems (ITS) by dynamically assessing learners’ evolving knowledge states. However, tracking the acquisition of English presents unique challenges. Existing KT models typically employ homogeneous, predefined forgetting mechanisms that fail to capture the highly individualized nature of linguistic memory retention. Furthermore, language assessment data is notoriously noisy, which leads models to overfit superficial performance rather than capturing true underlying linguistic competence. To address these issues, we propose a novel framework to robustly trace English language competence. First, we introduce a Learning-Profile-Driven Adaptive Forgetting mechanism. Unlike methods with shared forgetting rates, our approach constructs a dynamic and strictly causal profile from historical interactions to generate personalized cognitive parameters (e.g., individualized forgetting rates). These parameters synchronously modulate the decay of multi-level knowledge states, enabling the model to accurately capture the heterogeneous memory retention patterns of different learners. Second, we design a Masked Consistency Regularization training paradigm. By applying stochastic masking to historical responses and enforcing predictive consistency, we prevent the model from exploiting localized noise and “shortcut” learning, compelling it to mine robust and invariant language representations. Extensive experiments on real-world educational datasets demonstrate that our proposed framework significantly outperforms state-of-the-art baselines in both prediction accuracy and noise resistance, offering a robust and interpretable solution for personalized language learning.
- PaperERIC — ELT & TESOL1 Jan 2025
Understanding Technology Use Intentions among Generation Z English Language Learners: A Correlational Study
Meral Yavuz Kartal, Dinçay Koksal
A correlational study of 310 Turkish preparatory class students found that digital literacy and positive attitudes toward technology are positive predictors of Generation Z English learners' intention to use technology, while technology anxiety is a negative predictor. Weekly laptop/desktop usage also positively predicts technology use intentions.
Original abstract
Using technology in foreign language learning can provide a variety of possibilities, significant opportunities, and significant benefits. Considering that Generation Z is a generation born into technology and grew up with it, it is critical to understand the intentions of Generation Z students to use technology in learning English and to find out what variables might affect these intentions. For these reasons, in this article, a correlational research design was used to understand the technology use intentions of schools of foreign languages' preparatory class students while English learning and to investigate whether variables such as technology use anxiety, technology access, attitudes towards technology use and digital literacy level are a predictor of these intentions. The participant group consists of 310 preparatory class students at the Foreign Languages School of Çanakkale Onsekiz Mart University. According to the study's results, it has been revealed that students' attitudes towards technology use are upbeat and high, their anxiety is low, their digital literacy levels are high, and their technology access is high. It is understood that digital literacy and the attitude of using technology in English language learning are positive predictors of technology use intentions. It is also understood that technology anxiety is a negative predictor. In addition, weekly laptop/desktop PC usage time while learning a language is also a positive predictor of technology use intentions.