Research on English Hybrid Assisted Teaching System Using Contextual Support of R-CNN

Abstract

The developing countries and developed countries are depending on major as well as single language, i.e., English. The software tool level English teaching assistance models are available but those are not that user-friendly and also unable to support current technology. The earlier English teaching assistance techniques like RFO (Random Forest Optimization) machine learning, Xboosting machine learning, and SVM (Support Vector Machine) cannot support background provision. English teachers must find a way to coordinate the relationships between students, teachers, the learning environment, and learning strategies. A modern user-friendly building technique and an advanced ecobalancing of education are suggested and executed, which may increase the hybrid education characteristics and request skills of English. Therefore, an advanced ANN-(artificial neural network-) based hybrid English teaching assistance model is inevitability necessary. In this research work, R-CNN-based ANN model is imported to make applications for hybrid English teaching assistants. The performance measures like accuracy 97.89%, sensitivity 98.34%, recall 94.83%, and throughput 92.89% had attained. The implemented design is competing with present technology and outstrips the methodology. The process is outstripped by the realized design, which competes with current technology.

The effects of instructional scaffolding for writing skill of English majored students

“…When applying scaffolding to the field of education, instructional scaffolding must ensure the following characteristics [3; 13]: learning support activities must have continuity [15]. When teachers apply learning support activities, it is necessary to set up a timetable as well as clearly explain the sequence of activities, and various factors in blending learning environments for learners to get used to and must maintain this activity regularly throughout the teaching process, like incorporating dialogue and digital tools in educational settings [13; 24]; learning support activities must be done in the context, the situation (contextual support like blending learning environments) [32], in accordance with Vygotsky's principle (Zone of Proximal Development [14; 39]. According to Vygotsky, Actual Developmental Level is an individual's current level of development, as measured by an individual's ability to solve problems alone or with support from others.…”

Hybrid learning in EFL classes: attitudes, reasons, and implementation of the Indonesian millennial professors and Zen students

“…Hybrid learning (HL) was practiced long before the Coronavirus Disease 2019 (COVID-19) pandemic spread in many departments and courses. Recent studies of HL have reported variant evidence, variables of HL, and subjects (Anabel & Simanjuntak, 2022;Criollo-C et al, 2022;Dodiya, 2021;Doghonadze et al, 2021;Sunardi & Lutfi, 2022;Utami et al, 2020;Xiaodong, 2022;Yuan, 2022). However, the studies left limitations and gaps.…”

“…Xiaodong (2022) reported an experimental study that focused on a "hybrid online and offline English-speaking teaching method based on modern educational technology" in China (p. 1). Yuan (2022) reported an experiment study of a Region-based convolutional neural network (R-CNN)-based artificial neural network (ANN) that focuses on improving the performance of English hybrid-assisted teaching and comparing the performance of the proposed system with known nearest neighbor (KNN), random forest optimization (RFO), and Xboosting in China. Sunardi and Lutfi (2022) also reported their survey study that focuses on 203 students' experience in learning English with HL and their perception of how HL influences their learning of English in Indonesia.…”

“…It is still unexplored how MEPs and EZSs attitudes to HL after COVID-19 ended (participant and variable gaps), what reason they need the HL in the new regular (variable gaps), and what the scenarios of HL in EFL during the new normal (finding gaps). The synthesized evidence shows none of the current study, i.e., students' preference (Anabel & Simanjuntak, 2022), teacher's creativity (Arifani et al, 2019), tertiary level education (Çepik et al, 2016), speaking improvement (Criollo-C et al, 2022), learning efficiency (Doghonadze et al, 2021), EFL learner's perception (Bukhari & Basaffar, 2019;Simbolon 2021), mobility (Ghareb & Mohammed, 2019), community inquiry network (Gozali et al, 2022), constructivist approach (Haddad, 2013), character building (Handayani & Utami, 2020), blended learning application (Huong, 2019), ESL Instructor's reflection (Ibrahim & Ismail, 2021), students' role change (Javed et al,2018), assessment (Kamsin & Din, 2015), second language acquisition (Klimova & Pikhart, 2021), academic stress (Mucshini & Siswandari, 2020), learning solution (Nashir & Laili, 2021), productive language skills (Nayman & Bavlı, 2022), English language teacher (Mendes et al, 2020), computer software (Xiang, 2021), teaching speaking (Xiaodong, 2022), contextual Support (Yuan, 2022), and e-learning effectiveness (Zakarneh, 2018).…”

Retracted: Research on English Hybrid Assisted Teaching System Using Contextual Support of R-CNN

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”