An Evaluation of IELTS Speaking Test

Li, Jingyi

doi:10.4236/oalib.1105935

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…The results indicated that specific aspects of the test, including fluency and vocabulary, had a persistent effect on the total scores provided by the evaluators. Therefore, the findings corroborated the findings of previous studies [24,42,52,69] that established the IELTS speaking test's reliability. According to statistics presented in the studies, most IELTS speaking tests are accurate and reliable.…”

Section: Discussion and Recommendationsupporting

confidence: 90%

“…Speaking tests cover various language learning areas, including vocab, proper grammatical usage, fluency, correctness, interaction, the social side of speaking, and task fulfillment [41,42]. Additionally, assessing speaking is complicated because of its dynamic character, spontaneity, and appropriateness [11,[43][44][45].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Likewise, Demirel and Baser [50] discovered that assessing the reliability of speaking skills is not an easy process because it is influenced by various factors, including the test's construct, task, and understanding of the learners' background. Numerous studies have been conducted to verify the IELTS speaking test's reliability [24,42,52,69]. According to research, most of the IELTS speaking tests are accurate and consistent.…”

Section: Reliabilitymentioning

confidence: 99%

See 2 more Smart Citations

The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment

Khan

Kumar

Benyo

et al. 2022

Education Research International

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Speaking ability is regarded as among the essential aspects of language development. Oral examination appeared challenging to evaluate due to the presence of human evaluators. The speaking method depends on the test’s reliability, determined by the raters’ scores. The current study is aimed at evaluating the speaking test’s interrater reliability utilized to measure the speaking performance of Common First Year (CFY) students during remote learning. The data were obtained from 56 EFL learners using a scoring sheet and rubrics. Eight raters were responsible for rating the study. The speaking test’s reliability was estimated using quantitative data analysis. Correlation coefficients and the Bland-Altman test were employed to assess raters’ agreement. SPSS was utilized to analyze the data in this investigation. The study’s findings suggested that the speaking exam used in the CFY program during remote learning has shown some reliability on correlations and acceptable norms on the Bland-Altman test.

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Section: Discussion and Recommendationsupporting

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Section: Reliabilitymentioning

confidence: 99%

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The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment

Khan

Kumar

Benyo

et al. 2022

Education Research International

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“…Two parallel speaking tests of IELTS 10 in the form of two oral interviews were used in this study to evaluate the target points in the participants' speaking ability, that is, the lexical and syntactic complexity of their speech. As a standard test, the validity and reliability of the IELTS speaking module have been confirmed in previous studies (e.g., Li, 2019;Quaid, 2018). An attempt was made to choose two parallel forms of the IELTS speaking module based on available practice tests.…”

Section: Instrumentsmentioning

confidence: 62%

The effect of using the native language as a pedagogic intervention on Iranian EFL learners' complexity of English oral productions

Alimorad

Bidoki

2021

PortaLin

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The present study aims at investigating the effect of using the native language as a pedagogic intervention on the complexity of Iranian EFL learners' English oral productions. A sample of 39 male and female adult English learners of B1 and B2 CEFR proficiency levels was recruited to participate in this study. They were placed into two intact classes (i.e., as already determined by the institution’s authorities) and each class was randomly chosen to serve as either the experimental (EG) or the control (CG) group. Improving the learners’ speaking ability was the focus of both groups while only the EG was asked to orally produce the equivalents of Persian sentences presented to them. In order to measure the lexical and grammatical complexity of oral productions of the learners, two parallel speaking tests of IELTS 10, in the form of two oral interviews, were used as pre- and post-test oral interviews. A MANCOVA test was run to compare the performance of the two groups in terms of their lexical and grammatical complexity after the treatment. Results indicated that the EG's lexical and grammatical complexity improved as compared to the CG, and the improvement in both of these variables was statistically significant.

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“…Currently, researchers are beginning to experiment with computer-assisted pronunciation training systems to address these problems [12][13][14]. e core issue of computeraided pronunciation training systems is pronunciation bias testing, i.e., pronunciation bias assessment.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Assessment System for Spoken English Based on Linear Predictive Coding

Wang

2022

Mobile Information Systems

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In the teaching of English, there is an increasing focus on practical communication skills. As a result, the speaking test component has received more and more attention from education experts. With the rapid development of modern computer technology and network technology, the use of computers to assess the quality of spoken English has become a hot topic of research in related fields at present. A machine learning assessment system based on linear predictive coding is proposed in order to achieve automatic scoring of spoken English tests. First, the principle of linear predictive coding and decoding is analyzed, and the traditional linear predictive coding and decoding algorithm is improved by using hybrid excitation instead of the traditional binary excitation. Second, the overall structure of the machine learning assessment system is designed, which mainly includes division into four modules: acoustic model acquisition module, speech recognition module, standard pronunciation transcription module, and decision module. Then, the speech recognition module is implemented by an improved linear predictive speech coding method to acquire the feature parameters of the speech signal and generate the speech feature vector. Finally, the convolutional neural network algorithm is used to train the speech features so as to implement the acoustic model acquisition module. The experimental results show that the improved linear predictive speech coding method yields more natural and higher intelligibility speech signals. The designed machine learning evaluation system is able to accurately detect information about the quality of the learner’s pronunciation.

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An Evaluation of IELTS Speaking Test

Cited by 5 publications

References 9 publications

The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment

The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment

The effect of using the native language as a pedagogic intervention on Iranian EFL learners' complexity of English oral productions

A Machine Learning Assessment System for Spoken English Based on Linear Predictive Coding

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