Construction of Intelligent Evaluation Model of English Composition Based on Machine Learning

Wang, Yuling

doi:10.1155/2022/3499799

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…With the growth of MOOC´s, extensive research has been conducted to develop automatic assessment systems that can grade not just objective-type questions (i.e. multiple choices), but also more subjective questions like English essays (Han and Huwan, 2022;Wang, 2022). (Wagstaff et al, 2019) presented a seamless, efficient, and accessible mobile solution to automate grading across multiple formats called Snaptron.…”

Section: Education Quality Experience and Satisfactionmentioning

confidence: 99%

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Pinto

Abreu

Costa³

et al. 2023

J INFORM SYSTEMS ENG

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In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.

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Section: Education Quality Experience and Satisfactionmentioning

confidence: 99%

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Pinto

Abreu

Costa³

et al. 2023

J INFORM SYSTEMS ENG

View full text Add to dashboard Cite

show abstract

“…As role of academic requirements, "English as Second Language", EFL students exhibit public speaking abilities. Speakers of English as ESL have higher challenges in public speaking than native speakers [13,14].…”

Section: Introductionmentioning

confidence: 99%

English Speaking Assessment Algorithm Based on Deep Learning

Xiaoli Hu

2024

jes

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English as a Foreign Language (EFL) students perform when speaking in public. An increasingly globalized world, effective public speaking is critical, but EFL students struggle to perform it, despite importance of qualities such as eye contact, speech pauses, there is presently no objective examination of such elements. A summative assessment has historically been the predominant form of evaluation in college English speaking assessments. Exam-centric teaching has considerable negative effect on foreign language training. In this research work, English Speaking Assessment Algorithm Based on Deep Learning (ESA-NEGCN-NBOA) is proposed. Initially, input video data are gathered from the multiple video dataset (MVD).The input video data is then pre-processed using Deep Attentional Guided Image Filtering (DAGIF) to remove presence of signal-dependent noise and improve lack of pixels from the regions and enhanced the video data. The data that has been pre-processed is utilized to Feature extraction using New General Double Integral Transform (NGDIT), which extract the significant attributes such as mel-frequency cepstral coefficients, energy, speech rate and pitch. Then NEGCN is proposed to improve students spoken English performance by assessmenting the English speakers. In general, NEGCN doesn’t express some adaption of optimization approaches for determining optimal parameters to promise exact improvement of assessment. Therefore, NBOA is proposed to enhance weight parameter of NEGCN for English speaking assessments, which precisely assess the English speaking. Performance measures such as accuracy, assessment error, evaluation time, pretest and posttest are examined when the proposed ESA-NEGCN-NBOA method is put into practice. The proposed ESA-NEGCN-NBOA method attains21.36%, 23.42% and 19.29% higher accuracy, 23.36%, 18.42% and 28.27% lower evaluation error, 20.36%, 27.42%, 28.17% lesser evaluation time analysed with existing techniques, likes innovative strategy towards oral English assessment utilizing machine learning, data mining, blockchain methods(IST-OEA-ML), machine learning assessment system for spoken English depend on linear predictive coding (AS-SE-LPC-ML), multimodal transfer learning for oral presentation assessment (MM-TL-OPA) respectively.

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Grammar Error Correction Method for English Composition Based on Spider Monkey Optimization with Multilayer Perceptron

Tan

2024

2024 Second International Conference on Data Science and Information System (ICDSIS)

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Construction of Intelligent Evaluation Model of English Composition Based on Machine Learning

Cited by 4 publications

References 19 publications

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

English Speaking Assessment Algorithm Based on Deep Learning

Grammar Error Correction Method for English Composition Based on Spider Monkey Optimization with Multilayer Perceptron

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