Students’ feedback beliefs and anxiety in online foreign language oral tasks

Martin, Sidney; Valdivia, Ibis Marlene Álvarez

doi:10.1186/s41239-017-0056-z

Cited by 56 publications

(44 citation statements)

References 38 publications

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“…Similar to the findings about the necessity of feedback, learners of all groups, regardless of their level of proficiency or foreign language anxiety report that frequent error correction helps to improve their oral communication skills. These findings are also in line with the studies (e.g., Martin & Alvarez Valdivia, 2017;Lee, 2013) conducted in different EFL settings.…”

Section: Discussion Conclusion and Implicationssupporting

confidence: 92%

“…İleri seviye gruplarının çoğunun başlayacakları bölümler İngilizce öğretmenliği, dilbilim, Batı dilleri ve edebiyatı, Mütercim tercümanlık gibi meslek olarak dil ile ilgili çalışacakları bölümler olduğu için, bu öğreniciler geribildirimin öneminin farkındadırlar. Bu bulgu da alandaki diğer bulgularla (örn., Martin & Alvarez Valdivia, 2017;Lee, 2013) örtüşmektedir.…”

Section: Bulgular Tartışma Ve Sonuçunclassified

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Investigating Foreign Language Anxiety and Learner Beliefs in Spoken Corrective Feedback With a Focus on Language Proficiency

Geçkin

2020

Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi

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Research on the role of language proficiency on foreign language anxiety and learner corrective feedback beliefs in oral practice has not reached conclusive results. This study investigates whether level of proficiency affects foreign language anxiety and learner beliefs about spoken corrective feedback in an EFL context. Participants were recruited through convenience sampling. A total of 204 participants, who were all tertiary level university learners of English from four different proficiency groups, responded to two scales: Foreign Language Classroom Anxiety Scale (Horwitz, Horwitz & Cope, 1986) and Corrective Feedback Belief Scale (Fukuda, 2004). A series of one-way ANOVA analyses and Bonferroni adjusted post-hoc corrections were run to test significance between and within groups. The four proficiency groups were statistically different from each other in terms of foreign language anxiety and spoken corrective feedback preferences. The findings revealed that the learners with a low proficiency level appreciated immediate feedback, but those with a higher level of language proficiency preferred delayed feedback. Clarification requests, elicitation and metalinguistic feedback were rated more positively by the beginner group than the other groups. In terms of the choice of correctors, language proficiency did not play a role; however, all the groups valued teachers as the main agents of correction. This study is one of the very few comprehensive studies (e.g., Tercan & Dikilitaş, 2016;Uyanıker, 2018) focusing on the role of language proficiency in anxiety and feedback preferences of high achiever tertiary level learners of English who will pursue their undergraduate studies in an English medium public university in Turkey.

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Section: Discussion Conclusion and Implicationssupporting

confidence: 92%

Section: Bulgular Tartışma Ve Sonuçunclassified

Investigating Foreign Language Anxiety and Learner Beliefs in Spoken Corrective Feedback With a Focus on Language Proficiency

Geçkin

2020

Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi

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“…However, there are some papers in which DM is used for each isolated application, for instance learner motivation (13% of papers), learning styles (8%), provide feedback for instructors (9%), detecting language anxiety (6%), predicting performance (14%), L2 orientations (8%), language reading comprehension (5%), and detecting grammar issues and assessment (7%). In this analysis, the DM applications most frequently used in the context of FLL are: Predicting performance (Linck et al, 2013;Seker, 2016;Swanson et al, 2016;Wang & Cheng, 2016;Whitehill & Movellan, 2018), learner motivation (Apple, Falout, & Hill, 2013;Li & Zhou, 2017;Saeed et al, 2014;Tajeddin & Moghadam, 2012), provide feedback for instructors (Coskun & Mutlu, 2017;Jiang & Lee, 2017;Kaoropthai, Natakuatoong, & Cooharojananone, 2016;Kieffer & Lesaux, 2012;Rodriguez & Shepard, 2013;Zhao et al, 2015), learning styles (Aslan et al, 2014;Farrington et al, 2015;Hamedi, Pishghadam, & Ghazanfari, 2016;Hsiao, Lan, Kao, & Li, 2017), detecting language anxiety (Baghaei & Ravand, 2015;Cakir & Solak, 2014;Guntzviller et al, 2016;Martin & Valdivia, 2017), and L2 orientations (Allen et al, 2014;Lou & Noels, 2017;Maqsood et al, 2016;Winke, 2013). Figures 8 and 9 show the correlation between the educational level in where the articles mentioned to have developed their proposal and the EDM methods and applications that has been used, respectively.…”

Section: Edm Methods Referencesmentioning

confidence: 99%

Data mining in foreign language learning

Bravo‐Agapito

Bonilla

Seoane

2018

WIREs Data Min & Knowl

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Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m‐learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential. This article is categorized under: Application Areas > Education and Learning Fundamental Concepts of Data and Knowledge > Data Concepts

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“…Many foreign studies (Clark & Mayer, 2016;Gaebel et al, 2014;Kurucay & Inan, 2016;Martin & Alvarez Valdivia, 2017) consider the use of e-learning in the study of foreign languages, study the quality of knowledge acquisition when conducting an online course, and also demonstrate interest in the problem of using e-learning in higher education. For example, the work (Clark & Mayer, 2016) analyzes pedagogical approaches and a variety of educational technologies used in the implementation of elearning, adaptive technologies in particular, which are based on a person-centered approach.…”

Section: Research Questionsmentioning

confidence: 99%