Research and Design of Automatic Scoring Algorithm for English Composition Based on Machine Learning

With the development of artificial intelligence and big data, the concept of “Internet plus education” has gradually become popular, including automatic scoring system based on machine learning. Countries all over the world vigorously promote the deep integration of information technology and discipline teaching in various fields. English is a medium of communication in the current era of education information development trend. English composition automatic scoring mode is gradually accepted by the majority of educators and applied in the actual classroom teaching. However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms. Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm on English writing teaching, and proves the correctness of the design idea and algorithm of this paper through a lot of experiments.

Machine Learning-Based Grammar Error Detection Method in English Composition

The detection of grammatical errors in English composition is an important task in the field of NLP. The main purpose of this task is to check out grammatical errors in English sentences and correct them. Grammatical error detection and correction are important applications in the automatic proofreading of English texts and in the field of English learning aids. With the increasing influence of English on a global scale, a huge breakthrough has been made in the task of detecting English grammatical errors. Based on machine learning, this paper designs a new method for detecting grammatical errors in English composition. First, this paper implements a grammatical error detection model based on Seq2Seq. Second, this paper implements a grammatical error detection and correction scheme based on the Transformer model. The Transformer model performs better than most grammar models. Third, this paper realizes the application of the BERT model in grammar error detection and error correction tasks, and the generalization ability of the model has been significantly enhanced. This solves the problem that the forward and backward cannot be merged when the Transformer trains the language model. Fourth, this paper proposes a method of grammatical error detection and correction in English composition based on a hybrid model. According to specific application scenarios, the corresponding neural network model is used for grammatical error correction. Combine the Seq2Seq structure to encode the input sequence and automate feature engineering. Through the combination of traditional model and deep model, the advantages are complemented to realize grammatical error detection and automatic correction.

All Power To All the People: WGS and Feminist Pedagogy in the Era of the Alt-Right

Academic Feminists have long used anti-oppression composition writing as a emancipatory pedagogical tool, but the impact of neoliberalism on higher education and the institutionalization of WGS have impacted how faculty teach these courses. As well the successful implementation of a gender course requirement has changed the demographics of these classes. This essay looks at the pedagogical parallels between teaching the gender studies course and the English composition course. An additional focus is how institutional shifts in WGS and its curriculum impact pre-tenure faculty's ability to offer emancipatory writing and teaching.

A Study of the Application of Computer Translation Technology in the Process of English Teaching

Objectives: With the development of Internet and computer technology, auxiliary teaching has been widely used in English teaching. Methods: Based on this, this paper studied English teaching based on the computer corpus. First, we designed and built the corpus based on the outline of the current English Teaching in China. Results: Secondly, the diagnosis algorithm was designed according to the needs of auxiliary teaching. And the implementation steps were expounded. Finally, we applied the algorithm designed in this paper to the evaluation of English composition. Conclusion: Through the test of the algorithm, the difference range between the test data and the actual data was acceptable. The algorithm designed in this paper was effective, and it could be further promoted in English teaching.

Error Analysis Of Indonesian Grammatical Interference In Students’ English Composition

The first language (L1) of learners has a significant effect in learning a foreign language. The influence of L1 could become errors, that is called interference. The main purposes of the study are to identify the most dominant type of language interference and the factors contribute to language interference in students’ English composition. The data were collected from 67 Second Grade Students of Plantation Management Study Program in State Polytechnic of Agriculture, Samarinda for the Academic Year 2019/2020, and taken through elicitation technique and documentation, and then analyzed the errors. The result of the study shows that the type of grammatical interference comprise morphological errors and syntactical errors were the most troublesome areas of language that students encountered when conducting composition in English. Since the main focus of the study is L1 interference, only errors related to Indonesian as L1 interference were addressed.

Case studies in the classroom: assessing a pilot information literacy curriculum for English composition

PurposeThis mixed-methods study assesses a pilot library curriculum in a general education English composition course. Case-based learning (CBL), a form of problem-based learning (PBL), was used to scaffold information literacy skills and concepts across sessions. This article explores the approach's impact on student learning and engagement.Design/methodology/approachParticipants were enrolled in four sections of an undergraduate composition course. Two sections were taught with the CBL library curriculum, and two with the standard library curriculum as a control. Pretest/posttest surveys included quantitative and qualitative measures to assess students in several areas of information literacy. Weekly reflections from a subsample of students were analyzed, and the research team conducted structured classroom observations and teaching reflections.FindingsQuantitative survey results did not support the hypotheses that the CBL curriculum would increase students' confidence and skill levels compared to their control section peers. Although there was no significant difference between sections in measured information literacy outcomes, students generally agreed that the case studies used in the CBL curriculum taught skills applicable to their research. Teaching observation data revealed the cohesion of the curriculum across library sessions and increased student engagement in classroom activities. However, some of the case studies could be improved, and some limitations in study design point to the need for further research.Originality/valueThis study addresses a gap in the literature through a mixed-methods assessment of CBL pedagogy using a control group, contributing to an understanding of the role of PBL pedagogies in information literacy curricula.