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The aim of this qualitative case study was to explore the teachers' perspectives toward the causes of EFL (English as a foreign language) students' sentence errors. To collect data, the researchers utilized semistructured interviews with six EFL teachers in Banda Aceh, Indonesia, and the data were presented by including the excerpts of the participants' responses to the interview questions. The findings revealed that the students' sentence errors were made as a result of linguistic factors such as negative transfer, lack of vocabulary and lack of knowledge and non-linguistic factors, which are poor motivation, limited time to review the class material at home, excessive drill on simple sentence, and less attention to the teachers' explanations in the classroom. It is suggested that teachers should overcome all of these causes by using various methods to help learners avoid sentence errors in the future. [ABSTRACT FROM AUTHOR]

Individual distribution discrepancy poses significant challenges to cross-subject electroencephalography (EEG) signal decoding. Although transfer learning has emerged as an effective approach to minimize this distribution gap, EEG signals' low voltage amplitude makes them vulnerable to noise and results in abnormal, low-quality samples that cause negative transfer phenomenon. This phenomenon, in turn, undermines the efficacy of transfer learning and impedes brain-computer interface (BCI) applications. To overcome this challenge, we introduce the manifold embedded instance selection (MEIS) algorithm, which addresses negative transfer. The MEIS algorithm operates in two ways: converting raw EEG matrices into manifold embedded vectors that maintain sample discriminability, and designing an evaluator to assess the transferability of samples and filter out negative transfer samples from the source domain. When faced with a large number of source domains, our proposed method employs domain similarity estimation to determine the most beneficial subset of source domains for the target domain. The effectiveness of the proposed method is confirmed via offline and simulated online motor imagery-based BCI experiments where it demonstrates superior accuracy compared to other advanced techniques. Additionally, the MEIS algorithm significantly reduces both training time and the number of required training samples without affecting the model's performance. The code is available at https://github.com/ZilinL/MEIS. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Faculty of Letters / Edebiyat Fakultesi Dergisi is the property of Hacettepe University Faculty of Letters and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Life & School: a Magazine for the Theory and Practice of Education / Život i škola : časopis za teoriju i praksu odgoja i obrazovanja is the property of Zivot i Skola and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)